humour_detection_FeedForwardNetwork.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "humour_detection_FeedForwardNetwork.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSJfDFFaR2_L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "ed8bdb16-d5bd-4413-8d68-2160484cbb83"
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Embedding,Flatten,Dense,Dropout,SimpleRNN,LSTM,Conv1D,MaxPooling1D,GlobalMaxPooling1D,GRU\n",
        "from keras.optimizers import RMSprop\n",
        "from keras import backend as K"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uOmMMUrSLd0",
        "colab_type": "code",
        "outputId": "d33ac7c9-52cc-4789-810d-1e111875c88a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!git clone https://github.com/iamdsc/humor_detection.git"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'humor_detection' already exists and is not an empty directory.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9k8bIadSNWU",
        "colab_type": "code",
        "outputId": "43762904-a382-47c4-9854-01ef1c4e3f89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "%cd humor_detection/datasets\n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/humor_detection/datasets\n",
            "glove.6B.100d.txt  glove.6B.zip\t\t\t  reuters_headlines.pickle\n",
            "glove.6B.200d.txt  humorous_oneliners.pickle\t  wiki_sentences.pickle\n",
            "glove.6B.300d.txt  oneliners_incl_doubles.pickle\n",
            "glove.6B.50d.txt   proverbs.pickle\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOsa_8fISQlG",
        "colab_type": "code",
        "outputId": "6f609701-1624-4727-b61d-cfc365975cfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "# Read in the lists of sentences from respective pickle files\n",
        "humour = pd.read_pickle('humorous_oneliners.pickle')\n",
        "proverb = pd.read_pickle('proverbs.pickle')\n",
        "wiki = pd.read_pickle('wiki_sentences.pickle')\n",
        "long_humour = pd.read_pickle('oneliners_incl_doubles.pickle')\n",
        "reuters = pd.read_pickle('reuters_headlines.pickle')\n",
        "\n",
        "texts = []\n",
        "labels = []\n",
        "\n",
        "# shuffling the different negative samples\n",
        "neg = proverb+wiki+reuters\n",
        "np.random.shuffle(neg)\n",
        "\n",
        "# adding the positive samples\n",
        "print('No. of positive samples',len(humour))\n",
        "for line in humour:\n",
        "  texts.append(line)\n",
        "  labels.append(1)\n",
        "  \n",
        "# taking equal samples from both classes\n",
        "neg = neg[:len(humour)]\n",
        "\n",
        "# adding the negative samples\n",
        "print('No. of negative samples',len(neg))\n",
        "for line in neg:\n",
        "  texts.append(line)\n",
        "  labels.append(0)\n",
        "  \n",
        "print('Total no. of samples',len(texts))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No. of positive samples 5251\n",
            "No. of negative samples 5251\n",
            "Total no. of samples 10502\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-eJIsnyZSVVD",
        "colab_type": "code",
        "outputId": "35d3e9ed-2dea-41f9-f4d8-8bdaee8a451e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "# Tokenizing the data\n",
        "maxlen = 50 # cut off sentences after 50 words\n",
