WGAN_GP_200epochs.ipynb

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  "metadata": {
    "colab": {
      "name": "WGAN-GP trial2 200epochs",
      "provenance": [],
      "collapsed_sections": [
        "OVR6cXD2-3Bv"
      ]
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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      "codemirror_mode": {
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gJ5q4Qv1sPQY"
      },
      "source": [
        "# WGAN-GP for EEG signals"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXs82mGNsPQZ"
      },
      "source": [
        "## Wasserstein GAN (WGAN) with Gradient Penalty (GP)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iiBx9yJisPQZ"
      },
      "source": [
        "## Imports\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I65VK7znsPQZ"
      },
      "source": [
        "import numpy as np\n",
        "import pickle as pickle\n",
        "import pandas as pd\n",
        "from scipy.stats import skew,kurtosis\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Model,layers\n",
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import KFold,RepeatedKFold\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
        "from sklearn.metrics import classification_report,confusion_matrix,roc_auc_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import cross_validate,cross_val_score\n",
        "\n",
        "\n",
        "import sys\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "sys.path.append(\"..\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7EC6eZ90pByQ",
        "outputId": "145f5896-112a-4d0e-abe4-4b499c108421"
      },
      "source": [
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9KSjVopnpLe",
        "outputId": "3a83a8f9-4fd3-47e0-a099-326e14f45574"
      },
      "source": [
        "# A = np.load('/content/drive/MyDrive/Thesis data/A.npy') # File with all(40) channel data\n",
        "# B = np.load('/content/drive/MyDrive/Thesis data/B.npy') #File with only data from left hemisphere \n",
        "A_norm = np.load('/content/drive/MyDrive/Thesis data/traindata.npy') # normalised 40 channel data of only 22 parts\n",
        "B_norm = np.load('/content/drive/MyDrive/Thesis data/Anorm.npy') # normalised 7 channel data\n",
        "A_norm = A_norm.reshape(-1,40,99)\n",
        "print(\"trainingdata.shape =\",A_norm.shape)\n",
        "# print(\"B.shape =\",B_norm.shape)\n",
        "#Load the labels now    \n",
        "valence = np.genfromtxt('/content/drive/MyDrive/Thesis data/label_class_0.dat', delimiter=' ')\n",
        "arousal = np.genfromtxt('/content/drive/MyDrive/Thesis data/label_class_1.dat', delimiter=' ')\n",
        "dominance = np.genfromtxt('/content/drive/MyDrive/Thesis data/label_class_2.dat', delimiter=' ')\n",
        "liking = np.genfromtxt('/content/drive/MyDrive/Thesis data/label_class_3.dat', delimiter=' ')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "trainingdata.shape = (880, 40, 99)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OVR6cXD2-3Bv"
      },
      "source": [
        "## SPLIT DATA INTO TEST AND TRAINING\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amAeXCz6KRyA"
      },
      "source": [
        "Use ONLY TRAINING DATA to train the GAN\r\n",
        "\r\n",
        "uncomment the code only once"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1L4cc6o1pd4"
      },
      "source": [
        "# testsize=0.3125"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nz0apjuvteKN"
      },
      "source": [
        "# # valence\r\n",
        "# xtrain, xtest, ytrain, ytest = train_test_split(A_norm, valence, test_size=testsize ,shuffle = True,random_state = 100)\r\n",
        "# ytrain.shape\r\n",
        "# np.save(\"/content/drive/MyDrive/Thesis data/val22\",ytrain)\r\n",
        "# np.save(\"/content/drive/MyDrive/Thesis data/val10\",ytest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmc9FLdvt4kV"
      },
      "source": [
        "# # arousal\r\n",
        "# xtrain, xtest, ytrain, ytest = train_test_split(A_norm, arousal, test_size=testsize ,shuffle = True,random_state = 100)\r\n",
        "# ytrain.shape\r\n",
        "# np.save(\"/content/drive/MyDrive/Thesis data/aro22\",ytrain)\r\n",
        "# np.save(\"/content/drive/MyDrive/Thesis data/aro10\",ytest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1B2J-Oj2t5wC"
      },
      "source": [
        "# # dominance\r\n",
        "# xtrain, xtest, ytrain, ytest = train_test_split(A_norm, dominance, test_size=testsize ,shuffle = True,random_state = 100)\r\n",
        "# ytrain.shape\r\n",
        "# np.save(\"/content/drive/MyDrive/Thesis data/dom22\",ytrain)\r\n",
        "# np.save(\"/content/drive/MyDrive/Thesis data/dom10\",ytest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JfvzIl3t8JA"
      },
      "source": [
        "# # liking\r\n",
        "# xtrain, xtest, ytrain, ytest = train_test_split(A_norm, liking, test_size=testsize ,shuffle = True,random_state = 100)\r\n",
        "# ytrain.shape\r\n",
        "# np.save(\"/content/drive/MyDrive/Thesis data/lik22\",ytrain)\r\n",
        "# np.save(\"/content/drive/MyDrive/Thesis data/lik10\",ytest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "400Z2bqJsPQZ"
      },
      "source": [
        "## Data preparation\n",
        "\n",
        "To train WGAN-GP I would be using EEG dataset of extracted features. Each\n",
        "sample in this dataset is a 40*99 matrix of features associated with a label"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmfRIQvIsPQa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8201a36-0f50-4b54-93f4-7740aa6070df"
      },
      "source": [
        "IMG_SHAPE = (40, 99, 1)\n",
        "BATCH_SIZE = 40 #32\n",
        "\n",
        "# Size of the noise vector\n",
        "noise_dim = 256 # also tried 128\n",
        "train_images = A_norm.reshape(-1,40,99,1) # Add extra dimension to the dataset\n",
        "\n",
        "\n",
        "print(f\"Number of examples: {len(train_images)}\")\n",
        "print(f\"Shape of the images in the dataset: {train_images.shape[1:]}\")\n",
        "\n",
        "\n",
        "train_images.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of examples: 880\n",
            "Shape of the images in the dataset: (40, 99, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(880, 40, 99, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCc7ct94sPQa"
      },
      "source": [
        "## Create the critic (simply called the discriminator in the original GAN variant)\n",
        "\n",
        "The samples in the dataset have a (40, 99, 1) shape for each input image.\n",
        "\n",
        "I define a convolutional blocks function that takes in various parameters like the number of filters,activation,batch normalization and dropouts.\n",
        "\n",
        "In general the strides chosen for the Conv2D layers are of stride 2 this shrinks the image size by half each time"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oa1ag8Y1sPQa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f8f33c59-f16c-4480-8c74-07f6301970f4"
      },
      "source": [
        "\n",
        "def conv_block(\n",
        "    x,\n",
        "    filters,\n",
        "    activation,\n",
        "    kernel_size=(3, 3),\n",
        "    strides=(1, 1),\n",
        "    padding=\"same\",\n",
        "    use_bias=True,\n",
        "    use_bn=False,\n",
        "    use_dropout=False,\n",
        "    drop_value=0.5,\n",
        "):\n",
        "    x = layers.Conv2D(\n",
        "        filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias\n",
        "    )(x)\n",
        "    if use_bn:\n",
        "        x = layers.BatchNormalization()(x)\n",
        "    x = activation(x)\n",
        "    if use_dropout:\n",
        "        x = layers.Dropout(drop_value)(x)\n",
        "    return x\n",
        "\n",
        "\n",
        "def get_discriminator_model():\n",
        "    img_input = layers.Input(shape=IMG_SHAPE)\n",
        "  \n",
        "    x = layers.ZeroPadding2D((2, 2))(img_input)\n",
        "    x = conv_block(\n",
        "        x,\n",
        "        64,\n",
        "        kernel_size=(5, 5),\n",
        "        strides=(2, 2),\n",
        "        use_bn=False,\n",
        "        use_bias=True,\n",
        "        activation=layers.LeakyReLU(0.2),\n",
        "        use_dropout=False,\n",
        "        drop_value=0.3,\n",
        "    )\n",
        "    x = conv_block(\n",
        "        x,\n",
        "        128,\n",
        "        kernel_size=(5, 5),\n",
        "        strides=(2, 2),\n",
        "        use_bn=False,\n",
        "        activation=layers.LeakyReLU(0.2),\n",
        "        use_bias=True,\n",
        "        use_dropout=True,\n",
        "        drop_value=0.3,\n",
        "    )\n",
        "    x = conv_block(\n",
        "        x,\n",
        "        256,\n",
        "        kernel_size=(5, 5),\n",
        "        strides=(2, 2),\n",
        "        use_bn=False,\n",
        "        activation=layers.LeakyReLU(0.2),\n",
        "        use_bias=True,\n",
        "        use_dropout=True,\n",
        "        drop_value=0.3,\n",
        "    )\n",
        "    x = conv_block(\n",
        "        x,\n",
        "        512,\n",
        "        kernel_size=(5, 5),\n",
        "        strides=(2, 2),\n",
        "        use_bn=False,\n",
        "        activation=layers.LeakyReLU(0.2),\n",
        "        use_bias=True,\n",
        "        use_dropout=False,\n",
        "        drop_value=0.3,\n",
        "    )\n",
        "\n",
        "    x = layers.Flatten()(x)\n",
        "    x = layers.Dropout(0.2)(x)\n",
        "    x = layers.Dense(1)(x)\n",
        "\n",
        "    d_model = keras.models.Model(img_input, x, name=\"discriminator\")\n",
        "    return d_model\n",
        "\n",
        "\n",
        "d_model = get_discriminator_model()\n",
        "d_model.summary()\n",
        "plot_model(d_model,show_shapes=True)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"discriminator\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 40, 99, 1)]       0         \n",
            "_________________________________________________________________\n",
            "zero_padding2d (ZeroPadding2 (None, 44, 103, 1)        0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 22, 52, 64)        1664      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)      (None, 22, 52, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 11, 26, 128)       204928    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 11, 26, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 11, 26, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 6, 13, 256)        819456    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_2 (LeakyReLU)    (None, 6, 13, 256)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 6, 13, 256)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 3, 7, 512)         3277312   \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_3 (LeakyReLU)    (None, 3, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 10752)             0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 10752)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 10753     \n",
            "=================================================================\n",
            "Total params: 4,314,113\n",
            "Trainable params: 4,314,113\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GGGM36bsPQa"
      },
      "source": [
        "## Create the generator\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6lu9twmsPQa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5e3ff9d9-a35f-4784-d780-a2be681b19df"
      },
      "source": [
        "\n",
        "def upsample_block(\n",
        "    x,\n",
        "    filters,\n",
        "    activation,\n",
        "    kernel_size=(3, 3),\n",
        "    strides=(1, 1),\n",
        "    up_size=(2, 2),\n",
        "    padding=\"same\",\n",
        "    use_bn=False,\n",
        "    use_bias=True,\n",
        "    use_dropout=False,\n",
        "    drop_value=0.3,\n",
        "):\n",
        "    x = layers.UpSampling2D(up_size)(x)\n",
        "    x = layers.Conv2D(\n",
        "        filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias\n",
        "    )(x)\n",
        "\n",
        "    if use_bn:\n",
        "        x = layers.BatchNormalization()(x)\n",
        "\n",
        "    if activation:\n",
        "        x = activation(x)\n",
        "    if use_dropout:\n",
        "        x = layers.Dropout(drop_value)(x)\n",
        "    return x\n",
        "\n",
        "\n",
        "def get_generator_model():\n",
        "    noise = layers.Input(shape=(noise_dim,))\n",
        "    x = layers.Dense(5 * 11 * 256, use_bias=False)(noise)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.LeakyReLU(0.2)(x)\n",
        "\n",
        "    x = layers.Reshape((5, 11, 256))(x)\n",
        "    x = upsample_block(\n",
        "        x,\n",
        "        128,\n",
        "        layers.LeakyReLU(0.2),\n",
        "        strides=(1, 1),\n",
        "        use_bias=False,\n",
        "        use_bn=True,\n",
        "        padding=\"same\",\n",
        "        use_dropout=False,\n",
        "        up_size=(2, 3)\n",
        "    )\n",
        "    x = upsample_block(\n",
        "        x,\n",
        "        64,\n",
        "        layers.LeakyReLU(0.2),\n",
        "        strides=(1, 1),\n",
        "        use_bias=False,\n",
        "        use_bn=True,\n",
        "        padding=\"same\",\n",
        "        use_dropout=False,\n",
        "        up_size=(2, 3)\n",
        "    )\n",
        "    x = upsample_block(\n",
        "        x, 1, layers.Activation(\"tanh\"), strides=(1, 1), use_bias=False, use_bn=True,up_size=(2, 1)\n",
        "    )\n",
        "    \n",
        "    g_model = keras.models.Model(noise, x, name=\"generator\")\n",
        "    return g_model\n",
        "\n",
        "\n",
        "g_model = get_generator_model()\n",
        "g_model.summary()\n",
        "plot_model(g_model,show_shapes=True)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"generator\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         [(None, 256)]             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 14080)             3604480   \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 14080)             56320     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_4 (LeakyReLU)    (None, 14080)             0         \n",
            "_________________________________________________________________\n",
            "reshape (Reshape)            (None, 5, 11, 256)        0         \n",
            "_________________________________________________________________\n",
            "up_sampling2d (UpSampling2D) (None, 10, 33, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 10, 33, 128)       294912    \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 10, 33, 128)       512       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_5 (LeakyReLU)    (None, 10, 33, 128)       0         \n",
            "_________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2 (None, 20, 99, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 20, 99, 64)        73728     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 20, 99, 64)        256       \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_6 (LeakyReLU)    (None, 20, 99, 64)        0         \n",
            "_________________________________________________________________\n",
            "up_sampling2d_2 (UpSampling2 (None, 40, 99, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 40, 99, 1)         576       \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 40, 99, 1)         4         \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 40, 99, 1)         0         \n",
            "=================================================================\n",
            "Total params: 4,030,788\n",
            "Trainable params: 4,002,242\n",
            "Non-trainable params: 28,546\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L2lP2pbZsPQa"
      },
      "source": [
        "## Create the WGAN-GP model\n",
        "The generator and discriminator functions are ready, I use this to create WGAN-GP model. We will also override the `train_step` for training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIfk7jVksPQa"
      },
      "source": [
        "class WGAN(Model):\n",
        "    def __init__(\n",
        "        self,\n",
        "        discriminator,\n",
        "        generator,\n",
        "        latent_dim,\n",
        "        n_critic=3,\n",
        "        gp_weight=10.0,\n",
        "    ):\n",
        "        super(WGAN, self).__init__()\n",
        "        self.discriminator = discriminator\n",
        "        self.generator = generator\n",
        "        self.latent_dim = latent_dim\n",
        "        self.d_steps = n_critic\n",
        "        self.gp_weight = gp_weight\n",
        "\n",
        "    def compile(self, d_optimizer, g_optimizer, d_loss_fn, g_loss_fn):\n",
        "        super(WGAN, self).compile()\n",
        "        self.d_optimizer = d_optimizer\n",
        "        self.g_optimizer = g_optimizer\n",
        "        self.d_loss_fn = d_loss_fn\n",
        "        self.g_loss_fn = g_loss_fn\n",
        "\n",
        "    def gradient_penalty(self, batch_size, real_images, fake_images):\n",
        "        \"\"\" Calculates the gradient penalty.\n",
        "\n",
        "        This loss is calculated on an interpolated image\n",
        "        and added to the discriminator loss.\n",
        "        \"\"\"\n",
        "        # Get the interpolated image\n",
        "        diff = fake_images - real_images\n",
        "        alpha = tf.random.normal([batch_size, 1, 1, 1], 0.0, 1.0)  # Each image in the batch gets a random number between 0 and 1 \n",
        "        interpolated = real_images + alpha * diff # can also use : alpha*real + (1-alpha)*fake\n",
        "\n",
        "        with tf.GradientTape() as gp_tape:\n",
        "            gp_tape.watch(interpolated)\n",
        "            # 1. Get the discriminator output for this interpolated image.\n",
        "            pred = self.discriminator(interpolated, training=True)\n",