        "max_words = 10000 # only consider top 10000 common words in dataset\n",
        "\n",
        "tokenizer = Tokenizer(num_words=max_words)\n",
        "tokenizer.fit_on_texts(texts)\n",
        "\n",
        "sequences = tokenizer.texts_to_sequences(texts)\n",
        "word_index = tokenizer.word_index\n",
        "\n",
        "print('Found %s unique tokens'%len(word_index))\n",
        "\n",
        "# padding the sequences\n",
        "data = pad_sequences(sequences, maxlen=maxlen)\n",
        "\n",
        "labels = np.array(labels)\n",
        "\n",
        "print('Shape of data tensor:',data.shape)\n",
        "print('Shape of labels tensor:',labels.shape)\n",
        "\n",
        "# shuffle the data\n",
        "indices = np.arange(data.shape[0])\n",
        "np.random.shuffle(indices)\n",
        "data = data[indices]\n",
        "labels = labels[indices]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 16786 unique tokens\n",
            "Shape of data tensor: (10502, 50)\n",
            "Shape of labels tensor: (10502,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRVdbph6SXoJ",
        "colab_type": "code",
        "outputId": "4d0e8dd4-727b-4fbd-80ca-12dea36f6aab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "!wget http://nlp.stanford.edu/data/glove.6B.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-02 10:20:32--  http://nlp.stanford.edu/data/glove.6B.zip\n",
            "Resolving nlp.stanford.edu (nlp.stanford.edu)... 171.64.67.140\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://nlp.stanford.edu/data/glove.6B.zip [following]\n",
            "--2020-04-02 10:20:32--  https://nlp.stanford.edu/data/glove.6B.zip\n",
            "Connecting to nlp.stanford.edu (nlp.stanford.edu)|171.64.67.140|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip [following]\n",
            "--2020-04-02 10:20:33--  http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip\n",
            "Resolving downloads.cs.stanford.edu (downloads.cs.stanford.edu)... 171.64.64.22\n",
            "Connecting to downloads.cs.stanford.edu (downloads.cs.stanford.edu)|171.64.64.22|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 862182613 (822M) [application/zip]\n",
            "Saving to: ‘glove.6B.zip.1’\n",
            "\n",
            "glove.6B.zip.1      100%[===================>] 822.24M  2.14MB/s    in 6m 28s  \n",
            "\n",
            "2020-04-02 10:27:01 (2.12 MB/s) - ‘glove.6B.zip.1’ saved [862182613/862182613]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJcCFN3CSaIl",
        "colab_type": "code",
        "outputId": "aae66db3-a14f-493d-a9de-87d363d079c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "!unzip glove.6B.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  glove.6B.zip\n",
            "replace glove.6B.50d.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: glove.6B.50d.txt        \n",
            "replace glove.6B.100d.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: glove.6B.100d.txt       \n",
            "replace glove.6B.200d.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: glove.6B.200d.txt       \n",
            "replace glove.6B.300d.txt? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: glove.6B.300d.txt       \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5norTyPScF3",
        "colab_type": "code",
        "outputId": "344027a1-306a-444c-86c2-b4e537ae48b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "embeddings_index = {}\n",
        "\n",
        "f = open('glove.6B.100d.txt')\n",
        "for line in f:\n",
        "  values = line.split()\n",
        "  word = values[0]\n",
        "  coefs = np.asarray(values[1:], dtype='float32')\n",
        "  embeddings_index[word] = coefs\n",
        "f.close()\n",
        "\n",
        "print('Found %s word vectors:'%len(embeddings_index))\n",
        "\n",
        "# preparing glove word embeddings matrix\n",
        "embedding_dim = 100\n",
        "\n",
        "embedding_matrix = np.zeros((max_words, embedding_dim))\n",
        "for word, i in word_index.items():\n",
        "  if i<max_words:\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None:\n",
        "      embedding_matrix[i] = embedding_vector # for words not in embedding index values will be zeros"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 400000 word vectors:\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGbya9_2Sf7U",
        "colab_type": "code",
        "outputId": "0e8b42a7-0675-48a8-97aa-bc6f62ffb464",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(max_words, embedding_dim, input_length=maxlen))\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.3)) # adding regularization\n",
        "\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_2 (Embedding)      (None, 50, 100)           1000000   \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 5000)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                320064    \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 1,320,129\n",
            "Trainable params: 1,320,129\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QY48tAe8S7Wj",
        "colab_type": "code",
        "outputId": "8757c321-24fc-41d6-fdf1-3e6a6a6a229d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "model.layers[0].set_weights([embedding_matrix])\n",
        "model.layers[0].trainable = False"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVGwnSL1S_Ej",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def f1(y_true, y_pred):\n",
        "    def recall(y_true, y_pred):\n",
        "        \"\"\"Recall metric.\n",
        "\n",
        "        Only computes a batch-wise average of recall.\n",
        "\n",
        "        Computes the recall, a metric for multi-label classification of\n",
        "        how many relevant items are selected.\n",
        "        \"\"\"\n",
        "        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "        recall = true_positives / (possible_positives + K.epsilon())\n",
        "        return recall\n",
        "\n",
        "    def precision(y_true, y_pred):\n",
        "        \"\"\"Precision metric.\n",
        "\n",
        "        Only computes a batch-wise average of precision.\n",
        "\n",
        "        Computes the precision, a metric for multi-label classification of\n",
        "        how many selected items are relevant.\n",
        "        \"\"\"\n",
        "        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "        precision = true_positives / (predicted_positives + K.epsilon())\n",
        "        return precision\n",
        "    precision = precision(y_true, y_pred)\n",
        "    recall = recall(y_true, y_pred)\n",