        "\n",
        "        # 2. Calculate the gradients w.r.t to this interpolated image.\n",
        "        grads = gp_tape.gradient(pred, [interpolated])[0]\n",
        "        # 3. Calculate the norm of the gradients.\n",
        "        norm = tf.sqrt(tf.reduce_sum(tf.square(grads), axis=[1, 2, 3]))\n",
        "        gp = tf.reduce_mean((norm - 1.0) ** 2) # or do 1-gradient norm\n",
        "        return gp\n",
        "\n",
        "    def train_step(self, real_images):\n",
        "        if isinstance(real_images, tuple):\n",
        "            real_images = real_images[0]\n",
        "\n",
        "        # Get the batch size\n",
        "        batch_size = tf.shape(real_images)[0]\n",
        "\n",
        "        # According to the original paper the following steps are computed for each of the batch:\n",
        "        # 1. Train the generator and get the generator loss\n",
        "        # 2. Train the discriminator and get the discriminator loss\n",
        "        # 3. Calculate the gradient penalty\n",
        "        # 4. Multiply this gradient penalty with a constant weight factor\n",
        "        # 5. Add the gradient penalty to the discriminator loss\n",
        "        # 6. Return the generator and discriminator losses as a loss dictionary\n",
        "\n",
        "        # Discriminator is trained first \n",
        "        # Generally the critic is trained 'x' times more than 1 step of the generator .The original WGAN recommends this value be 5.\n",
        "        # n_critic = 5 means that the critic is trained 5 times before updating the weights of the generator.\n",
        "        \n",
        "        for i in range(self.d_steps): \n",
        "            # Get the latent vector\n",
        "            random_latent_vectors = tf.random.normal(\n",
        "                shape=(batch_size, self.latent_dim)\n",
        "            )\n",
        "            with tf.GradientTape() as tape:\n",
        "                # Generate fake images from the latent vector\n",
        "                fake_images = self.generator(random_latent_vectors, training=True)\n",
        "                # Get the logits for the fake images\n",
        "                fake_logits = self.discriminator(fake_images, training=True)\n",
        "                # Get the logits for the real images\n",
        "                real_logits = self.discriminator(real_images, training=True)\n",
        "\n",
        "                # Calculate the discriminator loss using the fake and real image logits\n",
        "                d_cost = self.d_loss_fn(real_img=real_logits, fake_img=fake_logits)\n",
        "                # Calculate the gradient penalty\n",
        "                gp = self.gradient_penalty(batch_size, real_images, fake_images)\n",
        "                # Add the gradient penalty to the original discriminator loss\n",
        "                d_loss = d_cost + gp * self.gp_weight\n",
        "\n",
        "            # Get the gradients w.r.t the discriminator loss\n",
        "            d_gradient = tape.gradient(d_loss, self.discriminator.trainable_variables)\n",
        "            # Update the weights of the discriminator using the discriminator optimizer\n",
        "            self.d_optimizer.apply_gradients(\n",
        "                zip(d_gradient, self.discriminator.trainable_variables)\n",
        "            )\n",
        "\n",
        "        # Train the generator\n",
        "        # Get the latent vector\n",
        "        random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))\n",
        "        with tf.GradientTape() as tape:\n",
        "            # Generate fake images using the generator\n",
        "            generated_images = self.generator(random_latent_vectors, training=True)\n",
        "            # Get the discriminator logits for fake images\n",
        "            gen_img_logits = self.discriminator(generated_images, training=True)\n",
        "            # Calculate the generator loss\n",
        "            g_loss = self.g_loss_fn(gen_img_logits)\n",
        "\n",
        "        # Get the gradients w.r.t the generator loss\n",
        "        gen_gradient = tape.gradient(g_loss, self.generator.trainable_variables)\n",
        "        # Update the weights of the generator using the generator optimizer\n",
        "        self.g_optimizer.apply_gradients(\n",
        "            zip(gen_gradient, self.generator.trainable_variables)\n",
        "        )\n",
        "        return {\"d_loss\": d_loss, \"g_loss\": g_loss}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ATcxwWOpsPQa"
      },
      "source": [
        "## Create a callback that saves the generated data into the folder specified"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stfxEEzTsPQa"
      },
      "source": [
        "\n",
        "class GANMonitor(keras.callbacks.Callback):\n",
        "    def __init__(self, num_img=880, latent_dim=128):\n",
        "        self.num_img = num_img\n",
        "        self.latent_dim = latent_dim\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        random_latent_vectors = tf.random.normal(shape=(self.num_img, self.latent_dim))\n",
        "        generated_images = self.model.generator(random_latent_vectors)\n",
        "        # print((self.num_img))\n",
        "        arr = np.zeros((880,40,99))\n",
        "        for i in range(self.num_img):\n",
        "            img = generated_images[i].numpy()\n",
        "\n",
        "            arr[i] = img.reshape(40,99)\n",
        "            \n",
        "            # print(generated_images[i].shape)\n",
        "            # print(\"img shape\",img.shape)\n",
        "            # '/content/drive/MyDrive/Thesis data/\n",
        "        np.save(\"/content/drive/MyDrive/Thesis data/3rdGAN_trial2/Generated_ep{epoch}\".format(epoch=epoch+1),arr) #changed the location\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZlPyHJrfsPQa"
      },
      "source": [
        "## Train the end-to-end model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMnqwSM8sPQa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e00be662-278c-427d-c797-d52be2e1c743"
      },
      "source": [
        "# Instantiate the optimizer for both networks\n",
        "# (learning_rate=0.0002, beta_1=0.5 are recommended)\n",
        "generator_optimizer = keras.optimizers.Adam(\n",
        "    learning_rate=0.0002, beta_1=0.5, beta_2=0.9\n",
        ")\n",
        "discriminator_optimizer = keras.optimizers.Adam(\n",
        "    learning_rate=0.0002, beta_1=0.5, beta_2=0.9\n",
        ")\n",
        "\n",
        "# Define the loss functions for the discriminator,\n",
        "# which should be (fake_loss - real_loss).\n",
        "# We will add the gradient penalty later to this loss function.\n",
        "def discriminator_loss(real_img, fake_img):\n",
        "    real_loss = tf.reduce_mean(real_img)\n",
        "    fake_loss = tf.reduce_mean(fake_img)\n",
        "    return fake_loss - real_loss\n",
        "\n",
        "\n",
        "# Define the loss functions for the generator.\n",
        "def generator_loss(fake_img):\n",
        "    return -tf.reduce_mean(fake_img)\n",
        "\n",
        "\n",
        "# Set the number of epochs for trainining.\n",
        "epochs = 200\n",
        "\n",
        "# Instantiate the customer `GANMonitor` Keras callback.\n",
        "cbk = GANMonitor(num_img=880, latent_dim=noise_dim)\n",
        "\n",
        "# Instantiate the WGAN model.\n",
        "wgan = WGAN(\n",
        "    discriminator=d_model,\n",
        "    generator=g_model,\n",
        "    latent_dim=noise_dim,\n",
        "    n_critic=5,\n",
        ")\n",
        "\n",
        "# Compile the WGAN model.\n",
        "wgan.compile(\n",
        "    d_optimizer=discriminator_optimizer,\n",
        "    g_optimizer=generator_optimizer,\n",
        "    g_loss_fn=generator_loss,\n",
        "    d_loss_fn=discriminator_loss,\n",
        ")\n",
        "\n",
        "# Start training the model.\n",
        "wgan.fit(train_images, batch_size=BATCH_SIZE, epochs=epochs, callbacks=[cbk])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "22/22 [==============================] - 24s 481ms/step - d_loss: -58.4289 - g_loss: 87.6310\n",
            "Epoch 2/200\n",
            "22/22 [==============================] - 11s 488ms/step - d_loss: -50.3916 - g_loss: 70.8932\n",
            "Epoch 3/200\n",
            "22/22 [==============================] - 11s 495ms/step - d_loss: -40.1985 - g_loss: 28.7942\n",
            "Epoch 4/200\n",
            "22/22 [==============================] - 11s 502ms/step - d_loss: -34.9741 - g_loss: -4.5618\n",
            "Epoch 5/200\n",
            "22/22 [==============================] - 11s 509ms/step - d_loss: -33.7938 - g_loss: -5.3415e-04\n",
            "Epoch 6/200\n",
            "22/22 [==============================] - 11s 513ms/step - d_loss: -31.9168 - g_loss: 0.2852\n",
            "Epoch 7/200\n",
            "22/22 [==============================] - 11s 506ms/step - d_loss: -30.4626 - g_loss: 0.4934\n",
            "Epoch 8/200\n",
            "22/22 [==============================] - 11s 502ms/step - d_loss: -29.7883 - g_loss: 0.1853\n",
            "Epoch 9/200\n",
            "22/22 [==============================] - 11s 501ms/step - d_loss: -28.2695 - g_loss: -3.1710\n",
            "Epoch 10/200\n",
            "22/22 [==============================] - 11s 500ms/step - d_loss: -27.2951 - g_loss: -7.2866\n",
            "Epoch 11/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -25.6472 - g_loss: -11.1268\n",
            "Epoch 12/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -26.2968 - g_loss: -10.1543\n",
            "Epoch 13/200\n",
            "22/22 [==============================] - 11s 506ms/step - d_loss: -25.0971 - g_loss: -15.4832\n",
            "Epoch 14/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -23.9884 - g_loss: -16.4369\n",
            "Epoch 15/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -24.4544 - g_loss: -16.2801\n",
            "Epoch 16/200\n",
            "22/22 [==============================] - 11s 502ms/step - d_loss: -23.9443 - g_loss: -17.8768\n",
            "Epoch 17/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -23.4563 - g_loss: -15.3355\n",
            "Epoch 18/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -22.9181 - g_loss: -23.5962\n",
            "Epoch 19/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -21.6863 - g_loss: -33.6062\n",
            "Epoch 20/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -21.1099 - g_loss: -10.1621\n",
            "Epoch 21/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -22.2625 - g_loss: -34.9063\n",
            "Epoch 22/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -21.0513 - g_loss: -27.5584\n",
            "Epoch 23/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -20.6862 - g_loss: -9.1100\n",
            "Epoch 24/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -19.9883 - g_loss: -7.1093\n",
            "Epoch 25/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -20.0561 - g_loss: -13.9090\n",
            "Epoch 26/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -19.4928 - g_loss: -3.5335\n",
            "Epoch 27/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -19.9876 - g_loss: -9.6550\n",
            "Epoch 28/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -18.6427 - g_loss: -7.1771\n",
            "Epoch 29/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -18.3815 - g_loss: -7.4188\n",
            "Epoch 30/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -18.0936 - g_loss: -3.2433\n",
            "Epoch 31/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -17.6345 - g_loss: -1.9366\n",
            "Epoch 32/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -17.4502 - g_loss: -3.4625\n",
            "Epoch 33/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -17.0939 - g_loss: -1.4107\n",
            "Epoch 34/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -17.2790 - g_loss: -1.8033\n",
            "Epoch 35/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -16.6471 - g_loss: -1.7781\n",
            "Epoch 36/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -16.3398 - g_loss: 4.0248\n",
            "Epoch 37/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -16.2100 - g_loss: -0.1705\n",
            "Epoch 38/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -15.5431 - g_loss: 1.9670\n",
            "Epoch 39/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -15.8014 - g_loss: 4.0486\n",
            "Epoch 40/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -15.2295 - g_loss: 4.3337\n",
            "Epoch 41/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -15.2326 - g_loss: 2.6508\n",
            "Epoch 42/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -15.2568 - g_loss: 1.8160\n",
            "Epoch 43/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -14.4354 - g_loss: 0.7088\n",
            "Epoch 44/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -14.2723 - g_loss: -0.9054\n",
            "Epoch 45/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -14.0972 - g_loss: -1.1077\n",
            "Epoch 46/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -13.6971 - g_loss: 0.0633\n",
            "Epoch 47/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -13.9053 - g_loss: 2.4504\n",
            "Epoch 48/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -13.4173 - g_loss: 2.8757\n",
            "Epoch 49/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -13.5455 - g_loss: -1.0934\n",
            "Epoch 50/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -13.5832 - g_loss: -1.0735\n",
            "Epoch 51/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -12.9027 - g_loss: -1.5640\n",
            "Epoch 52/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -12.5036 - g_loss: 0.9240\n",
            "Epoch 53/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -12.6689 - g_loss: 3.1953\n",
            "Epoch 54/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -12.2486 - g_loss: 0.3826\n",
            "Epoch 55/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -11.9704 - g_loss: 1.2991\n",
            "Epoch 56/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -12.0278 - g_loss: 1.0519\n",
            "Epoch 57/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -11.6375 - g_loss: 0.4201\n",
            "Epoch 58/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -11.5221 - g_loss: 0.4143\n",
            "Epoch 59/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -11.4568 - g_loss: 0.9374\n",
            "Epoch 60/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -10.9376 - g_loss: 1.1560\n",
            "Epoch 61/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -11.5662 - g_loss: 0.7284\n",
            "Epoch 62/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -10.7606 - g_loss: -1.1497\n",
            "Epoch 63/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -10.7260 - g_loss: 0.0853\n",
            "Epoch 64/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -11.0452 - g_loss: -0.2105\n",
            "Epoch 65/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -10.3204 - g_loss: 0.2390\n",
            "Epoch 66/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -10.5832 - g_loss: -0.3731\n",
            "Epoch 67/200\n",
            "22/22 [==============================] - 11s 502ms/step - d_loss: -10.3456 - g_loss: -0.1493\n",
            "Epoch 68/200\n",
            "22/22 [==============================] - 11s 502ms/step - d_loss: -10.5072 - g_loss: -0.1974\n",
            "Epoch 69/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -9.9287 - g_loss: -0.4179\n",
            "Epoch 70/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -9.7898 - g_loss: -0.7155\n",
            "Epoch 71/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -10.2254 - g_loss: -0.8485\n",
            "Epoch 72/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -9.3987 - g_loss: -0.4315\n",
            "Epoch 73/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -9.6831 - g_loss: -1.7721\n",
            "Epoch 74/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -9.5323 - g_loss: -1.3652\n",
            "Epoch 75/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -9.6380 - g_loss: 0.0705\n",
            "Epoch 76/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -9.4617 - g_loss: -0.2536\n",
            "Epoch 77/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -9.0522 - g_loss: -0.7807\n",
            "Epoch 78/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -9.2363 - g_loss: 0.4957\n",
            "Epoch 79/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -9.2998 - g_loss: 0.2391\n",
            "Epoch 80/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -8.6072 - g_loss: 0.6897\n",
            "Epoch 81/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.8901 - g_loss: 0.1868\n",
            "Epoch 82/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -8.5295 - g_loss: 0.0887\n",
            "Epoch 83/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.9856 - g_loss: 0.3359\n",
            "Epoch 84/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -8.8811 - g_loss: -4.0664e-04\n",
            "Epoch 85/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.7492 - g_loss: -0.0141\n",
            "Epoch 86/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -8.7551 - g_loss: 0.4350\n",
            "Epoch 87/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -8.5737 - g_loss: -0.2732\n",
            "Epoch 88/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.9244 - g_loss: 0.1468\n",
            "Epoch 89/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.5959 - g_loss: -0.4953\n",
            "Epoch 90/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.1088 - g_loss: 1.5565\n",
            "Epoch 91/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -8.8183 - g_loss: -0.4103\n",
            "Epoch 92/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -8.5410 - g_loss: -0.1790\n",