        "    return 2*((precision*recall)/(precision+recall+K.epsilon()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgsYFyojTCYX",
        "colab_type": "code",
        "outputId": "a2a58b36-eb4a-4e6a-f057-786555a6e602",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 910
        }
      },
      "source": [
        "model.compile(optimizer=RMSprop(lr=1e-4), loss='binary_crossentropy', metrics = ['acc', f1])\n",
        "history = model.fit(data, labels, epochs=20, batch_size=32, validation_split=0.1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "Train on 9451 samples, validate on 1051 samples\n",
            "Epoch 1/20\n",
            "9451/9451 [==============================] - 2s 217us/step - loss: 0.3836 - acc: 0.8390 - f1: 0.8328 - val_loss: 0.2717 - val_acc: 0.8944 - val_f1: 0.8835\n",
            "Epoch 2/20\n",
            "9451/9451 [==============================] - 2s 173us/step - loss: 0.2659 - acc: 0.8955 - f1: 0.8906 - val_loss: 0.2373 - val_acc: 0.9106 - val_f1: 0.9021\n",
            "Epoch 3/20\n",
            "9451/9451 [==============================] - 2s 171us/step - loss: 0.2341 - acc: 0.9059 - f1: 0.9024 - val_loss: 0.2274 - val_acc: 0.9125 - val_f1: 0.9096\n",
            "Epoch 4/20\n",
            "9451/9451 [==============================] - 2s 172us/step - loss: 0.2165 - acc: 0.9123 - f1: 0.9096 - val_loss: 0.2188 - val_acc: 0.9172 - val_f1: 0.9099\n",
            "Epoch 5/20\n",
            "9451/9451 [==============================] - 2s 175us/step - loss: 0.1997 - acc: 0.9241 - f1: 0.9220 - val_loss: 0.2189 - val_acc: 0.9144 - val_f1: 0.9109\n",
            "Epoch 6/20\n",
            "9451/9451 [==============================] - 2s 170us/step - loss: 0.1859 - acc: 0.9301 - f1: 0.9283 - val_loss: 0.2135 - val_acc: 0.9172 - val_f1: 0.9143\n",
            "Epoch 7/20\n",
            "9451/9451 [==============================] - 2s 179us/step - loss: 0.1792 - acc: 0.9311 - f1: 0.9290 - val_loss: 0.2024 - val_acc: 0.9210 - val_f1: 0.9145\n",
            "Epoch 8/20\n",
            "9451/9451 [==============================] - 2s 171us/step - loss: 0.1681 - acc: 0.9368 - f1: 0.9344 - val_loss: 0.2032 - val_acc: 0.9258 - val_f1: 0.9191\n",
            "Epoch 9/20\n",
            "9451/9451 [==============================] - 2s 174us/step - loss: 0.1587 - acc: 0.9407 - f1: 0.9389 - val_loss: 0.2030 - val_acc: 0.9248 - val_f1: 0.9186\n",
            "Epoch 10/20\n",
            "9451/9451 [==============================] - 2s 169us/step - loss: 0.1512 - acc: 0.9446 - f1: 0.9429 - val_loss: 0.2026 - val_acc: 0.9229 - val_f1: 0.9180\n",
            "Epoch 11/20\n",
            "9451/9451 [==============================] - 2s 169us/step - loss: 0.1431 - acc: 0.9473 - f1: 0.9457 - val_loss: 0.2108 - val_acc: 0.9163 - val_f1: 0.9127\n",
            "Epoch 12/20\n",
            "9451/9451 [==============================] - 2s 166us/step - loss: 0.1366 - acc: 0.9487 - f1: 0.9473 - val_loss: 0.2041 - val_acc: 0.9210 - val_f1: 0.9150\n",
            "Epoch 13/20\n",
            "9451/9451 [==============================] - 2s 169us/step - loss: 0.1314 - acc: 0.9521 - f1: 0.9505 - val_loss: 0.2022 - val_acc: 0.9220 - val_f1: 0.9157\n",
            "Epoch 14/20\n",
            "9451/9451 [==============================] - 2s 168us/step - loss: 0.1260 - acc: 0.9551 - f1: 0.9538 - val_loss: 0.2010 - val_acc: 0.9248 - val_f1: 0.9186\n",