            "Epoch 93/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -8.4972 - g_loss: 0.0133\n",
            "Epoch 94/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -8.5777 - g_loss: -0.9600\n",
            "Epoch 95/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.2761 - g_loss: -0.6062\n",
            "Epoch 96/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.5974 - g_loss: 0.5059\n",
            "Epoch 97/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.9972 - g_loss: -0.1113\n",
            "Epoch 98/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -8.4699 - g_loss: -0.4713\n",
            "Epoch 99/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -8.1710 - g_loss: -0.2036\n",
            "Epoch 100/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.2508 - g_loss: -1.6577\n",
            "Epoch 101/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -8.0047 - g_loss: -0.7728\n",
            "Epoch 102/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.0905 - g_loss: -0.1296\n",
            "Epoch 103/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.3453 - g_loss: -1.6729\n",
            "Epoch 104/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.3525 - g_loss: -1.1731\n",
            "Epoch 105/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.9219 - g_loss: 0.2326\n",
            "Epoch 106/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.6076 - g_loss: 0.3402\n",
            "Epoch 107/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -7.5585 - g_loss: 2.0506\n",
            "Epoch 108/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.8937 - g_loss: -0.4316\n",
            "Epoch 109/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.6523 - g_loss: 0.6305\n",
            "Epoch 110/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -8.0551 - g_loss: -0.1585\n",
            "Epoch 111/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.8821 - g_loss: -0.0793\n",
            "Epoch 112/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.9237 - g_loss: 1.6097\n",
            "Epoch 113/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.6155 - g_loss: 1.6635\n",
            "Epoch 114/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.2313 - g_loss: 0.5005\n",
            "Epoch 115/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.6852 - g_loss: -1.0900\n",
            "Epoch 116/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.8489 - g_loss: 0.1080\n",
            "Epoch 117/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.7074 - g_loss: -1.1178\n",
            "Epoch 118/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.5588 - g_loss: -0.0021\n",
            "Epoch 119/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.6376 - g_loss: 0.5936\n",
            "Epoch 120/200\n",
            "22/22 [==============================] - 11s 505ms/step - d_loss: -7.7212 - g_loss: 0.1142\n",
            "Epoch 121/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.5228 - g_loss: -0.0120\n",
            "Epoch 122/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.2803 - g_loss: 1.1037\n",
            "Epoch 123/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.6435 - g_loss: -0.0222\n",
            "Epoch 124/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.5434 - g_loss: -1.1747\n",
            "Epoch 125/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.2564 - g_loss: -0.6799\n",
            "Epoch 126/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.4831 - g_loss: 0.0848\n",
            "Epoch 127/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.4908 - g_loss: -0.6167\n",
            "Epoch 128/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.7163 - g_loss: 0.1230\n",
            "Epoch 129/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.4692 - g_loss: 0.8674\n",
            "Epoch 130/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.1674 - g_loss: -0.1399\n",
            "Epoch 131/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.1910 - g_loss: 0.0378\n",
            "Epoch 132/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.4593 - g_loss: -0.1046\n",
            "Epoch 133/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.1874 - g_loss: -0.1710\n",
            "Epoch 134/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.1466 - g_loss: 0.0931\n",
            "Epoch 135/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.4577 - g_loss: 1.2269\n",
            "Epoch 136/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.9814 - g_loss: 0.4546\n",
            "Epoch 137/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.6106 - g_loss: -0.8864\n",
            "Epoch 138/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.4977 - g_loss: -1.6818\n",
            "Epoch 139/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.2271 - g_loss: -0.6876\n",
            "Epoch 140/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.5669 - g_loss: -0.8906\n",
            "Epoch 141/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.6435 - g_loss: -0.4490\n",
            "Epoch 142/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.1450 - g_loss: -0.4413\n",
            "Epoch 143/200\n",
            "22/22 [==============================] - 11s 502ms/step - d_loss: -7.1157 - g_loss: -0.3880\n",
            "Epoch 144/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.9115 - g_loss: 0.3515\n",
            "Epoch 145/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.8124 - g_loss: -0.5842\n",
            "Epoch 146/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.5733 - g_loss: 0.3693\n",
            "Epoch 147/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.4028 - g_loss: 0.2822\n",
            "Epoch 148/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.7067 - g_loss: 0.4332\n",
            "Epoch 149/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.1358 - g_loss: -0.4299\n",
            "Epoch 150/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.0357 - g_loss: 0.0421\n",
            "Epoch 151/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.9187 - g_loss: 0.1409\n",
            "Epoch 152/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.8406 - g_loss: 0.0875\n",
            "Epoch 153/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.1264 - g_loss: -0.5849\n",
            "Epoch 154/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.7214 - g_loss: -0.1070\n",
            "Epoch 155/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.1593 - g_loss: 0.6743\n",
            "Epoch 156/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.1300 - g_loss: -0.2290\n",
            "Epoch 157/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.8142 - g_loss: -0.4221\n",
            "Epoch 158/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.1204 - g_loss: -1.1315\n",
            "Epoch 159/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.1349 - g_loss: 0.3392\n",
            "Epoch 160/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.4273 - g_loss: 0.9875\n",
            "Epoch 161/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.8294 - g_loss: 0.3585\n",
            "Epoch 162/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -7.2802 - g_loss: -0.5334\n",
            "Epoch 163/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.8853 - g_loss: 0.1409\n",
            "Epoch 164/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.7025 - g_loss: -0.7679\n",
            "Epoch 165/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.7409 - g_loss: -0.2522\n",
            "Epoch 166/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.5518 - g_loss: 0.5111\n",
            "Epoch 167/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.5755 - g_loss: -0.7144\n",
            "Epoch 168/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.5990 - g_loss: -0.1811\n",
            "Epoch 169/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.3687 - g_loss: -0.2871\n",
            "Epoch 170/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.4203 - g_loss: 0.1892\n",
            "Epoch 171/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.5637 - g_loss: 0.3828\n",
            "Epoch 172/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.6323 - g_loss: -0.1083\n",
            "Epoch 173/200\n",
            "22/22 [==============================] - 11s 502ms/step - d_loss: -6.8616 - g_loss: -0.8407\n",
            "Epoch 174/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -7.0012 - g_loss: -1.4169\n",
            "Epoch 175/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.4856 - g_loss: -0.6600\n",
            "Epoch 176/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.7320 - g_loss: 0.2282\n",
            "Epoch 177/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.4361 - g_loss: -0.3012\n",
            "Epoch 178/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.7556 - g_loss: -0.7626\n",
            "Epoch 179/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.4611 - g_loss: 0.0748\n",
            "Epoch 180/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.4820 - g_loss: -0.4168\n",
            "Epoch 181/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.5173 - g_loss: -0.2255\n",
            "Epoch 182/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.4422 - g_loss: -0.1918\n",
            "Epoch 183/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.8388 - g_loss: 0.4594\n",
            "Epoch 184/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.2497 - g_loss: -0.4763\n",
            "Epoch 185/200\n",
            "22/22 [==============================] - 11s 502ms/step - d_loss: -6.2063 - g_loss: -0.8110\n",
            "Epoch 186/200\n",
            "22/22 [==============================] - 11s 502ms/step - d_loss: -6.1241 - g_loss: -0.8355\n",
            "Epoch 187/200\n",
            "22/22 [==============================] - 11s 502ms/step - d_loss: -6.3814 - g_loss: -0.9597\n",
            "Epoch 188/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.3921 - g_loss: -1.7131\n",
            "Epoch 189/200\n",
            "22/22 [==============================] - 11s 502ms/step - d_loss: -6.5105 - g_loss: -1.3238\n",
            "Epoch 190/200\n",
            "22/22 [==============================] - 11s 502ms/step - d_loss: -6.5384 - g_loss: -1.5337\n",
            "Epoch 191/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.5351 - g_loss: -1.0365\n",
            "Epoch 192/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.6394 - g_loss: -0.6596\n",
            "Epoch 193/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.0887 - g_loss: -0.4977\n",
            "Epoch 194/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.2628 - g_loss: -1.0520\n",
            "Epoch 195/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.0558 - g_loss: -3.0132\n",
            "Epoch 196/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.8568 - g_loss: -2.1075\n",
            "Epoch 197/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.1582 - g_loss: -2.1552\n",
            "Epoch 198/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.2517 - g_loss: -0.0516\n",
            "Epoch 199/200\n",
            "22/22 [==============================] - 11s 504ms/step - d_loss: -6.4079 - g_loss: -0.8006\n",
            "Epoch 200/200\n",
            "22/22 [==============================] - 11s 503ms/step - d_loss: -6.1871 - g_loss: -0.9017\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9b900c6f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "Q3c48139m8oC",
        "outputId": "61946672-25a1-4563-cc23-c9cb61f752ec"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "i = [x for  x in range(1,201)]\r\n",
        "fig = plt.figure(figsize=(10,10))\r\n",
        "plt.ylim(-150, 150)\r\n",
        "p1 = plt.plot(i,wgan.history.history['d_loss']) # critic loss\r\n",
        "p2 = plt.plot(i,wgan.history.history['g_loss']) # generator loss\r\n",
        "\r\n",
        "plt.ylabel('Wasserstein Loss')\r\n",
        "plt.title('Wasserstein loss vs number of epochs')\r\n",
        "plt.xlabel('Number of Epochs')\r\n",
        "plt.legend((p1[0], p2[0]), ('Critic', 'Generator'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9b41feb6a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eeOIJwENV7969Oeecczj00EO56KKLiP1QwMSJEznuuOPo1q0b/fr1Y9myZQD07t2b3/3ud3Tv3p0HHniAd955h549e9KlSxdOOOEEVqxYwfz583n88ce5//776dy5M2PHjmX+/Pn07duXjh07cvzxx7Nw4UIALrvsMgYNGkTPnj256aab9vh90I+/i4iISPl8cDMsnxLfx2zcAU65q8Srp02bRteuXYu97plnnqF27dqMHz+e7du3c9RRR3HSSScBMGnSJKZNm0bTpk056qij+OKLL+jZsyfXXnstb731Fg0bNuSVV17h1ltvZfDgwQBkZWUR+3WktWvX8tVXX2FmPP3009xzzz3861//YtCgQdSoUYM//OEPAPTv35+BAwcycOBABg8ezHXXXcebb74JwOLFi/nyyy9JTU3d47dJgU1ERET2Gr/+9a/5/PPPqVKlCi1atGDy5MkMHz4cgPXr1zN79myqVKlCjx49aNasGQCdO3dm/vz51KlTh6lTp3LiiScCkJubS5MmTXY89vnnn7/j/8WLF3P++eezbNkysrKyaNmyZbHtGTduHK+//joAl1xySaFq2rnnnhuXsAYKbCIiIlJepVTCEqVdu3a89tprO84/8sgjrF69mu7du9O8eXMeeugh+vXrV+g+o0ePpmrVqjvOp6amkpOTQwiBdu3aMW7cuGKfq3r16jv+v/baa7n++us5/fTTGT16NLfddtsut73g4+0pjWETERGRpNW3b1+2bdvGY489tuOyLVu2ANCvXz8ee+wxsrOzAZg1axabN28u8bEOOeQQVq1atSOwZWdnM23atGJvu379evbff38AhgwZsuPymjVrsnHjxh3ne/XqxdChQwF48cUXOeaYY3bnZZZJgU1ERESSlpnx5ptvMmbMGFq2bEmPHj0YOHAgd999N1dddRVt27ala9eutG/fnmuuuabUGZlVqlRh+PDh/PGPf6RTp0507ty5xBmft912G+eeey7dunWjQYMGOy7v378/b7zxxo5JBw899BD//e9/6dixI88//zwPPPBA3N8DAIvNmthXde/ePcQGEIqIiMiumT59OocddlhlN2OfVNx7a2YTQwjdi95WFTYRERGRJKfAJiIiIpLkFNhERESkVPv68KnKsKvvqQKbiIiIlCgjI4M1a9YotMVRCIE1a9aQkZFR7vvoOGwiIiJSombNmrF48WJWrVpV2U3Zp2RkZOw4sG95KLCJiIhIidLT00s8yr9UHHWJioiIiCQ5BTYRERGRJKfAJiIiIpLkFNhEREREkpwCm4iIiEiSU2ATERERSXIKbCIiIiJJToFNREREJMkpsImIiIgkOQU2ERERkSSnwCYiIiKS5BTYRERERJKcApuIiIhIklNgExEREUlyCmwiIiIiSU6BTURERCTJKbCJiIiIJDkFNhEREZEkp8AmIiIikuQU2ERERESSnAKbiIiISJJTYBMRERFJcgpsIiIiIklOgU1EREQkySmwiYiIiCQ5BTYRERGRJKfAJiIiIpLkFNhEREREkpwCm4iIiEiSU2ATERERSXIKbCIiIiJJToFNREREJMkpsImIiIgkOQU2ERERkSSnwCYiIiKS5BTYRERERJKcApuIiIhIklNgExEREUlyCmwiIiIiSU6BTURERCTJKbCJiIiIJDkFNhEREZEkp8AmIiIikuQU2ERERESSnAKbiIiISJJTYBMRERFJcgpsIiIiIklOgU1EREQkyVVqYDOzwWa20symFrisnpl9Ymazo9O60eVmZg+a2Rwzm2xmXSuv5SIiIiIVp7IrbM8CJxe57GZgZAihDTAyOg9wCtAm+rsaeKyC2igiIiJSqSo1sIUQPgN+KnLxGcCQ6P8hwJkFLn8uuK+AOmbWpGJaKiIiIlJ5KrvCVpxGIYRl0f/LgUbR//sDiwrcbnF02U7M7Gozm2BmE1atWpW4loqIiIhUgGQMbDuEEAIQduN+T4YQuocQujds2DABLRMRERGpOMkY2FbEujqj05XR5UuAAwrcrll0mYiIiMg+LRkD29vAwOj/gcBbBS6/NJotegSwvkDXqYiIiMg+K60yn9zMXgZ6Aw3MbDHwN+AuYJiZXQksAM6Lbv4+cCowB9gCXF7hDRYRERGpBJUa2EIIA0q46vhibhuAXye2RSIiIiLJJxm7REVERESkAAU2ERERkSSnwCYiIiKS5BTYRERERJKcApuIiIhIklNgExEREUlyCmwiIiIiSU6BTURERCTJKbCJiIiIJDkFNhEREZEkp8AmIiIikuQU2ERERESSnAKbiIiISJJTYBMRERFJcgpsIiIiIklOgU1EREQkySmwiYiIiCQ5BbY9NeZeGP90ZbdCRERE9mEKbHtqxjsw6+PKboWIiIjswxTY9lR6NcjeUtmtEBERkX2YAtueSs+E7K2V3QoRERHZhymw7an0agpsIiIiklAKbHsqPVNdoiIiIpJQCmx7Sl2iIiIikmAKbHtKXaIiIiKSYApse0pdoiIiIpJgCmx7Kr0a5GVDbk5lt0RERET2UQpseyo9009z1C0qIiIiiaHAtqdigU3j2ERERCRBFNj2VHo1P9U4NhEREUkQBbY9pQqbiIiIJJgC255ShU1EREQSTIFtT6nCJiIiIgmmwLandlTYFNhEREQkMRTY9tSOCpu6REVERCQxFNj2lLpERUREJMEU2PaUJh2IiIhIgimw7SlV2ERERCTBFNj2lCpsIiIikmAKbHsqNR1S0lRhExERkYRRYIuH9GoKbCIiIpIwCmzxkJ6pLlERERFJGAW2eEjPVIVNREREEkaBLR7SVGETERGRxFFgiwdV2ERERCSBFNjiQZMOREREJIEU2OJBkw5EREQkgRTY4kFdoiIiIpJACmzxkF5NFTYRERFJGAW2eFCFTURERBJIgS0eNOlAREREEkiBLR5ikw5CqOyWiIiIyD5IgS0e0jMh5EFuVmW3RERERPZBCmzxkF7NTzXxQERERBJAgS0e0jP9VOPYREREJAEU2OJhR4VNgU1ERETiT4EtHnZU2NQlKiIiIvGnwBYPqrCJiIhIAimwxYMqbCIiIpJACmzxoEkHIiIikkAKbPGgw3qIiIhIAimwxYMqbCIiIpJACmzxoEkHIiIikkAKbPGgSQciIiKSQAps8aAuUREREUkgBbZ4SEmF1KqqsImIiEhCKLDFS3qmKmwiIiKSEAps8ZJeTYFNREREEkKBLV5UYRMREZEEUWCLF1XYREREJEEU2OIlPVOTDkRERCQhFNjiRV2iIiIikiAKbPGSXk0VNhEREUkIBbZ4UYVNREREEkSBLV406UBEREQSRIEtXjTpQERERBJEgS1e1CUqIiIiCaLAFi/p1SBnK+TlVXZLREREZB+jwBYv6Zl+mrOtctshIiIi+xwFtnhJr+an6hYVERGROFNgi5dYhU0TD0RERCTO0iq7ASUxs/nARiAXyAkhdDezesArwIHAfOC8EMLaympjITsCmypsIiIiEl/JXmHrE0LoHELoHp2/GRgZQmgDjIzOJ4cdXaKqsImIiEh8JXtgK+oMYEj0/xDgzEpsS2HpGX6qwCYiIiJxlsyBLQAfm9lEM7s6uqxRCGFZ9P9yoFFxdzSzq81sgplNWLVqVUW0FdLUJSoiIiKJkbRj2ICjQwhLzGw/4BMzm1HwyhBCMLNQ3B1DCE8CTwJ079692NvEnQ7rISIiIgmStBW2EMKS6HQl8AbQA1hhZk0AotOVldfCIjTpQERERBIkKQObmVU3s5qx/4GTgKnA28DA6GYDgbcqp4XFSIvGsKnCJiIiInGWrF2ijYA3zAy8jS+FED40s/HAMDO7ElgAnFeJbSxMFTYRERFJkKQMbCGEeUCnYi5fAxxf8S0qB1XYREREJEGSskt0r6QKm4iIiCSIAlu8pKaDpSqwiYiISNwpsMVTejV1iYqIiEjcKbDFU3qGKmwiIiISdwps8ZSWqQqbiIiIxJ0CWzypwiYiIiIJoMAWT2kZqrCJiIhI3CmwxVN6JmRvqexWiIiIyD5GgS2e0jMhWxU2ERERiS8FtnhKy4QcjWETERGR+FJgi6f0DFXYREREJO4U2OJJh/UQERGRBFBgiycd1kNEREQSQIEtntIyFdhEREQk7hTY4ik9mnQQQmW3RERERPYhCmzxlJ4BIQ9ysyu7JSIiIrIPUWCLp7RMP9WhPURERCSOFNjiKT3DT3VoDxEREYkjBbZ4UoVNREREEkCBLZ5UYRMREZEEUGCLp/RqfqofgBcREZE4UmCLp7SowqZfOxAREZE4UmCLp/RoDJsOnisiIiJxpMAWT6qwiYiISAIosMWTKmwiIiKSAAps8aQKm4iIiCSAAls87ZglqgqbiIiIxI8CWzztOA6bApuIiIjEjwJbPO34pQN1iYqIiEj8KLDFU2oapKSpwiYiIiJxpcAWb2mZqrCJiIhIXCmwxVt6hipsIiIiElcKbPGmCpuIiIjEmQJbvKVn6sffRUREJK4U2OItPQOyVWETERGR+FFgi7e0TMjRGDYRERGJHwW2eFOFTUREROJMgS3eVGETERGROFNgizdV2ERERCTOFNjiLb2aDushIiIicaXAFm9pGTqsh4iIiMSVAlu8pWeqS1RERETiSoEt3tIyfNJBCJXdEhEREdlHKLDFW3oGhDzIza7sloiIiMg+YpcCm5mlmFmtRDVmn5CW6ac6tIeIiIjESZmBzcxeMrNaZlYdmAr8YGY3Jr5pe6n0KLBpHJuIiIjESXkqbG1DCBuAM4EPgJbAJQlt1d4sXRU2ERERia/yBLZ0M0vHA9vbIYRsQCPqS5KW4afZCmwiIiISH+UJbE8A84HqwGdm1gLYkMhG7dV2dIkqsImIiEh8pJV1gxDCg8CDBS5aYGZ9EtekvVyswqZfOxAREZE4Kc+kg99Gkw7MzJ4xs2+BvhXQtr2TKmwiIiISZ+XpEr0imnRwElAXn3BwV0JbtTfbMelAFTYRERGJj/IENotOTwWeDyFMK3CZFJWmCpuIiIjEV3kC20Qz+xgPbB+ZWU0gL7HN2oulawybiIiIxFeZkw6AK4HOwLwQwhYzqw9cnthm7cVUYRMREZE4K88s0TwzawZcaGYAY0II7yS8ZXurdB2HTUREROKrPLNE7wJ+C/wQ/V1nZnckumF7rTRNOhAREZH4Kk+X6KlA5xBCHoCZDQEmAX9KZMP2WqlpkJKuCpuIiIjETXkmHQDUKfB/7UQ0ZJ+SnqkKm4iIiMRNeSpsdwKTzGwUfjiPY4GbE9qqvV1ahipsIiIiEjflmXTwspmNBg6PLvoj0CKRjdrrpSuwiYiISPyUp8JGCGEZ8HbsvJl9AzRPVKP2emmZkKPAJiIiIvFR3jFsRemXDkqTngHZGsMmIiIi8bG7gS3EtRX7GlXYREREJI5K7BI1s3coPpgZUD9hLdoXpGdC1ubKboWIiIjsI0obw3bfbl4nVarDphWV3QoRERHZR5QY2EIIYyqyIfuUjNqwbUNlt0JERET2Ebs7hk1KU7UWbFdgExERkfhQYEuEjFqwfSPk5VV2S0RERGQfoMCWCFVrAgGyNlV2S0RERGQfUOaBc83sYOBG/NcNdtw+hNA3ge3au1Wt5afbN3i1TURERGQPlOeXDl4FHgeeAnIT25x9RCykbdsAtSu3KSIiIrL3K09gywkhPJbwluxLClbY5H9P1mY/tIuIiEiclGcM2ztm9isza2Jm9WJ/CW/Z3iwjKqvp0B4Va+MKGPdI4ckea+bCf0+FTasqpg1LJ8FdzWHJtxXzfCIi8j+hPIFtID6G7UtgYvQ3IZGN2utVremnqrBVrM//DR/9CZZ/n3/Z9LdhwRcw++OKacOCLyEvx59XREQkTsoMbCGElsX8taqIxu211CVa8XKyYMqr/v+CcfmXL/w6uuyLimnH8il+OquCAqKIiPxPKDGwmVnf6PSs4v4qrol7oYKTDoqzcoYHDNl92Vvhp3n55+eMgC1rwFJh4Zd+WQiwaA8D2/zPYctP5b99LLCtnAbrF+/ec4qIiBRRWoXtuOi0fzF/pyW4XXu39GoeHIqrsG1aBY8fBeOfrvh27UvGPQwPHw5LJvr571+Gag2g3c+9whYCrJ4NW3+CRu1h7XxYv2TXnmj9UqMAACAASURBVGPdInj2NPjiP+W7fc52WDUDDvmZn6+oblhxq2bC1nWV3Yq92+KJ8NTxGn8rkoRKDGwhhL9Fp5cX83dFxTVxL2Tm49iKW+kt/97HOC36quLbVZmWT4GNy+P7eHk58PrVHsRmfQgdzoWWx8CW1bBmDiyMukaPud5Pd7XKNvU1IMDS78p3+1UzvE0dzoY6zffebtH3b/TJG3uTrWvhyd4w4rbKbkn5ZG2puIkwu2LS87BkAiweX9ktEZEiyhzDZmaNzOwZM/sgOt/WzK5MfNP2crGfpypq+VQ/XTKpYttTmUKA586E9/+we/fPy935vVw9G+q0iGaBngK5WdB5ADTv5dcv+NK7Q6vVh8POgKq1vXtzV8TGxC373l9DWWLdoY07QZuT4McxkL2thNeU51XWDUt3rU2Jtm0DTBgMP7xVOc+fm7N795s8DLK3eNd4eT6rkmxdC0+f6MtPIr39G3j6+MQ+x64KAWZ/4v8vn1y5bflfNP5pDaOQUpVnluizwEdA0+j8LOB3iWrQPqNq7eK7RFdEgW39wuTcw06E9Yu96jXnU+82jFnxgweu0qxdAE8c69WT2IY4N8craO3OhF7XwroFsF9baNwRGrTxrtGF42DhV3DAEZCaBs2PKHsjvOWn/OdY8YN/Vvu1hW3rYP2isl/nssmQXh3qtYI2/TxALCghJH7+b3jvBpjw37Iftzhj7i25CpaX54Era/OuP+78z71KWHB8YEWZ+hrcfWD5gvXsT/LHFoYAE58FS/HPqaxlqjTjn4HF38C3z+/+Y5Rl7XyY9oYvtxtX7Nlj/TjWl/OS7EoAXvkDbIgCw7LvS79tWVbNhKmv79ljJErWlspuwc7WLfL1wYe3VHZLJImVJ7A1CCEMA/IAQgg56BcPypZRq4Qu0Sle9QFY+j9yrK7Y3nr25vyNcV4uvHA2DL2o5IrIgi/hqT4enNbMye9SXbfAK2oNDoa+f4ZDT4NjbvCuaDMPZ7M/hp/mQvOefp8Dj4I1s0veQG5cAf86xA8LAjB1uI9D7PsXP7+sHBWH5VOgcXtISYEDj4a0DPjh7Z1f34+fwah/5t9nV61fDKPvhM/vL3zMuZjP7oFhl8KYe3b9sed+6qebVxVfIU6URePhjV9C1kYYfVfpt504BF48x5ed3Bzvvlv5A/S6zq+fN6r8z1vwNWZvg68f9/9nf+zLaCJ89TiE6HNbUeDzXzUTxj1aeHnJ2gwzPyz+s1g6CV44C4ZfUXxb1873ADzsUti8uux2xcZcNutRcmDbtNInTZX23iz5Fp45EYZfnt+jAP5deKgbzPqo7LYkyoTBcGcz+PrJinm+rWthxvvFf08LWjHNT6e/A6tm7dpz5OXC2H/Bu9f7rPhdrTCHALnZZd8uJwsmv7rrgbfoa584BB454n+nYBFH5Qlsm82sPhAAzOwIYH1CW1UKMzvZzGaa2Rwzu7my2lGmqrVge5G3KXubd+V1PN+rAf8rB1ddPgUwDzCxjcLcT2HjUlg1HRZ9s/N9Ni73btTMunD6w35ZbCOyerafNjgY0qrCBS9Ch3Py79uil88YBa+wAbQ42k9LGse2cJyHwK8eha+f8O7QVr39z1LKrjjk5UWBrYOfr1INDjkVvh0Cjx/tlZt5YzysDb8S6h/k1+9O19NXj0HI9VC1clrh62Z95IEntaqPRypY0SxO9tbCK/i5n/rnBPDTj7vetvJaPtUndHzyN9+gDb0QajWBY/4A88fmH46lqCUTvWu9XiufDTz2Pq+uVakBx97oYwfnljOwffUY3HmAb+zAJ65sXgVdB3pFuLzfz23r/fMtT8Dbus4/lzb9/HzBQPPlQ/DRLd69G/P2dfDy+XDvQfDKJTBvdPScG+DVywGDDUt8uSrq6ycgZyvM/AAe6Qkz3iu9bbM/8eX34H5eYd1WYP21biG8+3u4vx082tNDz7OnFW4/ePB+7gzIqOPV5nHRdzc3Gz7+s+94vXSeV5LKWjaLKm+A/vw/Xnks6sexPj6zag344Eb44sGSHyME+O6lsoNubjY8fcLOO0frl8AHN8O/28HQAfnDK0oS+x6nVYUvHij9tgVlbfFAPvLvMOkFGHwSPNLDeybKkrPdX+MTx8K9rcveKR3/NLx+FQw5rfxh67N74d+H+mQW8OXjvRt8vR/73km5lSewXQ+8DbQ2sy+A54DrEtqqEphZKvAIcArQFhhgZm0roy1lKm7SwarpvqE9oCc0OGTvr7Dl5cJ7fyh7j3DZZA8orXr75IAQfKOVWc83tN8O2fk+C8dB7nY460mf+YkVCGwz/bRBm+Kfr/mRfppaFZp29v+bdPLnKimwLfrGg8rBJ8MHN/kGqsO5HrwaHFx2sFq3wKtDscAGcOaj0D/aKLx3PTx3OgzpD1mb4LznvJ0blsDmNTs/3pq5vjIture8bb3voR54jJ+PVcTAN7Kv/8KrfOc+66G1uLFoc0bCo73gzubwz8bwYbTfs3a+VyXbnx2dLyOwheDVvh/Hesgo76SSEHzDuXiCb9CHDvCNx4XDfIJIZj3vMi5q82p45VKo0QiuHAEdL4Axd8OU4f5ZVa0Brfp44CuuKzA32587BBh1p7/umo19Yzf+aQ9MTbvACbd5dXXWh/n33bqu5MAw4b/++ZY0K3jG+75B37jcl/WsTdD3Vqh9QOEKa6xr88Ob/bVOfd0rvYdf5SFy4Vcehp470z/ndQvhwld8+MX3Qws/57YN3q3b7iy4egzU3t8DX0khdOtaf/w2/aBJ9J2JtW3Z914Zm/QCdL4Iznwcul4Kq2d5e2Lf/x/egufPhOoN4PL3oeslHlTWL/Fled0CX+57XOM7Rs+dWf4q7sKv4J5W8O1zpd9u82oY8TcPMQWr6Wvn+2X1WsN130HbM+GTv/jQguLM/xze/CW8fW3+dzBnu5+PjfMDr9gtHu/hOFalysvz92H8U9D2dKjd3Nd3pVnxg9+u66Uweah/r0Lwz6Ck92jrOl+fzHgPTr4LbpoLZzziO1pfP1H682VtgUeP9NeYm+Xh+sVzvWu2OLk58PVjvqO04gd45gRYPaf051i/GD67z6uyQ/r7kIdhl/qy2O4smPCML8MVafvG3R8nmwTKE9im4Yf46AVcA7QDZiSyUaXoAcwJIcwLIWQBQ4EzKqktpStu0kFsb7RxB9i/q68892SAdGX7aZ6vlGaWsee+fDI06egD8dfO95XvjPeh0wAPB1NfL7w3D15JSa0KjTr4hrhBmwKBbRZU38+rb8Vp3NFXQPt39T1W8HFsrfvCpBeL32gt+hqadoVzBueHu0Ojw3M06VT23ueOCQcFAlt6JnQbCIM+h1+Og8veg4teg19+Cfsdln/bomFw2pvwxHG+Ml1aZHLKxCEeDE/6BzQ8rHA16d3rAYPzX/DgWa/VzoeP2bTKN/bZW6DT+XDQCfDNk74Sjj1W92hOUXEVtu2bfG/547/Afzp4xWXIaf6YD3aF0XeXPXZuxnteHTv5DrjpR2/v5e9Bw0P8N1iP+JWHpdh7mrUFvnwYHj3CK2DnPw/V68PP7oO6B3qw73aZ37Z1Hx87GjvcS8y3z8EdTb2i9mAXGHOXh4/rvvOQ8t4NHlaP+i1Uq+fd6rGuu5/mwf3tS658xILapBd2vi57m2/kR/3TH2PMPR62m3Tyzz/2Gjev8S77juf7euOtX3sI3L8bnHw3nHoP/H4q9LsDln3n70+fP/nrbf9z/2WNguubSc/7cnLEL6FRW7j0bQ+6b1zjVdWi5o7ynck2J/l3FfK/bxOf9QB77bfQ/z8+ueeUu+Gy9736/Nzp/v4NuxQaHuqX127mn2MI8OWDXmnZvxscdrq/lrOe9u/c8z/f+VAsMz/0z2jiEL//uoXe/b1tHbx/k3fJliRWady61id2hOBdzc//3F/fgJf98z37GQ/8o26Hkf/YeT084ZmoLe/DjHf9/w9v8eXo1cuiQwat9Wp2jUZekY0FuXmjfB115mPw8yjczh/r676SrJjmn1Ova/38O7/zSSmPH+0/rVfc4WpG/dN3+s9/3j/nqjWhy8Vw6KleLY5VMLdtgNd+UXi2+/cv+/J+1lPwq6/g4td8uXjxHH8Pf3ircFfujHf8czjxH3DZu74eeOaE0sdPjrrDu/6v/ATqtvCu+y1rPLSfdDtgZQ9/iIf1S7wa/UAnrwy/+cvEP2eClCewjQsh5IQQpoUQpoYQsoFxZd4rMfYHCu4CLI4uK8TMrjazCWY2YdWqSuonr1rLNxwFVwQrpnqQqNvSw8SW1eUbzJ6sYntHm1aWfJstP/lrbNzBNwbgG6O8bF+5dLvMu20KdgOBh6rGHSCtip9v0il/A7Jqlm/cS5Ka5hvz3kV6zH/2b6jREF4eUPiYbNnb/LEP6OGB4fIPPGTFDoDcuKN335bWDbB8im+89ium4GvmK+MDj4Y2J0C9lvmPG7tvzMi/w6sDoX5rwApXbXKzfYzVgcd4Jah1Xx/nl73VA9e8UXDUdR5iUlI8eC36Ov/xQ4D3fu8b9gEvw6n3+gq7ai0fuzd3JNRq5stmtQaFJx58dq9XOO7cH57u6xWS/drCqffBJW/CVZ/CQcfD6Du8i6Wk2bE5WfDJX33D3uVSf48P61846Pa4CqrU9D3+h7r72MKPb/Xnu/x9f+3gG6iLhntVIVZJbXmcv28Fx7HNeA/e+S00Oxw6X+iP0/fP3tWenuHVyBZHw37tPFCAdwuumOIVh7ev8/BTsOIWs3Wdb7Sq1PTri34Xpg737/npD8PhV3qbj7sp+vw7eEjL2pJ/gOdul/l4zFkf+ud65uO+PIPvfBz5a/jt916NPDo6XE2nCz2AT3/Hz+fl+nLS/Ej/LAEy63jFd/UsGPF/O7+O2Z/4DlCz7lBjP6jZ1L8TOVk+QeLQn0GdAwrfp8FBcOmbkLPNdwx6XO3fnVpN/Pq6LXxi0NeP+zqgz5/8uwDQ8Vw4b4iHiCGneVdvCL4eGHqhf9feuc7XFS8P8GX/svf8+/naVSUvXz+O8eW53z/9u/P2b+Cpvr7MXzgs+l7h7+mZj3qYGnufd9fG1tUbV/h72eMa32F8/yavok54Bjpf7J/DsEvh09s9tA2IjgH5/Ut+//FPQ/WG0DaqJXQeAJhXGYuTk+XLwX5tvUu/w3kw5xNfto7+Payc7t3I2zfl32flDO+G736Ff38K6nqpH39y5vt+/vN/w5Rh/n7m5fnfV4/696jDufnrpwte8Mr+kP7++oYO8GpvCD7BqW5LOOQUX0au+sQr4UNOL76Kv3yqv96e18ABh/tn1/5sOOsJX5fX3h96/MKD4zdPeffxkP4w+BQYfLLvJOyKn+aVvEyMuM3fiyadfN057Y3iezX2AqX90kFjM+sGZJpZFzPrGv31BqpVWAt3QwjhyRBC9xBC94YNG1ZOIzJq+Wy7gnuzy6f4FyMlxas5sHMlYG+yI7CVMtNtR+Wpo6/wG7X3PbumXf29aNrFN1yxvWnwkvXSSb5HHtOkk89g27zaNzoldYfGdL7Qu2ALqtEQBrziFaCXz8+vBC37zgPkAT38fJXq+aEK8isOBX+jtKBNK32D0+Bgr6qVV/X6UGv//Arb2gU+rqPj+b5X2qx74QHaP7zlXaixvfDWfby6tHCcr4DTMqHb5YXfg7QMH9OzaqZXgKa/A31u9QofeLWh980ecGZ+4I9p5q+/YJfohGehRmM4/m9ehfzDbLhomK90W/eBZt18T//8F3yc0sQSZr9OGOyf/4n/yA8iRWXW9erbfodBo3a+or/8Axj4tr8nBdVv7cE/plo9D29zR3nVdsb7vmfftItvsE+9Bwa85OPdUqLVX5VqXjW4ehSkpPplB5/sp69d6dWRBod4F27RYQ7zosrUyXf4933yK/nXheATDPZr62085W64YQa0PNavb9zBKxArp/txGVPSvZ3HXO8b4dP+Aw0P3vn9yajtgTLW/gN6+Mb0u5d8Y/zdS/7dPOJXhe/Xuo+HkK8f8+Vs00oPAaPu8O6qNiflv/7YDtLckR5KOp5X/GfVqJ0vq5e84TsAsR2smNiyekBPaF3kMCaH9fews2Gpd60+2MUrtS16eTXxuD/Cdy/6hJJz/+s7PGc+5kG6YMAq9HmM9tv1/KV//ye94Dt3V4/xqmlBKalw2gNw+C+8a370nX75pOf9s+zxC+j/AGxcBu/+zsfD9v+P7+SsnO7BrPNFvp7qcK5XBpdG1c+uA/Or+7Wb+Xsf+3xC8EATa//qWf58jdr5+VPu9uX9NxO8e/6cwb7svXxB/g7Bx3/2XoDef9r5PWjVx3e8vn3O1ynjHvVq+7Lvvbt19kf+HT3yN/kBGny5/OWXvgM26As44tfwzRPwysXe7XvEr/KXj3qt/HNv0gmGDfQK3soZ/pp+mudjMTNq+84H+PfynMHR8JbIMTf4a3j/Dx7Qsrb4429e5UNtyjvbe/FE37F7+vidq5hrF/iyffhVXtk75R5f108ZtvPjhODjjHfll20qWAlrTAD6AZcBzYB/AbFPdiNQzFJSIZYABXfzmkWXJZ+CPwBfpVr+l7RDND6oUXtIreKVpIIL8d4kVh0srcJWMLCBbxRWTM3fyJp5VeG9G6KQ1tXHqGVv2TmwgR9na9s6D0e7o1FbOOcZ32P95ik4+nf51Y1mPYq/T6ztyyZ7FyL4infeKF/RzHzfV7h9/rzr7WncMf89mvaGn/b5k2/4Du7ne/EbV0DNRh526h4IB53ot2vRy5ehya/6SqnLRb5ijKlWz0PbhMFe6Ym9xthGNObwq3zjs2aOV+3AA0DswMPrl3hY7vUb73opzWH9fS927L98T79K9fzrVs3ybpyWx0GbE0t/nK6X+t/uaNXHqwp3Nffz9dvAha9613pJzPI3sODLV90Dfdlo1ccrHc+d7mMgDzkl/3azPvaA2fki30BOeiF/Q7jgCw8X/R8svGGMadTeT5dP9kkWTTvnB/7zi+leLa3tnQZ4dfO+g7zbqX6b/C79gk64zQ/wPPLvHtSq1vRA1u4sD9ExTTr5hn3isz6rPbZcFKdBm5J3oJp2gdMf8sBW3HvQ5kT4/Q++7E/8r3/nz3jEK599/uQhInurV28BDj7Jg8RXj3hF++S78oPr2gW+we75S7/s7Ge8q7jzRYU/24JSUjxoZm/18ZB1mvtrbnls/ms66jofJ3nOYEhN97Yc/1cfStA3+s53vtCD8LBL/Hz3yws/T5eLfcdh4mCfLfvjGF8uug30QAr5gS2jln+3Y9qe7l2rb/3ag0mn870Cd9LtvtO302tK9ecbc7dXGM28Szw2OaH2AR7o2hYzmqjhwfk7Cf3+6dXTCc94+Op8YeHbVq/vO1Gj7vBq35Rh3j0c24E/5d6Sh62Ar5+uGuFjOht39PcWfKzng11h5P95yCpN9jbv4qze0LdHT/b2inmr3n79uId9OYntvDRq68WCb5+HnoMKL5NzRsKLZ/tObodzvSK/fLJ3V+fl+jJZpabv8FWSEgNbCGEIMMTMzg4hvFaBbSrNeKCNmbXEg9oFwIWl36WSVK3tp9s2+MDm9Yt81mhsJZ1Wxfewi45R2puUq8I2GWo28eoW+EZ4/WL/QsS0Pwc+/JMPnI6N7YPCgS3WZRabbbW7gQ08CLU8zrtqjviVTzio2zK/jUVl1vGD9C773qty3zzpIWjdQu8W6DnIX1dp3bQladzBN4xZW2Da6/6a6x7o17WJAtucT2D/7h4ATvi/ApWh6r4hjHXF9CwmTJ1yr2+MNy6PNsw/z99LjklNh5/9y2dsxjbM9Vr5e52z3Y9LBiUH2qL6/hkG98sPxODdO0MHeMA845HiN97xcviVvgGu1cRfx4HH+Ge4K8w8fE54Fk5/0MdMpmV6BScW2PLy/LNpfXz+RvKd3/ry26ybz0LNrFdydapOC+++WzLR1wM9frH7r7nrJd6Weq19Y3Vwv50/Z/Cdx4FvezXkuxc94PS6zrutCmrSyat/sz70QB/bmO5W28oI3ukZ3m3YecDO1x149M6XnXS7fz7jHobNK+HnT3gg+3GMX9+qt59Wb+BdhmUxg9Pu92NjvvXr6DkKhNcT/w59/1q4InzM9XDU7/K/i006+rp9xVQ/zFDtZoWf45Cfeeh57wbfNtRo5N2B3Qb6fVLSfWJWSTqe5+H3vet9/VO3pXdBl6TLRR7YfvzMZ17XOQBOvtO/lxuX+XtY1mdq5kMeauzn36PidnjSM/29Ovr3vj79aZ5XMg88pnzrw+JuU7OxjyUdfYfvyDQ73HdIZ33oO/PrF3vVve+f/dBGq2f6GLy6LX2s4/M/952PThd4MOt0vnfB7nhvLvb3cdl3+cMrwCcEVavv3/vJw7zSWqWmT+KqUs3DYe7asl9TApVWYYtpZma18MraU0BX4OYQQoX/7k4IIcfMfoMfyDcVGBxCmFbG3SpHbPxT7OC5BSccxOzfzfdMnj0NWhzlY7lWTPMV5YBXdu5eSDaxGUVldYkWfM31WsLZTxW+TWYdOORk/1L2+6dvwDJq+0pix23q+gYuNjB+TwIb+F7zC2d7KFn09c7dNUU16egbhAe7+OtteaxXKw49reS99/Jo0tE/7xnveSA86Z/51zXu4GOJYgPwU9ILd/+BB6z5Y73qVlz3WWqa/1xXWVr1hmvG5J+v1xKIBnwvGu97nQU/x9I0P8Lfzy8e8PaGAG8O8grIwLd3HgsVb7WbwSlxGMzc9y/Q67f5Qb7FkfmH1QBYNsm7b2JjM9ud5WNxhg7wDfKKqb4hK6mbPCXFN/LT3vCu7djs5t1Rq6lXK8prv0MLh5KiYhVt8DFVySQlxdcTNRr5TM+UdJ9NPm+0d9vvzo5TWhU473l45iQf73ZIkepkcd33sbAW0/lCHwt6+FU73zY9w9cXy773bsxJz/nO2LqFPv604SFlB6gGbbxSNusj36krbb1Tp7lXL5d9n7/T1PwIX0bnjix/9TolZeexwMWpVs8rovHS6ze+U/ze9V4hWz7Z14WN2nqwnTA4mqy2zrufYz0fV33iFbePb/Vgm7PNv8MFtT/bP6dJL+QHts2rfUhIj6t9eMOJf/fL6rbc+XOuROUJbFeEEB4ws35AfeAS4HmgUn4oMYTwPvB+ZTz3LqlaJLCtmApY4UHpR/3WKw4/fuZ7Q6npnvA3LvMvcoNS9riSQaxLdNt63/tIzyh8ffZWHzt1yKllP1bHC3yM1pyRHtiadt35i9Kkkx8aIL26j/3aE62P943lyL/7RveAMqpHzQ738V/Ne/mKPXZA3j0VC0GjbvfTdmfmX2fmlZIpr/osvbZneNWgoEN/5jMPj/59fNoTEwvLP83zClvTLru2A9HnVp+ccG/r/Mv6P1C4qyfZpVUtXHVt1dsnTGxY5tW72Z8Alr+xyKjlQXHOCB8kX6e5V19L07iDz5gFr5Ymi1pNfSB9leplfzcqy1HXeQV41O2+8ztvjO/A7G71NrOOj2PM2rx7O8s9rvZuzVa9i7++YLWvw7ke2Ka86l2iLY4q33OY+c5teZz1lAeW2PAc8IkWW9f6DnEyq1LdK2hv/8YPd3LW0x60YtuEZZPhgz96hfWk2/PvV7UmnPucD8kY9U9fPxbdkc2s45OLJr/qlbgq1bx3Jy/bK9Xg708SvkflCWyxpf9U4LkQwjSzRPZn7CNiFbbYIOWVP/isqYJl5drNfE8RfK8uLcO7vZ47w0NbMge2nCwfLFyzqc+g3LzSN1AFrYyOO1eeysxBJ3j30cRnvcoY2yssqEknH5PS4KA93+sx87Fcb1zj58vaKPUc5GOZGneIb3denRa+Ylg73wc1F+1KObhf/gD+4rp3Gh4Cf1oa/73AutGki5XTfSB1WWPXimrWzTcYG5Z4wK7fKj/Y7K1a9fbTH8f4Cv+Ht3wSRMFxRN0uyz/ESHnEvhv1WpfcJV8ZzHxDWK1eYruv99QxN/gO3oc3A6HksFReVaoXHne5K1LTy//8dQ/0gD7xWf+ONErA4USLGwaQnrlrE6MqU5eLfVJR0647FwOadIQrPvDqfdHlMyUFjrvRD29Su4RqfvcrfMzd67+Ac4d49+f+3fMnYyWp8qzlJ5rZx3hg+8jMahL9TJWUouCkA/AZL6WNUaha07/wNaOfbN24LLHt21MblgAhf9ZewYkHWZt9kO5Ht/r52CzL0qRV8T2oWR94yCs4fi0mdkDPPe0OjWl/tlfqqtQo/nAchdpX1V9HvDdeZvmTGmIHrC2o5XEe5BseWnJ1KhEl++oN/H354c1oBu1uVH86nueVv55X7/1hDfwQD5n1vOv+2VM9zJZVQStLLLAVncGYDDoP8B2GZJaS4gPyY7O6Wx0X96dYum4rY2Yl4PBQHc7NHwccG9ss+cx8nVc0rBW9TUkatYOMWmzPyeW9ycsYNn4RL329kAVrNvvwhlPu8WPsPXe6T8SJVdeSWKkVtqiS9legITAvhLAl+pmqy0u7n5DfJbptQ/5U5/KMUanZ2E+TPbDFVjTNunvVKzaOLS/PfwZn/SIPn8f/Lb9aU5ZOF/iBeKGEwNap5GOd7Y7UdJ/BtnFZ8QO0K0qTTl5ZLW7WVpVqfgyv2s0qttIRO7RHbFJMsnaLVaSUFA8E097wquEFL/le/J7Y7zCvrBYX1oW8PD/0RUpKKct+Zh0fdL7gy50r1Htoe04ul/93PLNWbuSj3x3LwY1qln2n8mp3llcG83Lit06TnfztrWkMHZ9/vNN61avw2i970bLnNb59HnU7pFfzzyPJlbprHkIIwPshhG9DCOuiy9aEEHbjBxD/xxSssG1a6VOX67cu/T7gXalVavg4mWQWG7+2f6zCFgW2jcv8ut63wO+n+Wyq8gaN/bt511DNpvnBtaAaDf3YP6XNjtpVBx2/80D+inb09X50+JqNir++47m+R1jRYkG7TgufKSbQ5RIfz3fFB3se1sArt1d+lH/Yigq0PSeXuas2lX3DCpKdm0dWDZWhNwAAIABJREFUTuHOmz+8+j2nPfQ5G7bt/OPk2bl5XDVkAmNnr/Ixl6V8j7dm5ZKbt+u/KvPwp3OYuWIj6SkpPDhydrnvt2z9VlZvKuO3UqvX98pztfo+ZjAyccFP3P3hjN1q764KIfDdonW8/u1i1m8pxw/AV6DPZ6/mtIfG8p8Rs1i3JavE223NyuXhT2cze8XOP+E1dvYqho5fxOVHHcjYm/rw1q99rOClg79m1cbtcOwfoN+dcNLtZKXVYMri9azcsI1QzDH+pixez0O7sAwkQnnGsH1rZoeHEMYnvDX7kpRUnxK8fWP+EePrlSOwgYeVpK+wLQIs/wjzsS7RNdHvyzU/Yte76sz8wJhZpfy+YNEDp+4LqteH6pUQyMoSm3iQTIPhK9tBx1dKuIq3vLzAr174lk9nruTeczpxTreyK1O5eYHb3p7Gj6s3079TE05u14Ta1fbgcB8FrN60nfMeH0eDGlUZevURpKQYU5es5/VJfpjN61/5jicv6V6o0vbZrFWMmL6CxWu38MFvGxAbWj1j+QYa18qgTjWfOLBxWzY/e/BzGtasyotX9SQjPZXcvMBNwyczZ9Um+hzSkF6tG7B8wzamLVlPWqpxVtdmbM3K5dHRczm7azMa167Ko6Pnct2KjRzcqCYhBFZu3E5WTh4hQLO6mTvatml7Dmc8/AV5AYZe3ZOD9itclXt/yjJuf/cH7jy7I8f1f8B3dqO2/7Q5i2ue/5bVm7azf51MLj6iRanvW05uHmmp+evZOSs3cef70zmhbSPO7tqMKml+XV5eKPTebc3K5cWvFzB0/CLmrPTQXiUthVPbN+aCHs3p2bIeiRqqPnXJekZOX8k1x7UiI917NkIIrNq4nbrVq5CemsLzXy3gtrenUbdaOv8ZMZsnP5vHxUe0YNBxralXPX9CyPzVmxn0wkRmLN/I0PGLePfao3d87pu253Dza1No1bA6fzz5UDLSUzmgXjWeGdidC5/6mkue+Zo+h+5HesrxzFm1ic/e/YRN2/13RutWS6dbi7pccHhzjjm4AY+NnsvDn86hQY2qXHJkix3PUdHKE9h6Aheb2XxgMz4JIYQQyjEw6X9c7Afgf4qO2Fy/Vem3j6nZZC8IbAu9nVWq+x5irMK247Xu5oSJeM2+lD0XGxek7tBirdq4ndQUK7QB2V0/bc7ivclL2bQ9l0HHtdqljWUIgc9mr+aTH5ZzXd827FerlDE/kQdGzmbkjJW0qF+Nm4Z/T5W0FE7v1LTE2+fmBW589Xten7SExrUy+ONrU/jzm1M5rEkt2u9fm9YNa1Cjaio1qqZzdJsG1M4sf5DbkpXDlc+OZ/6azcxbvZmh4xdxYc/mPDByNrUy0rjmuNbc+9FM/jNiFteflH/Ijje/WwrAjOUbGT1rFX0O2Y9ZKzbS/6HPadmgOsN/2YtaGenc8f50Fq3dwsKftnDL61P493md+MtbU3nt28Uc0qgmD4yczX9GeOWkSmoKeSHwyKi51KiaRv3qVfjraW3JC4Fnv5jPgyNnc+vPDuOPr03hswLj2voeuh9PXdqd1BTj4U/nsHLjdupWS+eCJ79m6NVHcNB+NcjLC/zrk5k8MsrXkf949weO+u0xpEW9CSEE/vLmVNZvzaJtk1rc8+EMTm7fmAY1qjJ1yXpenbCIRrUzOLB+dWav2MQHU5cxZ+UmftXnIK7rexDz12zmgie/Zt2WLEbOWMnDn86hZ8t6TF26njkrN9GxWR3O7NyU1NQUHho5m5Ubt9OtRV3uPKsDBzeqyZuTlvDmd0t487ultGxQnYt6NueKo1oWCnpL122lSe2MHctnbl7gne+X8v3idcxesYlmdTP5+xntdwTFL+as5oOpyzirazO6HFCHoeMX8be3ppGVm8fUpet59CL/xZ+/vDmVoeMXkZ5q7F8nk/lrttD30P14cEAXlqzdymOj5/D02Hm8+NUCLjvqQOpWq8Ky9dsYNmERKWb86dRDufejmVw/7HuevrQ723Py+OtbU1m6fivDBx25IxgCdGlel0cv6sqNwyfzzNgfycrNo1GtqvTv1IQjWzfgp03bmbliIyOnr2TE9AlkpKewLTuPMzs35f9Obx+3nZTdUZ7AluSjTpNYRi0/WO6auZCS5tOTy6NmE/+pmmS2flH+8bRqNCpQYZvrg+Rrlrzyl73EAT390A6lHeU+iRStIiTSK+MX8pc3p5EbAkcf1ICzuzWjf8cmpQatrVm5DBk3n1PaN6ZFfZ+JuHLjNv765jRGTF9BTtQF1qNlPbq1KP4I8Vuycpi7cjM/rtnM2s1Z/LQ5iw+nLmdm1B20dks2j1zYtdS2j/hhBQ+MnM3ZXZtx+5ntGfjfb/j9K9/xwrgFLF67hZy8wDvXHk2jKPjl5QVuHO5h7foTD+bavgcxZcl6Ppi6nO8XreOd75eycdv/t3ff4VXW9//Hn++TvUMgrLD3lo2gILhFK7iqdVfrtmqX1dbW1tpvf2ptbbWuqrV1b6Vq3QIisvfeG5KwssjO5/fHfRISMgiQ5NzA63Fd50pyn5HPfU6S88pnvUsqHj8xOpxbxnbj2lGdiImse25oSWkZt70yj8Vbs3j6yiE8P209D32ygrRmMXy+LJ2fnN6DW8d2ZeOuPP7+1RoGdWzGuJ4tyS0s4fNlO7h0aHumrs7k6clrGdM9lbvfXkRMRBjrMvO47ZV5/PCkTrw2azM3ndKF+MhwHv18FZt372POxj3cfEpX7jmnF7tyC5m3aS9pyTF0bxXP3n3FvDNvC/9bsoOfndGj4g362pM68eTktUxZlUlJqeNnZ/SgdVI063bm8dTktTz2xSouGtyOF6at56LB7bhlbBcue3YGFz89ndaJ0WTmFLIrr4jLhrXnpG4t+PFr83l77hYuG+69L0xauI2PFm/n7rN7cmaf1pzzt6n838fLGduzJb94ayHOQVGpN2RsBkM7NvNCzZermbY6k027vTKIn9w1ms178nniqzVMXb2T/mmJjOmeyvS1u/jdf72qCkM7NuMfVwxmWKf9lVGGdGzGr8b35uPF23lt1iYe/Gg5hSVl3DbO++f72alr+b+PV3D1yI789rw+OOBnby5k0sJtREcE6NwinmlrdlJQXMpfvj+QORv3cN2LsyksKePlGZtIS45h6958RndvwYldmlcErJyCYiavzOSqEzsSHx3O6vRcJg5K48endicsYPRsncBjlw3itnHd+MvnqyoCb3REgIHtk3nk4hNonxJLVHgY909ayo0vzWXepj3szivilrFdGdKxUvWXoHG9WjLnPm8hVPnw54G/u8WlZXyxLJ3PlqVzZp9WnNO/TZ0/y03BahqrrXYjs5OB7s65f5lZKhDvnFt/sPv5wdChQ92cOXNC882fO8NbQh2T7G2ce8e8+t3v8996u6Tfl+GPJfWlJTDvRW/X9wlPeMOgj/X33tAves7bhqQoz9u489XLvL3Sbv0u1K2WBrQmI4eSMkev1omhbkqNduUWctFT0xnSMYWHLx5A2AHBbdb63fzm/SX86aL+DO5Qe7mc7IJi7n1nMWf1a11jj1N+USkPfLiM12ZtYnT3FvRPS+KDBdvYujefa0d14v7v9akxtBUUl3LDf+bwzeqdxEeF8+DEfrRPieHWV+aRlV/MNSM7cVa/1lzz/CzGBXsWwOvB+HpFBlNXZ/Ltmp2s25lXrYRm7zaJXH9yZzYFQ83L14/g5O4tqrUht7CEZ6es5dlv1tG9ZQJvBXsevKGjRaRnF9A2OYYPFmzjrtO7c9fp3mrst+du4edvLaxyrLKyMkd2QTH7ikrZtjefJyev5asVGcRHhdO7TULFz8zO3EIiwgLcdXp3uqTGk1tYwu2vzmPyykz+eEE/rhjRkdXpOZzzt28wg5iIMKbdcyqJ0REUlpRy7t+nUVhSyuc/OYWPFm3nZ28t5O2bR7JwSxZ/+HAZFw5O4915W3ns0oEUlpTyy3cWExYwOreI48Mfn0xUeIA7Xl/Afxdu46LB7fjzJQMOqSdzT14Rp/1lCl1T4/jzJSdUhG7nHPe8s5g35myme8t4tu3N5+ufj6VlYjSr03N46JMVmBnN4yI5sUtzJgz0fq4uemo6W/fmM/nn4/hieTq/encx3VvF89bNowgLGI98uqIinAzvlMKTVw4mOiKMDTvzaJkQVdGT+sGCrdz33hKiI8N47QavN682q9JzyMovZmjHZnWeu3OOO19fwIeLtvHKj070Fl+8OJtOzeNYvzOP03p5c1q/XJHB3Wf35OYxXQkEjH98vYZHPl3JhYPS+HxZOi0To3jxh8OZujqT/y7cxsguLbj91G6EBYwnJ6/h4U9WEhYw/jChH5ePqF+HRkZ2AVHhYSTGhFc5h/I2T1q4jXE9U7l1XLcqgfRoYmZznXPV5v8cNLCZ2f3AUKCnc66HmbUF3nLO1XOnv9AKaWB7+SKvrl9pibfR5hVv1e9+M56GT34Jv1hbfaPUprZtAbx/K2QsBcxbyXjxC/BgS6+kzen3w7s3enUn71oMTwzz9gY7lFqIEjKz1u+mU4tYWibUPow2e8NurnlhFs7Byz8aXuN/rHV5deYm/vDhMs4Jzo8Z1qnuNwuA9OwCAmakJni7uZeVOZ6cvIZ1mXk8dPEAIirN23HOcd2Ls5m6eielZY4LB6XxyCUnVIS2nIJizn7sG7buzad5XCTv3XoSHZrHUlhSyvQ1uxjVrTlR4V5P0D3vLKpYUXbp0Pbcf34fcgtLWJORy38XbufDhdvIKSzhlrFd+fmZPQkLGGVljj/9bzn//GY9l4/owIMT+lXp6SsqKePml+fy1YoM7j2nF18sT2f2hj0EDNqnxPL0lUPo3cYLNQ/8dxn/+W4D395zKq0So/ndpKW8OH0DMRFhjOiSwqD2zejRKp4uqfE0j48kKSai4rkoKC7lrMemEhYwPrlzDAGD+Zv3smJ7Nqszcvl48XZ25hZx3oA2/Pa8PrUOnV7zwiyWb8/m23tOJcyMMx+bSkRYgI/vOLneAWf2ht1MWrCN5duzWbkjh0DAaBEfSUZ2IUWlZfz41G78b8kOVuzI4YEJfblixP65Wg9/soInJ6/lp2f04I7T9tconb52J5f/cyZ3nNad+Zv2sH5nHt/cPY59RaWM+n9fkZVfzNieqfzr2mGYGQ9/soLnp63nzZtGckL75Irn6JvVOxnbM7XKz1B9FRSXEhUeqPY8FBSXcukz37FwSxa/PLsXt4w9+HzlWet38/1nvqNLahzrMvM4oX0y/7h8EO2axQLePwdXPDeDvm2T+M15fSqGGWuyM7eQgDXM8Hy53MISzn9iGtn5JRSWlNK+WSxv3zKSd+Zu4f5JS3HAHyb0qzLPzjnHr99fwqszvR61t28ZSZuk2vd9e2vOZtKaxTCqa8O8z5WUlrEju6DiOTxa1RbY6jMkegEwCJgH4JzbFtyLTQ4mKhF2r/fqONZUE682lbf2CHVg++w+b37a91/ydrz/7knYMsdbil4xJNrSGxItLfHOtz6VDY5TWfuKycovpkPzI/uD8tKMjcREhNVrsnhNMnMKuX/SEj5evIMB7ZJ479aTCAsYzjn+/uUa9uwr4nsntKG0DH74r1m0ToymzDl++K/ZvHnzyCo9besyc3ljzmYuGdKu2gTrjJwC/vTxclomRvHZsnTenb+VlLhIBrZPpl/bRJJjI4mP8v4M5RaWkJlbyNRVmSzdlk1keICbxnTh6pGduO/9xXy61Jsn2TIxmnvO6VXxPZ6ftp6vV2bywIS+ZO0r5tHPV2Fm/H5CX+KjwvnjR8vZnpXPIxcP4MGPlnPti7O4aUwX/v7lmoohmmevGsqcjbt5ffZmrj+5M9ERAZ6cvJa35m6mfLFedESA8f3bcPnwDgyt9J97IGD8anxvwsMCPDV5LV8uTyfMjJIyR5lzFBaXkVNYwoMTvTe360/uzNNT1rJ+5z5+e16fKnNirh7ZkX9NX88rMzcxsH0SL07fwFUnduS+83pXhMraREeEcf/3+nDdi3O48vmZrM3IZVeet7ouISqcQR2b8dMzejCwfd11Va8e2ZHr/z2Hz5elExEWYE1GLn+7bOAh9UYN65RSY+9GenYBv35vCX/+bBVxkWE8d81QxvWsugL5jtO6061lPOMPGIIa1bUF55/QlqenrKWktIxbx3bDzIiLCufWsV15Zuo6HpzYr6Kdd5/di9tP7UZs5P63ueiIMM7oU8tq7HqoPBfqwOPPXj2U/y7cxlUj614oUG545xTO6NOKKSszufvsntw4ukuVBQQxkWG8e2v9+kVaxB9BebxaxEeF8+QVg5n4j2+JjQzn2auHEBsZzlUjO9GjVQJFpWWM7l51s2cz44Hz+9KzVQKn9mpZZ1gDuGRow5apCw8LHPVhrS716WGb5ZwbbmbznHODzSwO+O5oWXQQ0h62SXfAgle9jUfH/7n+xZ03z4Lnz4DL34IeZzZuG6Hm3aLL/bW/t6XEhc96tSD/PtDbN2rTdG/vo26nw/THvWB301R4Zoy3t1l9a9UdZ+58fT6fL0tn0u0n1zl0UZclW7M4/4lpxEaGM+NXp1UEnvqasW4XN788l32FpZzVrzX/XbiN+87tzY9Gd+G1WZu4911vKKl8W4EuLeJ4/cYTKSwp45Knv6PUOc4/oS2tE6NZvDWLDxdto8xBm6Ro3rv1JFon7e+5+ekbC/hw0XY+/ckYWiVG8cmSHXy3dhcLNu9ldUb1LSUCBkM7pjCuV0tW7sjm/QXbKO+suu/cPqzOyOW1WZv417XDOKVHKp8s3cGdr89nXM+WPHPVEMyMv36+ir99uZrk2AjO6dea12ZtrpivNHPdLq58fibFpY5+aYmc2rMlT3y9hqEdU9i6N5+o8AAf3zma6Igwpq/dyeSVmaQlx9AhJZYhnZqRGF37hGPnHC/P3MT8jXsIC1iVy4jOzTl3QP3mwFz34mwWbdmLc5CaEMX7t51Ua1CoyW2vzOPrlRmc1rsV5/RrzeAOzWiVGFXvwFVa5hjz8Ne0T4mhqKSMzNxCvv7Z2Cph4kg455i8MpP2KTHVAv7BpGcXcNqjU8gtLOGLn46pcv/CktKDhlq/KSguJbuguM4e7lBbsjWLuKhwOrc4zAoQcsiOpIftTTN7Bkg2sxuA6/CKwMvBRCd6YQ2qFjI/mKbcPHftV/DG1fCTJdVLmZSVehUNyut2NusIPc6BlR95X5cvoogP/se6MThv7XBXiB7F5m3aQ7PYyDr/qBWVlPHl8gz2FZVy+6vzDvmNGLw301+/t5i4yHByCkt4Z+4WrhnVCYDNu/fx8eLtrMvMY1uW13P0g+EdSKgUMjKyC7j91XmkxEXy9s1D6JoaT35RCX/+bCVpyTHcP2kpo7u34InLB/Pl8nTmbtzDHaftX3n40vXDufP1BbwycyMFxWXERYZxw5gujO6Wyk0vzeGHL87mrZtHEh8Vzqz1u