            "Epoch 15/20\n",
            "9451/9451 [==============================] - 2s 171us/step - loss: 0.1186 - acc: 0.9571 - f1: 0.9563 - val_loss: 0.2059 - val_acc: 0.9229 - val_f1: 0.9165\n",
            "Epoch 16/20\n",
            "9451/9451 [==============================] - 2s 171us/step - loss: 0.1150 - acc: 0.9585 - f1: 0.9575 - val_loss: 0.2107 - val_acc: 0.9220 - val_f1: 0.9151\n",
            "Epoch 17/20\n",
            "9451/9451 [==============================] - 2s 175us/step - loss: 0.1096 - acc: 0.9614 - f1: 0.9603 - val_loss: 0.2100 - val_acc: 0.9229 - val_f1: 0.9179\n",
            "Epoch 18/20\n",
            "9451/9451 [==============================] - 2s 171us/step - loss: 0.1049 - acc: 0.9624 - f1: 0.9616 - val_loss: 0.2052 - val_acc: 0.9229 - val_f1: 0.9164\n",
            "Epoch 19/20\n",
            "9451/9451 [==============================] - 2s 165us/step - loss: 0.0996 - acc: 0.9670 - f1: 0.9661 - val_loss: 0.2185 - val_acc: 0.9182 - val_f1: 0.9125\n",
            "Epoch 20/20\n",
            "9451/9451 [==============================] - 2s 170us/step - loss: 0.0953 - acc: 0.9662 - f1: 0.9656 - val_loss: 0.2124 - val_acc: 0.9210 - val_f1: 0.9168\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qCI2Bgz2TEXk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_result(history):\n",
        "  acc = history.history['acc']\n",
        "  val_acc = history.history['val_acc']\n",
        "  loss = history.history['loss']\n",
        "  val_loss = history.history['val_loss']\n",
        "  f1 = history.history['f1']\n",
        "  val_f1 = history.history['val_f1']\n",
        "  \n",
        "  epochs = range(1, len(acc)+1)\n",
        "\n",
        "  plt.plot(epochs, acc, label='Training acc')\n",
        "  plt.plot(epochs, val_acc, label='Validation acc')\n",
        "  plt.title('Training and validation accuracy')\n",
        "  plt.xlabel('epochs')\n",
        "  plt.ylabel('acc')\n",
        "  plt.legend()\n",
        "\n",
        "  plt.figure()\n",
        "\n",
        "  plt.plot(epochs, loss, label='Training loss')\n",
        "  plt.plot(epochs, val_loss, label='Validation loss')\n",
        "  plt.title('Training and validation loss')\n",
        "  plt.xlabel('epochs')\n",
        "  plt.ylabel('loss')\n",
        "  plt.legend()\n",
        "\n",
        "  plt.figure()\n",
        "\n",
        "  plt.plot(epochs, f1, label='Training fmeasure')\n",
        "  plt.plot(epochs, val_f1, label='Validation fmeasure')\n",
        "  plt.title('Training and validation fmeasure')\n",
        "  plt.xlabel('epochs')\n",
        "  plt.ylabel('f1')\n",
        "  plt.legend()\n",
        "  \n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DW0PZK8TIkH",
        "colab_type": "code",
        "outputId": "db9d0f09-d6a2-4286-9d74-6cc3e592bc9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        }
      },
      "source": [