3l3/lZuH9et4nm5cHA7LhzcruL5yCssIbewBDPvP/q4qPAqQ1VXnNiR577xlvSP7p5KQXEp8zft4SdvLiAlNpJ1O/PokhrHwxfvn4/0kzN6MLZnKk98tYbXZm2mZ6sEfnKGN7Q2oktz/v3D4WTlF3NW39YEAkb3Vgn85I0FlDpXZUXZqK4tDmmoxsy46sSOXHWQrRgO5ppRnbjmhVlEhQd47cYTD/ln5PEfDKLMucMOWGEB44oTO/DwJysB+MOEvg0W1sB7nsb1Orx9/VolRvPgxH7M27SnWtg72sIaeD1zh/r6NrV+af6rqXm8Omhgc8792czOALKBnsBvnXOfN3rLjgVRlX7Q67Npbrn4Jgxs6Uu9fc9yM6oHttx0r0xUUqVC6yNuqhTYgsNx5Zuqlhexru9+c8eA0jLHY1+s4vGv1jC8cwpv3lT7fmoz1+8it7CEq07syEszNvLAh8v4vwtqrrM6e8NuZq3fzc2ndK0ygf7VmRtZuCWLv102kBenb6gYMsspLOGyZ2ewdW8+LeIjSYmL5P8+XsHjX67hypEduXF0FxJjIrjrjQXkFpYEJyd7b3h/mNiPM/4ylVtemUebpGgeu3QgSTERVcJVue6tEvj4ztE458guKCEizCqGnJ68cgjXvTibif/4lhbxkazJyCUtOaZildmBIsMDRIZH0qyOeTcHDq1FR4Tx5BWDueDJ6cRHh/P3HwzinH6tq81HGtShGc9fO4w1GTk0i42s8mY+qlvVEPa9E9rSIj6KnbmFhzw/rzGM7taCS4a0Y3SP1MPaWT8QMAIc2WKlS4e257HPV5MQHd7gw1ZHauKgNCYOSjv4DUWOMQcNbMEh0K+cc5+bWU+gp5lFOOf8tS2yH5UXgA+LrL0IbU3CI73tFJoisOXt9D4W76t+XdYW72PltnceA6m9Yd9Or2wSVO1hi4w/bnbFzy4o5vZX5zN1VSatEqNYsHlvncMyXyxLJzoiwK/G9yY2Moxnpq5j1Y4cuqTG0TU1nr5tk+jYPJYnJ6/ltVle6a9BHZIrenkycgp4+JOVnNytRcUKxjtfX8CU1Zm8P38rO7ILeOeWkRWhY9GWvTwzdR1PT1nLy99tZEinZkxfu4uHLxpA90pBoE1SDL85rzcPfrScJy4fTPN6zIcxs2p7bZ3SI5W/fP8E/vXtBsoc9E9L4vZTux10a4dD1SU1njn3nU54wA46zFffIbeRXZsf/EZNJBAwHrnkhJC2oXl8FH+Y2JeUuCjf9wCJHC/qMyQ6FRhtZs2AT4A5wKXAFY3ZsGNCeXmqZp0OvVZlYpv95anKSmHLbG8bjYbe5mPfLu9jXYEtsdJ/s2Yw8cmqYbI8sOVleIXM/bAVSSNzzvGzNxcyfc1O/nRhf5rHRXLjS3NZtCWrokfouW/WkZFTyK/G98Y5xxfLMzi5WyoxkWH8/CxvA9B5m/bw9cpM3pyzpeKxAwbXndSZ12Zt4qNF2ysC28szNpFbVMLvJ/TFzDinXxv+mLCce99ZzI7sAn56Ro8qPUQD2iXzj8sHs3JHDn/5fCWfLk1n4sC2XDK0+kKFS4d14KLB7Y546GvCwDQmDGz83o/DWeEnh+bSYfXcN1JEmkR9ApsFi75fDzzlnHvYzBY0dsOOCeUF4A9niLBytYOFr8EHt3lbZfT+XsO1D2Dfbu9jTYEt2ysLU62gctoBG3NGJ0Mgwpuvd5zMX3t+2no+X5bOb87rww+Gd2BPcDXerPW7GdYphZLSMp6cvJbdeUUM6diM9s1i2bo3nztO856fiLAA947vXfF4e/cVsXRbNit25DCicwr90pLIyCngkyU7+P35XkB7a85mRndPpWuqt1ghMjzAlSd25C+fr2JIx2bcWstWAj1bJ/DMVUPZvHtflV3KD9SQ85RERKRh1ecvtJnZSLweteDkJdRHXh/lQ6KHsuCgXOXAtmyS9/Hr/4Oystrvczj2lQ+J5le/LmuLN8QZfZBJp4HA/mHQQ5mr18B25xXxwrT1bNiZV+P1e/KK2LKnhmB6iOZu3MP/+98KzurbiutO6gRAs7hIureMZ9Z6LwDP2ejttB0fFc5973tlcMzg1F41bymQHBvJSd1acP3JnSsm+Z43oC278or4bt0upq7KZHtWAZcNqzq0fs3ITlw7qhN/u2zgQQNX+5RYhTIRkaNUff563wXcC7znnFtqZl2Arxu3WceI8h62+tYQrSyhDeRlej1g676GFj0gYxksfbdh21g+JFpUy5BoUrv6DXGWB7YQLjh47ItVPPDhMsb+eTKXPD2dORt2V1znnOPK52dy8kNfc+qfJ/PHj5aRV7i/nE5BcSn3vruI1en7C8/nFZYw+uGvePHb/UU9ikrKuOuN+bRJjubhi0+o0ls1rHMK8zbuobTM8enSHUSGB3jh2mHszivi+WnrGdg+uWIj2PoY2zOVuMgwPlrklYppHhfJ6b2rBr6k2Ah+d37fY3rvIRERqUdgc85Ncc6d75x7yMwCwE7n3B1N0LajX8s+MPxG6HUYw5iJwT2bFrwCpUVw7l+gZV+Y/Cdvg9rKJt0Bb117eG082KKDxHrORyqfxxaiIdGcgmLembuFs/q24pdn92Lz7nzuemMBxcHae9+t28XSbdlcOCiNdimxPDdtPY9/tabi/q/P2sRrszbzUHArA4B3521h8+58/v7VGvYVec/5e/O9Yw9M6Fdt0v3wTinkFJawfHs2ny1NZ0z3FgzvnMJNY7zAfmDYOpjyTT4/WrydL1dkcPGQdnXudi4iIseug/71N7NXzSwxuFp0CbDMzH7R+E07BoRHwvhHIOEwdtZOCAa22c9BbHPoOArG/Qp2rYFFb+y/3arPYN6/Yf03h/49SkugYK/3eW1z2A6cv1abJh4SnbtxD2/O3lxRuPeduVvIKyrl1rHduGVsV/50YX+27Mnn3XneZP5/fbvB2+riwv7857rhTDihLS9OX09GTgEFxaU8NWUtkWEBvliezur0HMrKHC98u4HWidHszivi1ZmbKual9U9LYmyP1GptGtY5peJ7bd2bz5l9ve1Z7jitO784qyeXDz/0SdznDWhLTkEJpWWO7w/z1/YKIiLSdOrz73of51w2MBH4H9AZuKpRWyX7A9ueDdDzHG+Vaa9zIW0IfPwLWDcFCnPho595t9u3E0oK999/c7CMVF3y9+z//MDAVlzgDcnWN7B1PgW6ngaxDbOPlXOOeZv2UFhSWuX4ih3Z/Ojfs7noqenc/c4inp6yjrIyx3++28jA9skVNQPH9kxlQLsknvh6DWszc/lieTpXjOhQsUXBXaf3oLjU8eTXa3lj9mbSswv566UDiY4I8MzUdUxelcH6nXncO74XI7s055mp63hn3hY27trHbeO61ThxPy05hrTkGN6dv4WA7e9Ri44I47Zx3ercb6w2o3u0IDE6nOGdUyoWG4iIyPGnPqtEI8wsAi+wPeGcKzazuutZyZFLqFTGpnxI1QwuexVeugBeuQQ6nQRZm2DQlTD/ZW+RQrOzArhSAAAgAElEQVRO3m1nPw+LXode4/cfO1D5ggOovuigfIVofYdE+1/sXRpAbmEJ9767mP8u3Ebftok8/gOvIPLjX63myclriY0M4xdn9WTFjhwe+mQFO7LyWbczj79eun/vKjPjjlO786P/zOFH/55DeMCqFCnu1CKOS4a049WZm0iMiWB4pxTG92/NrPXteXXWJlbsyKZ1YjTj+7chNSGKy/85k9+8v5QereI5s45ahMM7p/De/K2M6JLSIIWYo8LDePlHI2gW23BFnUVE5OhTnx62Z4ANQBww1cw64lU9kMYU29zbKiMyHrqM3X88oTVc+xG06uOVlRp6HfS90Lsue9v+22Vt9j4uerP271G+4ACqLzoIBrZtNK8yEb+hzNu0h/F/+4Zfvr2oyvE1GblMeGIaHy3axpUndmDr3nzOe3wa5z3+DY9/tYaJA9P45u5x3DauG49cPIBBHZL593cbaREfWa1Y9Gm9W9K3bSLrd+Zxbv82tEqsWq/vx6d55Yp25hZy1+ndMTN+NLoLZQ6WbM3m6lEdiQgLMLJLc4Z2bEZRaRm3jetGIFD7IozyPdjOCg6HNoQB7ZJpn6JFBSIix7P6LDr4u3MuzTk33nk2AuOaoG3Ht0DAm8DfczxEHFAYODYFrp4E5zwCZzywvxescmDbGwxsC1/zirvXJK9yD9sBgS24ae7/Tcvhun/PrpgrdqSKSsp48MNlXPTUdFZn5PDm3M2szfSKgJeWOW5+eS579xXz8o9G8ODE/vzvztH0T0tid14xz141hEe/fwLJwd6m6Igwnr1qKH3bJvLjU7tXqzBgZvz8zJ5Ehgf40ejqK3XTkmO48/TuTBzYtmKn+/YpsXxvQBtiI8Mq5pyZGb87vy/XjurEuf3rLuB9dr/WXDS4HRObYPNYERE5ftRnSBQzOxfoC1RODg80Sotkv2v+CxExNV8XnQgjbvQ+T/TKFFUEttKS4IKB9rB7HWyZA+2HVX+M8h62iNgaApvXwzYlPZKcknzmbdrLkI7NjvCE4D/fbeC5aeu5YkQHbhzThTP/OpVnp6zjoYsH8O68LazJyOWpKwZX7O7fJimG1288kdKymotZpyZE8dEdo2v9fuN6tWTx786stVxUTXUu/3hBf35yRo+KYAheAeT6FEFOiYvk0e+HtqyQiIgce+qzSvRpvFJUPwYMuAToWOedpGHEp0JUPSaaRyd6Q6flgS1nu1e0ffiNEB7j9bLVpLzKQWJa9TlsWZspiW5OTomX6T9YsPUwT2K/sjLHSzM2MrRjM/54QX86No/jkqHteG/+Vjbv3sdjX6xmQLskzu5XdTjRzI5ow9fawlpt4qLC6dg87rC/n4iISEOrz7vgKOfc1cAe59zvgZFAj8ZtlhyyxLb7FwqUz19r2cdbWbr03aorSMvt2+lt7huTXL2HLXsrWZHe5PoT2iXx4aLtFXuaHa6pqzPZuGsfV43cn/dvHN2VkrIyrnhuJlv35nP3Wb0OWtBbRETkeFOfwFbe9bLPzNoCxUDdE3mk6SW23d/DVj5/Lbk9nHCZt33H6s+q32ffLohtTmlYDLk52bw0YyPLtgXXk2RtZbtrTrPYCG4b143deUVMW7Oz+mMcgpdneIsDKvegdWgey7kD2rJp9z5O6tack7u3OKLvISIiciyqT2D70MySgUeAeXgrRl9tzEbJYUhM2197tLyHLakddBkHFoBtC6rcPL+olIwdW1mTF8Xk9bls2LGT37y/hF++E1y1mbWFNYXJ9G+XzNieLUmKieCD+Yc/LLp59z6+XJHBZcM6VBuivH1cN9qnxHDvOb1rubeIiMjxrdbAZmZ3mdlw4E/Oub3OuXfw5q71cs79tslaKPWT0AZydngLDrI2e9uCRMZBWLj3eaU910rLHOc/MY2M9G2kl8TRpkUKnRO94LR4axYbt26HohyW70tkQFoSkeEBxvdvzWfL0itKNJVbm5lLUcnBh0pfnrkRAy4fUX23/56tE/jm7lPrNalfRETkeFTXKtF2wGNALzNbDHwLTA9exG8S23oLDfIyvCHRpEpljOJSq2zhMW3NTlZn5NIpuYC4nj0xC8C65Vw+ogNPfL2G6fMW0BHYWpbC+e28EDVxYBqvzdrMh4u28/2h3mMv3ZbFeY9P49SeLXnmqiGEhwXIyC7g528vorSsjCEdU0iKiWDSwm0s3LyX8f1b0za5llWvIiIiUqtae9iccz93zo0CWgP3AruBHwJLzGxZE7VP6qvyXmxZm735a+Vim3tlpoLembuFpOhw4kr2YnHNIdLb1qNtcgzDOjVjyYoVAGx3KRWlnoZ3TqF3m0SeneqVggJ4duo6wsz4ckUG909ayvasfC59dgZzNuxmT14xT3y1mj98uIzC4lLuO7c3D100oGmeCxERkWNMffZhiwESgaTgZRuwuDEbJYehYi+2rV4PW7cz9l8XlwrbF3pXFxTz6dIdXDGoBba4AGJbQP7uikoH3zuhLbP/uxMiITw+paI6gJlx8ylduPP1BXyxPJ0+bRP5cNF2rh3ViYiwAE9PWcukhdvAwUvXD2dIxxRyCorZnVekLTJERESOUK2BzcyexdssNweYiTcU+hfn3J7a7iMhVN7DtmMxlORX7WGrNCT60aLtFJaUcXHvaC92xzaHkgIoLYSyUs7p14aVH3oLg9u3qbof2rn92/DIpyt5espaBrb3NtG97uTOtEmMJiO7gK9XZvDiD4dX9MolREeQEB3RuOctIiJyHKirh60DEAWsBrYCW4C9TdEoOTTrMnPJLQgwICwKNs0AYNm+JF5+bzF3ndadlnGpUJgFJYW8M3cL3VrG0zup2LtzbHNv2w+A4nxSE+Lp3xzIhm7t21b5PuFhAW4Y3YX7Jy1l8dYsvjegDWnBOWmPfv8EiksdkeGHv8GtiIiI1KyuOWxnA8OAPwcP/QyYbWafmdnvm6JxUj+/fm8Jt702HxLbwNa5ALy7Fl6duYkz/jqV+bu8bTTmLF/NnI17uHhIO6y8ykFci/3lr4Kb5w5INUpcgH4dq2+39/2h7UmJi6S41HHjmK4Vx81MYU1ERKSR1DmHzXkVv5eY2V4gK3g5DxgO3N/4zZODKSwpZd6mPRSWlFHSrQ3hezYAMH1XLMM7p1Ba5nh6TjbPRML9r04hPNCZCwalwfrZ3gOUb/8BFYGtZzNHaVQCo7pV38Q2JjKM357Xh5XpOfRpm9gUpygiInLcq2sO2x3AqOClmP1beryAFh00GuccWfnFVQqP12XRliwKg/ug7QlPJRVwEXEs2xvO3SemctOYrnw3eTdMhd+MSyW+70neQoLywu+xzff3sAUXHoQV5hAW1wwCNZeImjgo7YjOUURERA5NXWNYnYC3gBHOua7Ouaucc0855xY6546sqKTU6rlv1nPin74kM6dq7c+9+4pqvP2s9bsrPt9a5i0EyI9tCxh92iQSFjBOHtALgBNbuf2b0+btBAuD6CSIiPWOlReAL8z2aoyKiIiIL9Q1h+2nzrl3nHPbm7JBx7OcgmL+MXkNBcVlTFm1f9+0L5alM+TBL1i8JavafWas20XPVgm0SYpmbYEXsnaFtwTYP2QZFxzarLQXW3kdUcwqBbZgAfiCLC/IiYiIiC9olriPvPjtBvbuKyY2MozJKzMqjn+wcBulZY4Xvl1f5fYlpWXM3biHEV1S6Ns2kcU53ly0zWUtaBEfRcsEbw81opMgEFE9sJUHuWqBTT1sIiIifqLA5hNZ+cX885t1nN67Fef0a8M3q3dSWuYoKC7lq+XpRIYF+HDRNjJyCirus2RbNvuKShneOYU+bZNYkOUFr5UFyVUXBJhVK09V0cMG1VaJUpgN0QpsIiIifqHA5hMvTFtPdkEJPzmjO2N7ppKVX8yCzXuZvnYneUWl/Gp8L4pLHa/O3FRxn1nrvYUDwzun0K9tImvK2pKf1I2Pc7rQp80BgSuuagH4KoEtMtjDVlSph01DoiIiIr6hwOYD8zbt4Z/frOPsvq3p2zaJ0d1bEDCYsjKDT5bsICEqnMtHdGRcz1RenrGJouCq0JnrdtOlRRwtE6Lpm5ZELrH8odOLzC7pXn3LjbjU/UOizkFuRqUetkpDomVlWnQgIiLiMwpsIbZkaxbXvDCL1IQofj+hLwDJsZEM6tCML1dk8PmydE7r3ZLI8ADXntSZnbmFvDRjI9uz8pm9YTfDO6cA0DYpmuTYCP67cBtADT1slQJbbgYU7IUW3b2vK68SLcoFnIZERUREfESBLYRWpedw5fMzSYyO4NUbTqwotA4wtkcqS7dls2dfMWf382p6ju7Wgh6t4vnDh8sY+aevyC4oYUQXL7CZGX3bJpJTUEJ0RIDOLQ4ouB7bAvKCe69lLPU+tuzjfaw8h60guBJVPWwiIiK+UWelA2k8OQXF3PifOUSEBXj1hhEVNTnLndIzlUc/X0VUeIAxPVIBCASMN24cyawNu9mZW0h+kVesvVzftkl8u2YXvVp7+69VEdcCivOgKA/Sl3nHWnk9egTCICzKC2yF2d4x9bCJiIj4hgJbCDjnuOedxWzek89rN5xIx+Zx1W7Tr20SrRKjGNyhGbGR+1+mZnGRnNW3dY2P2zc4b63GklFxXugjbydkLIO4lvu39QBv4UHRPm/BAWjRgYiIiI8osIXASzM28tHi7dx9ds+KOWgHCgSMt28eRUJ0/V+iAe2SvY9pNYSt8sC2byekL4VWfapeHxHrzWEr72GLUmATERHxCwW2JrZ59z4e/HA543qmcvOYrnXetn1K7CE9ducWcbx76yj61xjYgr1pOemQuQKGXl/1+ojY4Bw2DYmKiIj4jQJbE/vL56swgz9dOIBALcXVj8TgDs1qvqI8sG2ZBSUFNfSwxQTnsGnRgYiIiN9olWgTWr49m/cXbOWHJ3WmdVL0we/QkMqHRNdP9T62rGlItNIqUfWwiYiI+IYCWyOYu3E336zOrHb8kU9XkhAVzi2n1D0U2igi4yA8BrbNBwxSex1wfaVFB4EICG/iQCkiIiK1UmBrBI98upLbXpnHvqKSimOz1u/mqxUZ3DK2G0mxEaFpWFwquDJI6bK/HFW5yosOopO8+qMiIiLiCwpsjWB7VgHZBSW8N38r4G3j8dAnK2iVGMW1ozqFrmHl89gOnL8GVRcdaDhURETEVxTYGphzju1ZBQD8e/oGnHN8uTyDuRv3cOdpPYiJDAtd48rnsbXsW/26ikUHqiMqIiLiNwpsDWzPvmKKSsrol5bIqvRcpq/dxcOfrqBzizguGdoutI07aA9bvnrYREREfEiBrYFtz8oH4IbRXWgWG8FdbyxgVXouPzuzBxFhIX66ywNbTT1skbFe2aqCLPWwiYiI+IwCWwPbERwO7dQ8jsuGdyAzp5D+aUmMr1TzM2S6jIXuZ0JK5+rXRcSAK/UqIUQnN3XLREREpA7aOLeBbQsGtjZJ0Vw9siOfL0vnN+f1aZRNcg9Z11O9S00igqtG83ZqSFRERMRnFNga2I6sfMIDRvP4KMICxhc/PSXUTaqf8sCG05CoiIiIz2hItIFtzyqgVWI0YX7oUTsUEZX2ZVMPm4iIiK8osDWwHVkFTV92qiFU3khXPWwiIiK+osDWwI7awBYRs//z6KTQtUNERESqUWBrQOWb5rZJPBoDm4ZERURE/EqBrQFl55eQX1x6lPawaUhURETErxTYGtD2bG/T3DZJMQe5pQ9V6WHTkKiIiIifKLA1oPIaokdlD5sWHYiIiPiWAlsD2lFp09yjjuawiYiI+JYCWwPanlVAwCA1ISrUTTl05atEw6Mh/Chsv4iIyDFMga0B7cjKJzUhKvRF3g9HeDRgGg4VERHxoaMwWfjX9qwCWh+NCw4AzLxhUQ2HioiI+I4CWwM6avdgKxcRox42ERERH1Jga0BHbZWDcpGx2tJDRETEhxTYGkhOQTG5hSVH5wrRcolpkNwh1K0QERGRA4SHugHHih1H8x5s5X7wOoRFhLoVIiIicgAFtgZSsWnu0TyHLSY51C0QERGRGmhItIFk5BQCR3kPm4iIiPiSAlsDycjxethaJiiwiYiISMNSYGsgGdmFJESFExMZFuqmiIiIyDFGga2BZOQU0DJRJZ1ERESk4SmwNZD07EINh4qIiEij8F1gM7PfmdlWM1sQvIyvdN29ZrbGzFaa2VmhbOeB1MMmIiIijcWv23r81Tn358oHzKwPcBnQF2gLfGFmPZxzpaFoYGXOOTKyC2l1NG/pISIiIr7lux62OkwAXnfOFTrn1gNrgOEhbhMA2fklFJaU0TJBPWwiIiLS8Pwa2G43s0Vm9oKZNQseSwM2V7rNluCxaszsRjObY2ZzMjMzG7utFVt6pCqwiYiISCMISWAzsy/MbEkNlwnAU0BXYCCwHXj0UB/fOfesc26oc25oampqA7e+uvJNczUkKiIiIo0hJHPYnHOn1+d2ZvZP4MPgl1uB9pWubhc8FnLp2eWb5qqHTURERBqe74ZEzaxNpS8vAJYEP58EXGZmUWbWGegOzGrq9tWkvIetpXrYREREpBH4cZXow2Y2EHDABuAmAOfcUjN7E1gGlAC3+WGFKHhVDuIiw4iP8uPTKSIiIkc73yUM59xVdVz3R+CPTdiceknPKVDvmoiIiDQa3w2JHo0ysws1f01EREQajQJbA8hQD5uIiIg0IgW2I+ScC9YRVQ+biIiINA4FtiOUW1hCfnEprVRHVERERBqJAtsRqtjSI0FDoiIiItI4FNiOkDbNFRERkcamwHaEMrVproiIiDQyBbYjlJFdHtjUwyYiIiKNQ4HtCKVnFxAdESBBVQ5ERESkkSiwHaGMnEJaJUZjZqFuioiIiByjFNiOUEZOgRYciIiISKNSYDtC+4pKtaWHiIiINCpNvDpCk24/mdIyF+pmiIiIyDFMPWwNICyg+WsiIiLSeBTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHxOgU1ERETE5xTYRERERHwuJIHNzC4xs6VmVmZmQw+47l4zW2NmK83srErHzw4eW2Nm9zR9q0VERERCI1Q9bEuAC4GplQ+aWR/gMqAvcDbwpJmFmVkY8A/gHKAP8IPgbUVERESOeeGh+KbOueUAZnbgVROA151zhcB6M1sDDA9et8Y5ty54v9eDt13WNC0WERERCR2/zWFLAzZX+npL8Fhtx2tkZjea2Rwzm5OZmdkoDRURERFpKo3Ww2ZmXwCta7jq1865Dxrr+wI4554FngUYOnSoa8zvJSIiItLYGi2wOedOP4y7bQXaV/q6XfAYdRwXEREROab5bUh0EnCZmUWZWWegOzALmA10N7POZhaJtzBhUgjbKSIiItJkQrLowMwuAB4HUoGPzGyBc+4s59xSM3sTbzFBCXCbc640eJ/bgU+BMOAF59zSULRdREREpKmZc8f2FK+hQ4e6OXPmhLoZIiIiIgdlZnOdc0MPPO63IVEREREROYACm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPKbCJiIiI+JwCm4iIiIjPhSSwmdklZrbUzMrMbGil453MLN/MFgQvT1e6boiZLTazNWb2dzOzULRdREREpKmFqodtCXAhMLWG69Y65wYGLzdXOv4UcAPQPXg5u/GbKSIiIhJ6IQlszrnlzrmV9b29mbUBEp1zM5xzDvgPMLHRGigiIiLiI+GhbkANOpvZfCAbuM859w2QBmypdJstwWM1MrMbgRuDX+aaWb3D4SFoAexshMc9Wuj8df7H6/kfz+cOOv/j+fyP53OHpjv/jjUdbLTAZmZfAK1ruOrXzrkParnbdqCDc26XmQ0B3jezvof6vZ1zzwLPHur9DoWZzXHODT34LY9NOn+d//F6/sfzuYPO/3g+/+P53CH0599ogc05d/ph3KcQKAx+PtfM1gI9gK1Au0o3bRc8JiIiInLM89W2HmaWamZhwc+74C0uWOec2w5km9mJwdWhVwO19dKJiIiIHFNCta3HBWa2BRgJfGRmnwavGgMsMrMFwNvAzc653cHrbgWeA9YAa4H/NXGzD9SoQ65HAZ3/8e14Pv/j+dxB5388n//xfO4Q4vM3b9GliIiIiPiVr4ZERURERKQ6BTYRERERn1NgOwxmdraZrQyWybon1O1pTGbW3sy+NrNlwXJidwaP/87MtlYqIzY+1G1tLGa2IVgWbYGZzQkeSzGzz81sdfBjs1C3szGYWc9Kr/ECM8s2s7uO5dffzF4wswwzW1LpWI2vt3n+HvxbsMjMBoeu5Q2jlvN/xMxWBM/xPTNLDh6vtZzg0aiWc6/1Z93M7g2+9ivN7KzQtLrh1HL+b1Q69w3BOebH3GsPdb7f+eP33zmnyyFcgDC8RQ9dgEhgIdAn1O1qxPNtAwwOfp4ArAL6AL8Dfh7q9jXRc7ABaHHAsYeBe4Kf3wM8FOp2NsHzEAbswNvU8Zh9/fEWPw0Glhzs9QbG4y2AMuBEYGao299I538mEB78/KFK59+p8u2O9kst517jz3rw7+BCIAroHHxfCAv1OTT0+R9w/aPAb4/F1z54TrW93/ni9189bIduOLDGObfOOVcEvA5MCHGbGo1zbrtzbl7w8xxgOXVUmTiOTAD+Hfz83xwfpdJOw6v1uzHUDWlMzrmpwO4DDtf2ek8A/uM8M4Bk80rpHbVqOn/n3GfOuZLglzOoui/mMaOW1742E4DXnXOFzrn1eDsYDG+0xjWBus4/uKXW94HXmrRRTaiO9ztf/P4rsB26NGBzpa/rLJN1LDGzTsAgYGbw0O3BbuAXjtUhwSAHfGZmc80rewbQynn7A4LX69QqNE1rUpdR9Y/18fL6Q+2v9/H49+A6qm6r1NnM5pvZFDMbHapGNbKaftaPt9d+NJDunFtd6dgx+9of8H7ni99/BTapFzOLB94B7nLOZQNPAV2BgXglxR4NYfMa28nOucHAOcBtZjam8pXO6xs/pvfHMbNI4HzgreCh4+n1r+J4eL1rY2a/BkqAV4KHyssJDgJ+CrxqZomhal8jOW5/1g/wA6r+w3bMvvY1vN9VCOXvvwLbodsKtK/09TFfJsvMIvB+eF9xzr0L4JxLd86VOufKgH9ylA8F1MU5tzX4MQN4D+9c08u7voMfM0LXwiZxDjDPOZcOx9frH1Tb633c/D0ws2uB84Argm9aBIcDdwU/n4s3j6tHyBrZCOr4WT+eXvtw4ELgjfJjx+prX9P7HT75/VdgO3Szge5m1jnY63AZMCnEbWo0wXkLzwPLnXN/qXS88jj9BcCSA+97LDCzODNLKP8cb/L1ErzX/Jrgza7h2C+VVuW/6+Pl9a+kttd7EnB1cLXYiUBWpaGTY4aZnQ3cDZzvnNtX6XiN5QRD08rGUcfP+iTgMjOLMrPOeOc+q6nb10ROB1Y457aUHzgWX/va3u/wy+9/qFdlHI0XvJUhq/D+o/h1qNvTyOd6Ml737yJgQfAyHngJWBw8PgloE+q2NtL5d8FbCbYQWFr+egPNgS+B1cAXQEqo29qIz0EcsAtIqnTsmH398YLpdqAYb07K9bW93nirw/4R/FuwGBga6vY30vmvwZurU/434OngbS8K/l4sAOYB3wt1+xvh3PqAUnQAAAQySURBVGv9WQd+HXztVwLnhLr9jXH+weMv4pWKrHzbY+q1D55Tbe93vvj9V2kqEREREZ/TkKiIiIiIzymwiYiIiPicApuIiIiIzymwiYiIiPicApuIiIiIzymwiUjImZkzs0crff1zM/tdAz32i2Z2cUM81kG+zyVmttzMvj7geCczyzezBZUuVzfg9x1rZh821OOJiD+Fh7oBIiJAIXChmf3JObcz1I0pZ2bhbn/R84O5HrjBOTethuvWOucGNmDTROQ4ox42EfGDEuBZ4CcHXnFgD5mZ5QY/jg0Wnf7AzNaZ2f8zsyvMbJaZLTazrpUe5nQzm2Nmq8zsvOD9w8zsETObHSzsfVOlx/3GzCYBy2pozw+Cj7/EzB4KHvst3qabz5vZI/U9aTPLNbO/mtlSM/vSzFKDxwea2Yxgu94rLzhuZt3M7AszW2hm8yqdY7yZvW1mK8zsleCO7QSfk2XBx/lzfdslIv6jwCYifvEP4AozSzqE+5wA3Az0Bq4CejjnhgPPAT+udLtOeDUgzwWeNrNovB6xLOfcMGAYcEOwxBDAYOBO51yV2ohm1hZ4CDgVrxj4MDOb6Jx7AJiDV2fzFzW0s+sBQ6Kjg8fjgDnOub7AFOD+4PH/AL90zg3A20G9/PgrwD+ccycAo/B2pQcYBNwF9MGrznGSmTXHK6XUN/g4Dx7syRQR/1JgExFfcM5l4wWVOw7hbrOdc9udc4V45WE+Cx5fjBfSyr3pnCtzzq3Gq3fYC68u7NVmtgCYiVd+pnvw9rOcc+tr+H7DgMnOuczgUOkrwJh6tHOtc25gpcs3weNl7C+o/TJwcjCwJjvnpgSP/xsYE6xpm+acew/AOVfg9tf1nOWc2+K8AuULgueeBRTg9fpdCFTUABWRo48Cm4j4yWN4PV9xlY6VEPxbZWYBILLSdYWVPi+r9HUZVefoHliDz+HVAfxxpRDV2TlXHvjyjugsDt/h1gqs/DyUAuVz74YDbwPnAZ8cYdtEJIQU2ETEN5xzu4E38UJbuQ3AkODn5wMRh/HQl5hZIDjnqwtese5PgVvMLALAzHqYWVxdDwLMAk4xsxZmFgb8AG8o83AFgPL5eZcD05xzWcCeSsOmVwFTnHM5wBYzmxhsb5SZxdb2wGYWDyQ55z7Gmxt4whG0U0RCTKtERcRvHgVur/T1P4EPzGwhXi/R4fR+bcILW4nAzc65AjN7Dm/ocF5wkn4mMLGuB3HObTeze4Cv8XroPnLOfVCP7981OPRa7gXn3N/xzmW4md0HZACXBq+/Bm+uXSzeEO4Pg8evAp4xsweAYuCSOr5nAt7zFh1s60/r0U4R8Slz7nB74EVE5EiYWa5zLj7U7RAR/9OQqIiIiIjPqYdNRERExOfUwyYiIiLicwpsIiIiIj6nwCYiIiLicwpsIiIiIj6nwCYiIiLic/8fPNc3IN8O1K4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pMu1N5_mhAgv",
        "outputId": "ff23e9f8-1d2a-4d8e-bc01-4ca53addc7f9"
      },
      "source": [
        "tttttttttttt = np.load('/content/drive/MyDrive/Thesis data/3rdGAN_trial2/Generated_ep200.npy') # just testing\r\n",
        "tttttttttttt.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(880, 40, 99)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_EEaRXPm9Kb1"
      },
      "source": [
        "##  Try some testing here (test quality of generated data)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vyUAE5Jv9ZEq"
      },
      "source": [
        "# traindata\r\n",
        "traindata = np.load('/content/drive/MyDrive/Thesis data/traindata22.npy') # training data  for baseline and also the data for appending to the model\r\n",
        "\r\n",
        "testdata = np.load('/content/drive/MyDrive/Thesis data/testdata10.npy') # testdata for baseline and all of the augmented models\r\n",
        "\r\n",
        "# this is the training data labels\r\n",
        "val22 = np.load('/content/drive/MyDrive/Thesis data/val22.npy') #22 subjects of data = 880 experiments\r\n",
        "aro22 = np.load('/content/drive/MyDrive/Thesis data/aro22.npy') #22 subjects of data = 880 experiments\r\n",
        "dom22 = np.load('/content/drive/MyDrive/Thesis data/dom22.npy') #22 subjects of data = 880 experiments\r\n",
        "lik22 = np.load('/content/drive/MyDrive/Thesis data/lik22.npy') #22 subjects of data = 880 experiments\r\n",
        "\r\n",
        "# this is the test data labels\r\n",
        "val10 = np.load('/content/drive/MyDrive/Thesis data/val10.npy') #10 subjects of data = 400 experiments\r\n",
        "aro10 = np.load('/content/drive/MyDrive/Thesis data/aro10.npy') #10 subjects of data = 400 experiments\r\n",
        "dom10 = np.load('/content/drive/MyDrive/Thesis data/dom10.npy') #10 subjects of data = 400 experiments\r\n",
        "lik10 = np.load('/content/drive/MyDrive/Thesis data/lik10.npy') #10 subjects of data = 400 experiments\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3KuQpsV69IVH"
      },
      "source": [
        "# # Define KNN based model for actual/original dataset\r\n",
        "def knnmodel(xtrain,ytrain,xtest,ytest,kval):\r\n",
        "  random_state=100\r\n",
        "  # print(\"xtrain.shape,ytrain.shape\",xtrain.shape,ytrain.shape)\r\n",
        "  # print(\"xtest.shape,ytest.shape\",xtrain.shape,ytrain.shape)\r\n",
        "  # Define the KNN model here\r\n",
        "\r\n",
        "  model = KNeighborsClassifier(n_neighbors=kval) \r\n",
        "  model.fit(xtrain, ytrain)\r\n",
        "  y_pred = model.predict(xtest)\r\n",
        "\r\n",
        "  # print(\"Accuracy score for fold\",fold_no)\r\n",
        "  acc = accuracy_score(ytest, y_pred)*100\r\n",
        "  acc = round(acc, 4)\r\n",
        "  print(\"Accuracy\",acc)\r\n",
        "  # print(classification_report(ytest, y_pred))\r\n",
        "  # print(confusion_matrix(ytest,y_pred))\r\n",
        "  # return acc,xtrain, xtest, ytrain, ytest\r\n",
        "  return acc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pK376gUtuhTO"
      },
      "source": [
        "# Exhaustively test the accuracy scores on all epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JplsADf29j5J",
        "outputId": "9a57762d-2e62-4906-9a29-d1b34fd464df"
      },
      "source": [
        "#KNN model\r\n",
        "for ep in range (200):\r\n",
        "  stringval = \"/content/drive/MyDrive/Thesis data/3rdGAN_trial2/Generated_ep\"+str(ep+1)+\".npy\"\r\n",
        "  # print(stringval)\r\n",
        "  gendata = np.load(stringval)\r\n",
        "  # Using only generated data\r\n",
        "\r\n",
        "  trainlabel_list = [val22,aro22,dom22,lik22]\r\n",
        "  testlabel_list = [val10,aro10,dom10,lik10]\r\n",
        "  names = [\"Valence\",\"Arousal\",\"Dominance\",\"Liking\"]\r\n",
        "  scorelist = []\r\n",
        "\r\n",
        "  # kval = np.sqrt((len(ls[0]) - len(ls[0]) * ts))\r\n",
        "  # kval = int(np.round(kval))\r\n",
        "  # try it for different kvalues\r\n",
        "  k = 20 \r\n",
        "  print(\"\\n EPOCH\",ep+1)\r\n",
        "  for traain,tesst,name in zip(trainlabel_list,testlabel_list,names):\r\n",
        "    \r\n",
        "    print(\"#########################################\",name,\"#########################################\")\r\n",
        "    sc = knnmodel(gendata.reshape(-1, 40*99),traain,testdata.reshape(-1, 40*99),tesst,kval = k)\r\n",
        "    scorelist.append(sc)\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "# "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " EPOCH 1\n",
            "######################################### Valence #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 70.5\n",
            "\n",
            " EPOCH 2\n",
            "######################################### Valence #########################################\n",
            "Accuracy 42.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 42.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 70.0\n",
            "\n",
            " EPOCH 3\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 70.5\n",
            "\n",
            " EPOCH 4\n",
            "######################################### Valence #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 45.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.0\n",
            "\n",
            " EPOCH 5\n",
            "######################################### Valence #########################################\n",
            "Accuracy 58.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 70.0\n",
            "\n",
            " EPOCH 6\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.25\n",
            "\n",
            " EPOCH 7\n",
            "######################################### Valence #########################################\n",
            "Accuracy 43.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.5\n",
            "\n",
            " EPOCH 8\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 70.5\n",
            "\n",
            " EPOCH 9\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 70.25\n",
            "\n",
            " EPOCH 10\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.5\n",
            "\n",
            " EPOCH 11\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 70.0\n",
            "\n",
            " EPOCH 12\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 71.25\n",
            "\n",
            " EPOCH 13\n",
            "######################################### Valence #########################################\n",
            "Accuracy 44.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.25\n",
            "\n",
            " EPOCH 14\n",
            "######################################### Valence #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 70.5\n",
            "\n",
            " EPOCH 15\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.25\n",
            "\n",
            " EPOCH 16\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.25\n",
            "\n",
            " EPOCH 17\n",
            "######################################### Valence #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.5\n",
            "\n",
            " EPOCH 18\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.75\n",
            "\n",
            " EPOCH 19\n",
            "######################################### Valence #########################################\n",
            "Accuracy 44.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 58.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.75\n",
            "\n",
            " EPOCH 20\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.75\n",
            "\n",
            " EPOCH 21\n",
            "######################################### Valence #########################################\n",
            "Accuracy 45.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.25\n",
            "\n",
            " EPOCH 22\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 70.25\n",
            "\n",
            " EPOCH 23\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.75\n",
            "\n",
            " EPOCH 24\n",
            "######################################### Valence #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 58.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.25\n",
            "\n",
            " EPOCH 25\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.0\n",
            "\n",
            " EPOCH 26\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.25\n",
            "\n",
            " EPOCH 27\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.75\n",
            "\n",
            " EPOCH 28\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.75\n",
            "\n",
            " EPOCH 29\n",
            "######################################### Valence #########################################\n",
            "Accuracy 43.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.75\n",
            "\n",
            " EPOCH 30\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.5\n",
            "\n",
            " EPOCH 31\n",
            "######################################### Valence #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.25\n",
            "\n",
            " EPOCH 32\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 58.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.0\n",
            "\n",
            " EPOCH 33\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 46.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.0\n",
            "\n",
            " EPOCH 34\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.5\n",
            "\n",
            " EPOCH 35\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 58.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.0\n",
            "\n",
            " EPOCH 36\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.5\n",
            "\n",
            " EPOCH 37\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 58.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.75\n",
            "\n",
            " EPOCH 38\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 56.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.5\n",
            "\n",
            " EPOCH 39\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.5\n",
            "\n",
            " EPOCH 40\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.5\n",
            "\n",
            " EPOCH 41\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 58.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.5\n",
            "\n",
            " EPOCH 42\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 58.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.25\n",
            "\n",
            " EPOCH 43\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.25\n",
            "\n",
            " EPOCH 44\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.5\n",
            "\n",
            " EPOCH 45\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.0\n",
            "\n",
            " EPOCH 46\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.75\n",
            "\n",
            " EPOCH 47\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.75\n",
            "\n",
            " EPOCH 48\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 58.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.25\n",
            "\n",
            " EPOCH 49\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.75\n",
            "\n",
            " EPOCH 50\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.5\n",
            "\n",
            " EPOCH 51\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.75\n",
            "\n",
            " EPOCH 52\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.25\n",
            "\n",
            " EPOCH 53\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.25\n",
            "\n",
            " EPOCH 54\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.25\n",
            "\n",
            " EPOCH 55\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.75\n",
            "\n",
            " EPOCH 56\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 57.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.75\n",
            "\n",
            " EPOCH 57\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.75\n",
            "\n",
            " EPOCH 58\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.75\n",
            "\n",
            " EPOCH 59\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.0\n",
            "\n",
            " EPOCH 60\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.75\n",
            "\n",
            " EPOCH 61\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.5\n",
            "\n",
            " EPOCH 62\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.25\n",
            "\n",
            " EPOCH 63\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.25\n",
            "\n",
            " EPOCH 64\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.75\n",
            "\n",
            " EPOCH 65\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 45.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.5\n",
            "\n",
            " EPOCH 66\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.75\n",
            "\n",
            " EPOCH 67\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 58.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.5\n",
            "\n",
            " EPOCH 68\n",
            "######################################### Valence #########################################\n",
            "Accuracy 45.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.5\n",
            "\n",
            " EPOCH 69\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.0\n",
            "\n",
            " EPOCH 70\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.5\n",
            "\n",
            " EPOCH 71\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.75\n",
            "\n",
            " EPOCH 72\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.5\n",
            "\n",
            " EPOCH 73\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.75\n",
            "\n",
            " EPOCH 74\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.25\n",
            "\n",
            " EPOCH 75\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.25\n",
            "\n",
            " EPOCH 76\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 46.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.75\n",
            "\n",
            " EPOCH 77\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.5\n",
            "\n",
            " EPOCH 78\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.5\n",
            "\n",
            " EPOCH 79\n",
            "######################################### Valence #########################################\n",
            "Accuracy 44.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.5\n",
            "\n",
            " EPOCH 80\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.0\n",
            "\n",
            " EPOCH 81\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.75\n",
            "\n",
            " EPOCH 82\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.75\n",
            "\n",
            " EPOCH 83\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.0\n",
            "\n",
            " EPOCH 84\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.25\n",
            "\n",
            " EPOCH 85\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.5\n",
            "\n",
            " EPOCH 86\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.5\n",
            "\n",
            " EPOCH 87\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.75\n",
            "\n",
            " EPOCH 88\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.75\n",
            "\n",
            " EPOCH 89\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.75\n",
            "\n",
            " EPOCH 90\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.5\n",
            "\n",
            " EPOCH 91\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.75\n",
            "\n",
            " EPOCH 92\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 56.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.0\n",
            "\n",
            " EPOCH 93\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.25\n",
            "\n",
            " EPOCH 94\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.25\n",
            "\n",
            " EPOCH 95\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.0\n",
            "\n",
            " EPOCH 96\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.0\n",
            "\n",
            " EPOCH 97\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.5\n",
            "\n",
            " EPOCH 98\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.5\n",
            "\n",
            " EPOCH 99\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.0\n",
            "\n",
            " EPOCH 100\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.25\n",
            "\n",
            " EPOCH 101\n",
            "######################################### Valence #########################################\n",
            "Accuracy 45.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.25\n",
            "\n",
            " EPOCH 102\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.0\n",
            "\n",
            " EPOCH 103\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 59.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.25\n",
            "\n",
            " EPOCH 104\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.75\n",
            "\n",
            " EPOCH 105\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 58.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.0\n",
            "\n",
            " EPOCH 106\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.25\n",
            "\n",
            " EPOCH 107\n",
            "######################################### Valence #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.5\n",
            "\n",
            " EPOCH 108\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.0\n",
            "\n",
            " EPOCH 109\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.0\n",
            "\n",
            " EPOCH 110\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 59.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.0\n",
            "\n",
            " EPOCH 111\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.0\n",
            "\n",
            " EPOCH 112\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.0\n",
            "\n",
            " EPOCH 113\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.5\n",
            "\n",
            " EPOCH 114\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.25\n",
            "\n",
            " EPOCH 115\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.5\n",
            "\n",
            " EPOCH 116\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.5\n",
            "\n",
            " EPOCH 117\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.75\n",
            "\n",
            " EPOCH 118\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.75\n",
            "\n",
            " EPOCH 119\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.5\n",
            "\n",
            " EPOCH 120\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.5\n",
            "\n",
            " EPOCH 121\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.75\n",
            "\n",
            " EPOCH 122\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.75\n",
            "\n",
            " EPOCH 123\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.25\n",
            "\n",
            " EPOCH 124\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.25\n",
            "\n",
            " EPOCH 125\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 58.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.0\n",
            "\n",
            " EPOCH 126\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.25\n",
            "\n",
            " EPOCH 127\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.0\n",
            "\n",
            " EPOCH 128\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.0\n",
            "\n",
            " EPOCH 129\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.5\n",
            "\n",
            " EPOCH 130\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.25\n",
            "\n",
            " EPOCH 131\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.75\n",
            "\n",
            " EPOCH 132\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 60.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.0\n",
            "\n",
            " EPOCH 133\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.5\n",
            "\n",
            " EPOCH 134\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.0\n",
            "\n",
            " EPOCH 135\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 57.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 59.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.0\n",
            "\n",
            " EPOCH 136\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.75\n",
            "\n",
            " EPOCH 137\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.0\n",
            "\n",
            " EPOCH 138\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.0\n",
            "\n",
            " EPOCH 139\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 56.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.5\n",
            "\n",
            " EPOCH 140\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.25\n",
            "\n",
            " EPOCH 141\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.75\n",
            "\n",