        "plot_result(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NiyAlLpLTh6XQ227MY0ybYgnCNIuq8s6qPH47P4sthW6ai9S4SFLjIhk3INo9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\npGnjMJssAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Wwncvw4aPnWn9LoFht0LPUc7wHa6FG/P4+TsrKC6r4IkrB3JlRtMct9AY07I0\nRIJYCVxYVWsQkQRgrqoOatBIT0OTTBCeDu+CJX+HZa9D0QHo0A+G3QKDJkO4U2PIzS/m7reX8932\ng0wZ1o3HLk8hIjQ4wIEbY1qyhkgQq1U11eN9ELDSc1qgNfkEUaWsGLLeh29fgj0rIDwa0q+DwTdC\npxTKKyr546cbeXH+Fvp3juKF6wfTKyEy0FEbY1qohkgQTwFpwNvupGuBVar6QINFeZqaTYKoogo5\nS53mpzXvQ2UZdE6FQddB6tXMy4F731lBaXkl/31VGhMGdQ10xMaYFui0E4S7kqtwBs0D+EJVP2ig\n+BpEs0sQno4egDXvwcq3YPdykGA44wIO9pnET5Z05pudR7l+eDKPXDbAmpyMMQ2qQRJEU9esE4Sn\nfeth1XRY+Q4U7kbDo1kRPZYnsgdxrPNQnr9+CD06tAt0lMaYFqLeCUJECgFvCwigqhrdMCGevhaT\nIKpUVsD2L2DldFg7C8qOsotOfKgjGTDuVsacPTzQERpjWgCrQTR3JUdg3YcUL32TsF2LCELZ0S6N\nrqP/H6Gpk6BNbKAjNMY0U5YgWpDSAzv54v0X6L7r/zgjaDcaHI70Gw9x3T2G9tDjr1U93tfyLALt\nz4Buw52O8mAbRcWYRlO4F4oPQ4e+J1wb1VgsQbRAc9bs4bUZ7zOBhVwVvpjw8iPul0uOP8P3pwk1\n3ovTnFV82Fk+tC0kDnGSRfJZkDTUaijG+ENxASz6M3z9PFSUQLuOzgW0PUdBr9EQ16NRwrAE0ULt\nOljEnW8tY1V2PlPP6cGD4/vX/yyn/BzY9Q3s/NZ5zl0DWgGIMxBhVcLoNtz54gbgl44xLUJlBSx/\nAz7/LRzNg7TJ0P0c2L4Iti2AI+6I0LHJbsIYAz1HQlRnv4RjCaIFKymv4PezN/D3L7fRv3MUz03J\noG+nBhjLqeQI5CyBXd/Bzm8gezGUFDjzIjt5JIyzoEuaNUsZ44ut82HOQ7B3jfO/M+53To29iqpz\n+4BtC51lt38Bxe692RL6H69h9DjXue1xA7AE0QrM27CPX/5rJYXF5Tx82QB+ODwZachf+ZUVsG/d\nibWMwzudeSFtIOUKOPceSOjXcNs0pqXYvxk+eRg2znZqBhc+DgOuOHlNvLICclc7NYttC2HHV1BW\nBAh0GXS8OSr5bAir3+nvliBaibzCEn7xr5Us3JjHBWd24g9XpxHfLsx/GyzY4ySKrQuc03HLi+HM\ny2DkL6Brhv+2a0xzUXTQuZHY4r85P6RG/QKG3wGhEfVbX3mpMwLDtoXOI/s7qCiFjgPgJ1/Xa5WW\nIFqRykrlta+28/vZ64ltG0MTlEoAABwRSURBVMqfr01nxBkd/L/ho/vh27/Cty9DST70Pg9G3ue0\nrVp/xfepOs13a2Y417mER0Kfi6DPhdB9BISEBzrCpqs43xlxIGcZ7F4Gu1c404LDICTC2Xffew6v\nZbr7HNXV+VGT0A+CGmC0gooyWPwqLHjSiW3wjTD2IYjsePrr9lRa5PxIKzsG/S+t1yosQbRCa3Ly\nuXv6crbtP8pto3pz74V9CQvx9f5Qp6G4AJa86pyZcTTPaWcd+QvnwNfaE4Uq7M1yksKa95wmuuBw\n6HuR8w++7QvnbJbQttBzNPS5wEkaNe4h0qqUHXOaWKqSQc4yOLDp+Py4npA42OkXKy9xH8Xuo8S3\nZ89rgUPbOk03XTPcx2CI7wVBPv7vqMLGOU5z0oFN0GsMXPw76JTSgDulYVmCaKWKSsv5zb/X8fZ3\nO0lLiuHZyRn0bKxhOsqOwbI34MtnoSAbOqXCyHthwMSG+YXWnBzcCqvfcxJD3npnrK3eY2Hg1c6v\nvgh3QILSIqdTctOnsGnO8T6ehP5whpssks+GED82GwZSRTnkrTsxGexbC5XlzvzIzk4ySBzsHLi7\nZkDb+NPbpqrza//QNqdWUvXYswrKjznLhEc7SSNx8PHEEdv9+z949mbBnF87ncvt+8BFv4W+Fzf5\nH0aWIFq52av38OD7qymrqOTxiQO5anBiw3Zg16W8FFb/yznf+8AmiO/tdGanXdtyD3QAhbnOKL1r\nZjhtxuAc3AdeBSlXQruTNPupwv5NsPlT2PQJbP/SGfE3LNL5VdrnQjjjQohJ9Pcn8Q9VJ3HuXu7s\nn5xlsGfl8YNyRMzxX/CJQ5yDc3QjjmhcUe4kc8+ksXeN094Pzl0iq5JFl0Gw5TNY9k8nmYz5lXPr\n4WZyZp8lCMPuw8e4550VfLvtIJcP6spvrxhITJtG/AJXVsC6D+GLP0LuKohOhHPudtpmw9o2Xhz+\ndOyQ05+wZobTXIQ6V6anXgMpkyC2W/3XXXLE6ZTc9IlTwyjIdqZ3THGSRedUaNveebTr4Dw3pX6M\nwr1urWDp8RrCsUPOvJAIt1nHrR0kDnGajnxt1mks5SVOjeaEpLHWuV4oKASG3gKj7z/9Wk0jC1iC\nEJFxwLNAMPCKqj5ZY/69wI+BciAPuFlVd7jzbgIedhf9raq+Xte2LEGcXEWl8uL8zfx57iY6R0fw\n3JR0hnRv5C+zKmz+zEkUO7+Cth3grDucA0R5sXNDpXKPR1mx86uyvMRptiovcd7XXE4VJKjGQ2o8\n1/YQp9knKOT4IzjkxPdBwbW/R5yD9+a5zq/8+N6QerXThJTQ1z/7MG/98WSx8+vjzTCewiKPJw3P\nxOFtWkSs01EeEnH6TSLFBc7NsHKWuo/lxxOaBDtn3CRmOImg62DnQsxm8mv7e8qOOU1L7RKc4W6a\noYAkCBEJBjYCFwLZwGJgiqqu9VhmLPCtqhaJyB3AGFW9VkTigSVAJk4P0lJgiKoeqm17liB8t2zn\nIX42fTk5h47xs/P7cufY3oQEB+DX2o6vnESxeW7dy0mQc4pgaIR71kkEhLZxz0JxnyUItLLGo2q8\nqZrTayxTWeG8rixzDrSVFe5zuff33kR1hYGTnMTQJb1x251LjkB+tnMr26IDULTfeT56oMa0g87Z\nZlXNON5IsHML3KpHWKSTOMKjICzK43XkicsU5ByvHezfSHXHb1UnclUy6JJW7/P1jX8EKkGcDUxT\n1Yvd978CUNX/rmX5DOAvqjpCRKbgJIvb3HkvAfNV9W1vZcESxKkqLC7jkZlrmLliN0N7xPHHa9JJ\nbh+gpp79m5zmhuqDf8TxA39om6b167IqodRMIG3iml6TSG1Ki76fSIrzobTQSTYlhVDqPle/rjHd\n210A2nU83l9Q1ZHczJpbWqO6EkSIH7ebCOzyeJ8N1HUTgx8Bs+so+73eOBG5FbgVIDm5FZ8KWA9R\nEaE8MzmD0f0SeGRmFhf8eQG3j+rFHWPOoE1YI59l1KFP427vdIg4zU/B