            " EPOCH 142\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.0\n",
            "\n",
            " EPOCH 143\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.0\n",
            "\n",
            " EPOCH 144\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.0\n",
            "\n",
            " EPOCH 145\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.75\n",
            "\n",
            " EPOCH 146\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.5\n",
            "\n",
            " EPOCH 147\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.25\n",
            "\n",
            " EPOCH 148\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.25\n",
            "\n",
            " EPOCH 149\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.0\n",
            "\n",
            " EPOCH 150\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.0\n",
            "\n",
            " EPOCH 151\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.75\n",
            "\n",
            " EPOCH 152\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.5\n",
            "\n",
            " EPOCH 153\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.75\n",
            "\n",
            " EPOCH 154\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.75\n",
            "\n",
            " EPOCH 155\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.5\n",
            "\n",
            " EPOCH 156\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.25\n",
            "\n",
            " EPOCH 157\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.5\n",
            "\n",
            " EPOCH 158\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.25\n",
            "\n",
            " EPOCH 159\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.0\n",
            "\n",
            " EPOCH 160\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.25\n",
            "\n",
            " EPOCH 161\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.25\n",
            "\n",
            " EPOCH 162\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.25\n",
            "\n",
            " EPOCH 163\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.5\n",
            "\n",
            " EPOCH 164\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.5\n",
            "\n",
            " EPOCH 165\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.75\n",
            "\n",
            " EPOCH 166\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.25\n",
            "\n",
            " EPOCH 167\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.0\n",
            "\n",
            " EPOCH 168\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.25\n",
            "\n",
            " EPOCH 169\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 59.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.5\n",
            "\n",
            " EPOCH 170\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.25\n",
            "\n",
            " EPOCH 171\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.75\n",
            "\n",
            " EPOCH 172\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.0\n",
            "\n",
            " EPOCH 173\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 57.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.25\n",
            "\n",
            " EPOCH 174\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.0\n",
            "\n",
            " EPOCH 175\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.25\n",
            "\n",
            " EPOCH 176\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.0\n",
            "\n",
            " EPOCH 177\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.0\n",
            "\n",
            " EPOCH 178\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.75\n",
            "\n",
            " EPOCH 179\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.75\n",
            "\n",
            " EPOCH 180\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.5\n",
            "\n",
            " EPOCH 181\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.0\n",
            "\n",
            " EPOCH 182\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.25\n",
            "\n",
            " EPOCH 183\n",
            "######################################### Valence #########################################\n",
            "Accuracy 44.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.75\n",
            "\n",
            " EPOCH 184\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.25\n",
            "\n",
            " EPOCH 185\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.75\n",
            "\n",
            " EPOCH 186\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.75\n",
            "\n",
            " EPOCH 187\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.75\n",
            "\n",
            " EPOCH 188\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.5\n",
            "\n",
            " EPOCH 189\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.25\n",
            "\n",
            " EPOCH 190\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 59.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.25\n",
            "\n",
            " EPOCH 191\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.25\n",
            "\n",
            " EPOCH 192\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.5\n",
            "\n",
            " EPOCH 193\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 58.5\n",
            "\n",
            " EPOCH 194\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.75\n",
            "\n",
            " EPOCH 195\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.75\n",
            "\n",
            " EPOCH 196\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.0\n",
            "\n",
            " EPOCH 197\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.0\n",
            "\n",
            " EPOCH 198\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 68.5\n",
            "\n",
            " EPOCH 199\n",
            "######################################### Valence #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.0\n",
            "\n",
            " EPOCH 200\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 66.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iidREnUL-NRt"
      },
      "source": [
        "# for KNN\r\n",
        "# epoch 5 for valence = 58.75 @ 5\r\n",
        "# epoch 5 for arousal = 58 @ 37,  57.75 @ 173\r\n",
        "# epoch 5 for dominance =    60  @ 132\r\n",
        "# epoch 5 for liking = 71.25 @12"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMyrBzj3O0SL"
      },
      "source": [
        "# Define SVM based model for augmented data\r\n",
        "def svmmodel(xtrain,ytrain,xtest,ytest,c,kernel = 'linear'):\r\n",
        "\r\n",
        "  # SVM model\r\n",
        "\r\n",
        "  model = SVC(C=c,kernel=kernel,gamma = 'auto')\r\n",
        "  model.fit(xtrain, ytrain)\r\n",
        "  y_pred = model.predict(xtest)\r\n",
        "\r\n",
        "  # print(\"Accuracy score for fold\",fold_no)\r\n",
        "  acc = accuracy_score(ytest, y_pred)*100\r\n",
        "  acc = round(acc, 4)\r\n",
        "  print(\"Accuracy\",acc)\r\n",
        "  # print(classification_report(ytest, y_pred))\r\n",
        "  # print(confusion_matrix(ytest,y_pred))\r\n",
        "  # return acc,xtrain, xtest, ytrain, ytest\r\n",
        "  return acc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eExo9vcfO1FE",
        "outputId": "49aaa9ba-eacd-4116-f03f-f5338132484b"
      },
      "source": [
        "# SVM \r\n",
        "for ep in range (200):\r\n",
        "  stringval = \"/content/drive/MyDrive/Thesis data/3rdGAN_trial2/Generated_ep\"+str(ep+1)+\".npy\"\r\n",
        "  # print(stringval)\r\n",
        "  gendata = np.load(stringval)\r\n",
        "  # Using only generated data\r\n",
        "\r\n",
        "  trainlabel_list = [val22,aro22,dom22,lik22]\r\n",
        "  testlabel_list = [val10,aro10,dom10,lik10]\r\n",
        "  names = [\"Valence\",\"Arousal\",\"Dominance\",\"Liking\"]\r\n",
        "  scorelist = []\r\n",
        "\r\n",
        "  # kval = np.sqrt((len(ls[0]) - len(ls[0]) * ts))\r\n",
        "  # kval = int(np.round(kval))\r\n",
        "  # try it for different kvalues\r\n",
        "  c = 0.1\r\n",
        "  print(\"\\n EPOCH\",ep+1)\r\n",
        "  for traain,tesst,name in zip(trainlabel_list,testlabel_list,names):\r\n",
        "    \r\n",
        "    print(\"#########################################\",name,\"#########################################\")\r\n",
        "    sc = svmmodel(gendata.reshape(-1, 40*99),traain,testdata.reshape(-1, 40*99),tesst,c = c)\r\n",
        "    scorelist.append(sc)\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " EPOCH 1\n",
            "######################################### Valence #########################################\n",
            "Accuracy 57.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 45.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.25\n",
            "\n",
            " EPOCH 2\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 33.5\n",
            "\n",
            " EPOCH 3\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 46.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.5\n",
            "\n",
            " EPOCH 4\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 56.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 50.5\n",
            "\n",
            " EPOCH 5\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 69.25\n",
            "\n",
            " EPOCH 6\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 45.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 44.75\n",
            "\n",
            " EPOCH 7\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.5\n",
            "\n",
            " EPOCH 8\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 46.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 67.25\n",
            "\n",
            " EPOCH 9\n",
            "######################################### Valence #########################################\n",
            "Accuracy 43.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 45.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 43.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 42.5\n",
            "\n",
            " EPOCH 10\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 40.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.5\n",
            "\n",
            " EPOCH 11\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.5\n",
            "\n",
            " EPOCH 12\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 43.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.5\n",
            "\n",
            " EPOCH 13\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 44.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.5\n",
            "\n",
            " EPOCH 14\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.75\n",
            "\n",
            " EPOCH 15\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.0\n",
            "\n",
            " EPOCH 16\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.75\n",
            "\n",
            " EPOCH 17\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 58.5\n",
            "\n",
            " EPOCH 18\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 50.0\n",
            "\n",
            " EPOCH 19\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.0\n",
            "\n",
            " EPOCH 20\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.0\n",
            "\n",
            " EPOCH 21\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 51.5\n",
            "\n",
            " EPOCH 22\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.5\n",
            "\n",
            " EPOCH 23\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 44.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.75\n",
            "\n",
            " EPOCH 24\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.5\n",
            "\n",
            " EPOCH 25\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.0\n",
            "\n",
            " EPOCH 26\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.25\n",
            "\n",
            " EPOCH 27\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 47.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.0\n",
            "\n",
            " EPOCH 28\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 57.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.5\n",
            "\n",
            " EPOCH 29\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 46.0\n",
            "\n",
            " EPOCH 30\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 44.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.25\n",
            "\n",
            " EPOCH 31\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 47.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.75\n",
            "\n",
            " EPOCH 32\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 47.75\n",
            "\n",
            " EPOCH 33\n",
            "######################################### Valence #########################################\n",
            "Accuracy 45.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.0\n",
            "\n",
            " EPOCH 34\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 44.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.5\n",
            "\n",
            " EPOCH 35\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.5\n",
            "\n",
            " EPOCH 36\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.5\n",
            "\n",
            " EPOCH 37\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 46.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.5\n",
            "\n",
            " EPOCH 38\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.25\n",
            "\n",
            " EPOCH 39\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.5\n",
            "\n",
            " EPOCH 40\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.25\n",
            "\n",
            " EPOCH 41\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 65.75\n",
            "\n",
            " EPOCH 42\n",
            "######################################### Valence #########################################\n",
            "Accuracy 44.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 47.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.5\n",
            "\n",
            " EPOCH 43\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.5\n",
            "\n",
            " EPOCH 44\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.0\n",
            "\n",
            " EPOCH 45\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.75\n",
            "\n",
            " EPOCH 46\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 46.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 51.75\n",
            "\n",
            " EPOCH 47\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 46.75\n",
            "\n",
            " EPOCH 48\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.75\n",
            "\n",
            " EPOCH 49\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.0\n",
            "\n",
            " EPOCH 50\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.0\n",
            "\n",
            " EPOCH 51\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.5\n",
            "\n",
            " EPOCH 52\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 45.25\n",
            "\n",
            " EPOCH 53\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.25\n",
            "\n",
            " EPOCH 54\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.5\n",
            "\n",
            " EPOCH 55\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 56.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.0\n",
            "\n",
            " EPOCH 56\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.5\n",
            "\n",
            " EPOCH 57\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.5\n",
            "\n",
            " EPOCH 58\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.5\n",
            "\n",
            " EPOCH 59\n",
            "######################################### Valence #########################################\n",
            "Accuracy 58.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.5\n",
            "\n",
            " EPOCH 60\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.5\n",
            "\n",
            " EPOCH 61\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.75\n",
            "\n",
            " EPOCH 62\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 45.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.0\n",
            "\n",
            " EPOCH 63\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 49.5\n",
            "\n",
            " EPOCH 64\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.0\n",
            "\n",
            " EPOCH 65\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 48.0\n",
            "\n",
            " EPOCH 66\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.0\n",
            "\n",
            " EPOCH 67\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.25\n",
            "\n",
            " EPOCH 68\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.0\n",
            "\n",
            " EPOCH 69\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 44.75\n",
            "\n",
            " EPOCH 70\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.75\n",
            "\n",
            " EPOCH 71\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.75\n",
            "\n",
            " EPOCH 72\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.5\n",
            "\n",
            " EPOCH 73\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 45.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 50.25\n",
            "\n",
            " EPOCH 74\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.75\n",
            "\n",
            " EPOCH 75\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.5\n",
            "\n",
            " EPOCH 76\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.75\n",
            "\n",
            " EPOCH 77\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.5\n",
            "\n",
            " EPOCH 78\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 45.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.0\n",
            "\n",
            " EPOCH 79\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 46.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.0\n",
            "\n",
            " EPOCH 80\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.75\n",
            "\n",
            " EPOCH 81\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 47.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.5\n",
            "\n",
            " EPOCH 82\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.25\n",
            "\n",
            " EPOCH 83\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.5\n",
            "\n",
            " EPOCH 84\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.75\n",
            "\n",
            " EPOCH 85\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 50.0\n",
            "\n",
            " EPOCH 86\n",
            "######################################### Valence #########################################\n",
            "Accuracy 45.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 49.5\n",
            "\n",
            " EPOCH 87\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 47.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.75\n",
            "\n",
            " EPOCH 88\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 46.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.0\n",
            "\n",
            " EPOCH 89\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.25\n",
            "\n",
            " EPOCH 90\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.75\n",
            "\n",
            " EPOCH 91\n",
            "######################################### Valence #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.75\n",
            "\n",
            " EPOCH 92\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 47.0\n",
            "\n",
            " EPOCH 93\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.5\n",
            "\n",
            " EPOCH 94\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.0\n",
            "\n",
            " EPOCH 95\n",
            "######################################### Valence #########################################\n",
            "Accuracy 45.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 44.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 45.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.75\n",
            "\n",
            " EPOCH 96\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 56.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.25\n",
            "\n",
            " EPOCH 97\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.0\n",
            "\n",
            " EPOCH 98\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 56.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 45.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.0\n",
            "\n",
            " EPOCH 99\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 49.75\n",
            "\n",
            " EPOCH 100\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.0\n",
            "\n",
            " EPOCH 101\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.5\n",
            "\n",
            " EPOCH 102\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.75\n",
            "\n",
            " EPOCH 103\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 58.0\n",
            "\n",
            " EPOCH 104\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 43.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.25\n",
            "\n",
            " EPOCH 105\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 47.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.25\n",
            "\n",
            " EPOCH 106\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.0\n",
            "\n",
            " EPOCH 107\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.0\n",
            "\n",
            " EPOCH 108\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.25\n",
            "\n",
            " EPOCH 109\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 51.0\n",
            "\n",
            " EPOCH 110\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 51.0\n",
            "\n",
            " EPOCH 111\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.75\n",
            "\n",
            " EPOCH 112\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 44.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.25\n",
            "\n",
            " EPOCH 113\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 64.25\n",
            "\n",
            " EPOCH 114\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.5\n",
            "\n",
            " EPOCH 115\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 47.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.5\n",
            "\n",
            " EPOCH 116\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.25\n",
            "\n",
            " EPOCH 117\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 56.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.0\n",
            "\n",
            " EPOCH 118\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.5\n",
            "\n",
            " EPOCH 119\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.75\n",
            "\n",
            " EPOCH 120\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.0\n",
            "\n",
            " EPOCH 121\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 51.0\n",
            "\n",
            " EPOCH 122\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 46.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.25\n",
            "\n",
            " EPOCH 123\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.75\n",
            "\n",
            " EPOCH 124\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 58.75\n",
            "\n",
            " EPOCH 125\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.25\n",
            "\n",
            " EPOCH 126\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.0\n",
            "\n",
            " EPOCH 127\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.0\n",
            "\n",
            " EPOCH 128\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.25\n",
            "\n",
            " EPOCH 129\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.75\n",
            "\n",
            " EPOCH 130\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 58.5\n",
            "\n",
            " EPOCH 131\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 46.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 50.75\n",
            "\n",
            " EPOCH 132\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 58.5\n",
            "\n",
            " EPOCH 133\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.75\n",
            "\n",
            " EPOCH 134\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.75\n",
            "\n",
            " EPOCH 135\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.75\n",
            "\n",
            " EPOCH 136\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 49.75\n",
            "\n",
            " EPOCH 137\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.5\n",
            "\n",
            " EPOCH 138\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 47.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 47.0\n",
            "\n",
            " EPOCH 139\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.5\n",
            "\n",
            " EPOCH 140\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 58.25\n",
            "\n",
            " EPOCH 141\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.75\n",
            "\n",
            " EPOCH 142\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.5\n",
            "\n",
            " EPOCH 143\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 55.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 58.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.5\n",
            "\n",
            " EPOCH 144\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.0\n",
            "\n",
            " EPOCH 145\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.0\n",
            "\n",
            " EPOCH 146\n",
            "######################################### Valence #########################################\n",
            "Accuracy 56.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.75\n",
            "\n",
            " EPOCH 147\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 50.25\n",
            "\n",
            " EPOCH 148\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.5\n",
            "\n",
            " EPOCH 149\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 51.25\n",
            "\n",
            " EPOCH 150\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.5\n",
            "\n",
            " EPOCH 151\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.75\n",
            "\n",
            " EPOCH 152\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.5\n",
            "\n",
            " EPOCH 153\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.25\n",
            "\n",
            " EPOCH 154\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 51.5\n",
            "\n",
            " EPOCH 155\n",
            "######################################### Valence #########################################\n",
            "Accuracy 41.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 58.75\n",
            "\n",
            " EPOCH 156\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 59.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.0\n",
            "\n",
            " EPOCH 157\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.25\n",
            "\n",
            " EPOCH 158\n",
            "######################################### Valence #########################################\n",
            "Accuracy 56.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.0\n",
            "\n",
            " EPOCH 159\n",
            "######################################### Valence #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.5\n",
            "\n",
            " EPOCH 160\n",
            "######################################### Valence #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.25\n",
            "\n",
            " EPOCH 161\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.75\n",
            "\n",
            " EPOCH 162\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 46.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 50.75\n",
            "\n",
            " EPOCH 163\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.5\n",
            "\n",
            " EPOCH 164\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.0\n",
            "\n",
            " EPOCH 165\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.5\n",
            "\n",
            " EPOCH 166\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.0\n",
            "\n",
            " EPOCH 167\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.25\n",
            "\n",
            " EPOCH 168\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 47.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.25\n",
            "\n",
            " EPOCH 169\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.75\n",
            "\n",
            " EPOCH 170\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.75\n",
            "\n",
            " EPOCH 171\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.5\n",
            "\n",
            " EPOCH 172\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.0\n",
            "\n",
            " EPOCH 173\n",
            "######################################### Valence #########################################\n",
            "Accuracy 44.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.5\n",
            "\n",
            " EPOCH 174\n",
            "######################################### Valence #########################################\n",
            "Accuracy 46.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 50.75\n",
            "\n",
            " EPOCH 175\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 58.75\n",
            "\n",
            " EPOCH 176\n",
            "######################################### Valence #########################################\n",
            "Accuracy 56.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 53.0\n",
            "\n",
            " EPOCH 177\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 44.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 57.75\n",
            "\n",
            " EPOCH 178\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 56.25\n",
            "\n",
            " EPOCH 179\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 53.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 61.5\n",
            "\n",
            " EPOCH 180\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 56.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 63.25\n",
            "\n",
            " EPOCH 181\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.5\n",
            "\n",
            " EPOCH 182\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 46.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.25\n",
            "\n",
            " EPOCH 183\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.75\n",
            "\n",
            " EPOCH 184\n",
            "######################################### Valence #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.0\n",
            "\n",
            " EPOCH 185\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.5\n",
            "\n",
            " EPOCH 186\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 50.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.5\n",
            "\n",
            " EPOCH 187\n",
            "######################################### Valence #########################################\n",
            "Accuracy 57.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 54.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.75\n",
            "\n",
            " EPOCH 188\n",
            "######################################### Valence #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 62.5\n",
            "\n",
            " EPOCH 189\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 55.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 51.25\n",
            "\n",
            " EPOCH 190\n",
            "######################################### Valence #########################################\n",
            "Accuracy 44.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 52.75\n",
            "\n",
            " EPOCH 191\n",
            "######################################### Valence #########################################\n",
            "Accuracy 47.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.25\n",
            "\n",
            " EPOCH 192\n",
            "######################################### Valence #########################################\n",
            "Accuracy 44.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.0\n",
            "\n",
            " EPOCH 193\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 59.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.25\n",
            "\n",
            " EPOCH 194\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 44.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.75\n",
            "\n",
            " EPOCH 195\n",
            "######################################### Valence #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 56.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 55.5\n",
            "\n",
            " EPOCH 196\n",
            "######################################### Valence #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 49.0\n",
            "######################################### Liking #########################################\n",
            "Accuracy 58.0\n",
            "\n",
            " EPOCH 197\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 48.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 51.25\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.25\n",
            "\n",
            " EPOCH 198\n",
            "######################################### Valence #########################################\n",
            "Accuracy 55.0\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 54.25\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.5\n",
            "######################################### Liking #########################################\n",
            "Accuracy 54.0\n",
            "\n",
            " EPOCH 199\n",
            "######################################### Valence #########################################\n",
            "Accuracy 48.5\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 50.0\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 52.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 59.0\n",
            "\n",
            " EPOCH 200\n",
            "######################################### Valence #########################################\n",
            "Accuracy 49.25\n",
            "######################################### Arousal #########################################\n",
            "Accuracy 49.5\n",
            "######################################### Dominance #########################################\n",
            "Accuracy 53.75\n",
            "######################################### Liking #########################################\n",
            "Accuracy 60.75\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0bhn3UiPGQy"
      },
      "source": [
        "# 146"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}