/vzX8bOwts6jvn0iqlB6\n1CNxFDhNLDFJTf6MHXNqmsS3XER+iNOcNPpUyqnqy8DL4NQg/BBai3dlRhJn9+rAf89ex3Ofb+a9\nZTk8dOmZjB/YufHOdDLNi4jb1BQJDTDsl2m6/FknzgE8f6IkudNOICIXAA8BE1S15FTKmobROSaC\nZydn8O5tZxMVEcJP3lzG9a98y6a9hYEOzRgTQP5MEIuBPiLSU0TCgMnALM8F3H6Hl3CSwz6PWXOA\ni0QkTkTigIvcacaPhvWM598/PZfHJ6aQtbuAcc9+weMfrqWguCzQoRljAsBvCUJVy4G7cA7s64B3\nVTVLRB4XkQnuYk8BkcC/RGSFiMxyyx4EfoOTZBYDj7vTjJ+FBAdx49k9mHffGH6Q2Y3XvtrGeU/P\n519LdlFZaa14xrQmdqGcqdPq7Hwem7WGZTsPk5Ecy39NSCEtye4wZ0xLUddZTM3kvDwTKKlJMcy4\n/Rz+eM0gdh08xsTnv+TB91Zx4EjJyQsbY5o1SxDmpIKChKuGJPH5faP50YiezFiazdin5/P6V9sp\nr6gMdHjGGD+xBGF8Fh0RysOXDeA/Px9JWlIsj83K4rL/WcS3Ww8EOjRjjB9YH4SpF1XlP2ty+e1H\n68g5fIz0brGM6ZfA2H4dSU2MISjIrqEwpjmw0VyN3xwrreD1r7czJyuXFbsOowrt24Uxul8CY/p1\nZFSfDsS2bcHDehvTzFmCMI3i4NFSFm7MY96GfSzcmMehojKCBAYnxzHGTRgpXaPtCm1jmhBLEKbR\nVVQqK7MPM3/9PuZtyGN1Tj4AHaPCGd03gbH9O3Junw5ERzShgfiMaYUsQZiA21dYzMKN+6trF4XF\n5YQECYO7xzG2X0cmpnela2ybQIdpTKtjCcI0KeUVlSzfdZh5bu1i3Z4CQoKESYMTuX10b3olRAY6\nRGNaDUsQpknbdbCIV77YyvTFuyitqOSSgV24Y0xvBibGBDo0Y1o8SxCmWcgrLOG1L7fxxtc7KCwp\nZ0y/BO4cewZDe9hNZ4zxF0sQplnJP1bG/36zg1cXbePg0VKG9YjnJ2N7M7pvgp0BZUwDswRhmqVj\npRVMX7yTlxduZU9+MSldo7lz7BlcnNKZYLsQz5gGYQnCNGul5ZXMXJ7DXxdsYev+o/Tq0I7bx/Tm\nivREwkJstBhjToclCNMiVFQ6w3s8P28za/cU0DUmgltH9eLaocmNfx9tY1oISxCmRVFV5m/M4/nP\nN7NkxyHatwvj+rO6M3loN7uWwphTZAnCtFjfbTvIi/M3M39jHgKM7deR64YnM6ZfR+unMMYHdSWI\nkMYOxpiGNKxnPMN6DmPXwSKmL97JO4uz+Wz9ErrGRHDt0GSuHdqNzjERgQ7TmGbJahCmRSmrqGTu\n2r28+e1OFm3eT3CQcF5/p1Yxqk+C1SqMqcFqEKbVCA0OYnxqF8andmH7/qO8vXgnM5Zk8+navSTF\ntWHKsGSuyUyiY5TVKow5GatBmBavpLyCT7L28ta3O/l66wFCgoQLB3TiuuHJjOjdwW5uZFo1q0GY\nVi08JJjLB3Xl8kFd2Zp3hLe/28mMpdnMXpNL9/ZtmTw0mUmDE+kUbbUKYzxZDcK0SsVlFczJyuXN\nb3fy3baDAGQkxzIupTMXp3SmR4d2AY7QmMZhp7kaU4cteUeYvXoP/8nKZU1OAQD9O0dxcUpnxg3s\nTP/OUTYGlGmxLEEY46NdB4v4ZO1e5qzJZfGOg6hCcnxbxg10ahYZ3WKtz8K0KAFLECIyDngWCAZe\nUdUna8wfBTwDpAGTVXWGx7wKYLX7dqeqTqhrW5YgTEPLKyxh7rq9/GdNLl9t2U9ZhdIxKpyLUjox\nLqULw3vFExpsY0GZ5i0gCUJEgoGNwIVANrAYmKKqaz2W6QFEA/cBs2okiCOq6vOtxSxBGH/KP1bG\nvPX7mJOVy/wNeRwrqyCmTSjnn9mRcSmdGdU3gYhQGw/KND+BOotpGLBZVbe6QUwHJgLVCUJVt7vz\nKv0YhzGnLaZNKFdkJHJFRiLHSitYuCmPOWtymbt2L+8vyyEyPISLBnTi8vSunHtGB6tZmBbBnwki\nEdjl8T4bGH4K5SNEZAlQDjypqjNrLiAitwK3AiQnJ59GqMb4rk1YMBe7ZzuVVVTy9ZYD/HvVbmav\nyeX95TnEtQ1lfGoXJgzqyrAe8dZnYZqtpnwdRHdVzRGRXsDnIrJaVbd4LqCqLwMvg9PEFIggTesW\nGhzEqL4JjOqbwG+uGMjCjfuZtXI3HyzL4a1vd9I5OoLL0rpw+aCupCXF2NlQplnxZ4LIAbp5vE9y\np/lEVXPc560iMh/IALbUWciYAAoPCebCAZ24cEAnikrLmbtuH7NW7Ob1r7fzyqJtdG/flsvTujIh\nvSt9O0UFOlxjTsqfCWIx0EdEeuIkhsnAdb4UFJE4oEhVS0SkAzAC+IPfIjWmgbUNC2HCoK5MGNSV\n/KIy5mTl8uGq3bwwfzN/mbeZ/p2jnKu707qS3L5toMM1xit/n+Z6Cc5prMHA31X1CRF5HFiiqrNE\nZCjwARAHFAO5qpoiIucALwGVQBDwjKq+Wte27Cwm0xzkFZbw8eo9fLhyN0t2HAIgvVssF6d0ZnTf\nBM7sYhflmcZlF8oZ0wRlHyrio1V7+HDV7uoruDtGhTOyTwKj+yUw8owOxLULC3CUpqWzBGFME7ev\noJiFm/azYGMeX2zK43BRGSKQlhTL6L4JjO6bwKCkGELs9FnTwCxBGNOMVFQqq7IPs3DjfhZs3MeK\nXYepVIiOCHFqF+5ZU3anPNMQLEEY04wdLipl0eb9LNyYx4KNeewtKAGgX6coRvXtwOi+HcnsEWdX\ncpt6sQRhTAuhqmzYW8iCDXks3JTH4m2HKK2oJDRYGNAlmozkODKSYxmcHEdSXBvr8DYnZQnCmBbq\naEk532w9wOLth1i+8xCrsvM5VlYBQEJUOBndYhncPY6MbrGkJcXSJsxqGeZEdkc5Y1qoduEhnH9m\nJ84/sxMA5RWVrM8tZPnOQyzbeZhlOw/xydq9AIQECWd2iWZwciwZyXEMTo6jW7zVMkztrAZhTAt3\n4EgJy91kscytZRSVOrWMDpFhpHeLI7NHHJnd40hNiiE8xGoZrYnVIIxpxdpHhnPBgE5cMOB4LWPD\n3sLqpLF852HmrnNqGWEhQQxKiiGzRzxDe8QxJDmemLahgQzfBJDVIIwx7D9SwtIdh1iy/SCLtx9i\nTU4+5ZXOsaFvp8jqhJHZPd46v1sY66Q2xpySY6UVrMw+XJ0wlu04RGFJOQCdosOdhNE9jswe8ZzZ\nJZpgG9K82bImJmPMKWkTFsxZvdpzVq/2gHPx3sa9hdUJY8n2g3y0ag8AkeEhnNUrnotSOnPBmZ2I\nt+FBWgyrQRhj6iXn8DE3YRxk3vo8cg4fIzhIGNojrvqGSl1j2wQ6THMS1sRkjPErVWVNTgFzsnKZ\nk5XLpn1HAEhLinGTRSfO6Gj3wGiKLEEYYxrV1rwjzMnay5ysXFbsOgxAr4R21TWLtMQYuxVrE2EJ\nwhgTMLn5xXy6Npf/ZOXyzdaDVFQqnaMjuCilExendGZYz3hCbZTagLEEYYxpEg4XlfLZun3Mycpl\n4aY8issqiWkTytAe8fROaEevhHb0ToikV0KkdXY3EjuLyRjTJMS2DeOqIUlcNSSJY6UVLNiYxydr\nc1mTk8/CjXmUVlR6LBtKrw7HE4aTPNqRHN+OsBCrcTQGq0EYY5qEikol+1ARW/OOsiXvCFv3H2XL\nPuc5r7CkerngICE5vi29OhyvcZzZJZoBXaOtqaoerAZhjGnygoOE7u3b0b19O8b273jCvILiMrZV\nJY68o2zd7zwv2ryfknKn1hERGsSgpFgye8QxpLszGGFsW2umOh2WIIwxTV50RCiDusUyqFvsCdMr\nKpXdh4+xKjufJTsOsmzHIV5asLV6mJA+HSMZ0t1JGJk94unRvq0NE3IKrInJGNOiFJWWs3JXPst2\nOld8L91xiIJiZ5iQ9u3CGFyVMLrHMTAxptXfic+amIwxrUbbsBDO7t2es3s7w4RUViqb8464gxEe\nYumOg3zq3iMjLDiIgYnOnfjSkmJIS4qle3xbu0bDZQnCGNOiBQUJfTtF0bdTFFOGJQPHR6+tGsH2\nf7/ZUd2XERURQmpiDKlJMaQlxpKWFNNqR7C1JiZjTKtXVlHJpr1HWJ1zmFXZ+azOyWfdngLKKpzj\nY2zbUFITY0hLiiHVTRpdYiJaRNKwC+WMMeYUlZRXsDH3CKtyDrM6O59V2fls3FtY3QHeITLMrWnE\nkt4thoxuccQ1w4v7rA/CGGNOUXhIMKlJTlMTw51pxWUVrNtTwJqc/OqaxoKNm3BzBr0S2jEkOa76\nzKneCZHNuj/DrwlCRMYBzwLBwCuq+mSN+aOAZ4A0YLKqzvCYdxPwsPv2t6r6uj9jNcaYk4kIDSYj\nOY6M5LjqaUWl5azOzmfpTufGSnPX7eVfS7MBiI4IYbB7TcaQ7nEM6hZLZHjz+V3utyYmEQkGNgIX\nAtnAYmCKqq71WKYHEA3cB8yqShAiEg8sATIBBZYCQ1T1UG3bsyYmY0xToKps23+UpTsOsWznIZbt\nOMzGfYWoQpBA/87RzoV83WMZkhxPt/jAdoAHqolpGLBZVbe6QUwHJgLVCUJVt7vzKmuUvRj4VFUP\nuvM/BcYBb/sxXmOMOW0i4o4dFck1md0AyD9Wxopdh52kseMQ7y/L5o1vdgDQITKc9G4xnNkluvrR\nVE619WeCSAR2ebzPprolr15lE2suJCK3ArcCJCcn1y9KY4zxs5g2oYzum8DovgmAcwX4htzC6map\n1Tn5fL5+X3VfRpvQYPp1jnLGmOriPPfvEt3ozVPNpzHMC1V9GXgZnCamAIdjjDE+CQ4SBnR1Bhi8\n4azugNMBvnFvIev3FLJ2TwHr9hTw0ardvP1deXW55Pi2nFmVMDpHM6BLtF+bqPyZIHKAbh7vk9xp\nvpYdU6Ps/AaJyhhjmqCI0GDSkmJJSzo+3pSqsju/mHW7nYSxLreA9XsK+WTtXqq6jyPDQxjTL4G/\nXDe4wWPyZ4JYDPQRkZ44B/zJwHU+lp0D/E5Eqk4VuAj4VcOHaIwxTZeIkBjbhsTYNlwwoFP19KLS\ncjbkFrJuTyHr9hQQ3cY/h3K/JQhVLReRu3AO9sHA31U1S0QeB5ao6iwRGQp8AMQBl4vIf6lqiqoe\nFJHf4CQZgMerOqyNMaa1axsW8r3Tbf3BrqQ2xphWrK7TXO32S8YYY7yyBGGMMcYrSxDGGGO8sgRh\njDHGK0sQxhhjvLIEYYwxxitLEMYYY7xqMddBiEgesCPQcdShA7A/0EHUweI7PRbf6bH4Ts/pxNdd\nVRO8zWgxCaKpE5EltV2M0hRYfKfH4js9Ft/p8Vd81sRkjDHGK0sQxhhjvLIE0XheDnQAJ2HxnR6L\n7/RYfKfHL/FZH4QxxhivrAZhjDHGK0sQxhhjvLIE0UBEpJuIzBORtSKSJSI/87LMGBHJF5EV7uPR\nAMS5XURWu9v/3g00xPGciGwWkVUi0vD3Maw9tn4e+2aFiBSIyM9rLNOo+1BE/i4i+0Rkjce0eBH5\nVEQ2uc9e79oiIje5y2wSkZsaMb6nRGS9+/f7QERiaylb53fBj/FNE5Ecj7/hJbWUHSciG9zv4oON\nGN87HrFtF5EVtZRtjP3n9bjSaN9BVbVHAzyALsBg93UUsBEYUGOZMcC/AxzndqBDHfMvAWYDApwF\nfBugOIOBXJyLeAK2D4FRwGBgjce0PwAPuq8fBH7vpVw8sNV9jnNfxzVSfBcBIe7r33uLz5fvgh/j\nmwbc58PffwvQCwgDVtb8f/JXfDXm/xF4NID7z+txpbG+g1aDaCCqukdVl7mvC4F1QGJgo6qXicA/\n1fENECsiXQIQx/nAFlUN6NXxqroQqHm724nA6+7r14ErvBS9GPhUVQ+q6iHgU2BcY8Snqp+oarn7\n9hsgqaG366ta9p8vhgGbVXWrqpYC03H2e4OqKz4REeAHwNsNvV1f1XFcaZTvoCUIPxCRHkAG8K2X\n2WeLyEoRmS0iKY0amEOBT0RkqYjc6mV+IrDL4302gUl0k6n9HzPQ+7CTqu5xX+cCnbws01T24804\nNUJvTvZd8Ke73Cawv9fSPNIU9t9IYK+qbqplfqPuvxrHlUb5DlqCaGAiEgm8B/xcVQtqzF6G02Qy\nCPgfYGZjxwecq6qDgfHAnSIyKgAx1ElEwoAJwL+8zG4K+7CaOnX5JnmuuIg8BJQDb9aySKC+Cy8C\nvYF0YA9OM05TNIW6aw+Ntv/qOq748ztoCaIBiUgozh/xTVV9v+Z8VS1Q1SPu64+BUBHp0JgxqmqO\n+7wP+ACnKu8pB+jm8T7JndaYxgPLVHVvzRlNYR8Ce6ua3dznfV6WCeh+FJGpwGXA9e4B5Ht8+C74\nharuVdUKVa0E/lbLdgO9/0KAScA7tS3TWPuvluNKo3wHLUE0ELe98lVgnar+qZZlOrvLISLDcPb/\ngUaMsZ2IRFW9xunMXFNjsVnAje7ZTGcB+R5V2cZS6y+3QO9D1yyg6oyQm4D/87LMHOAiEYlzm1Au\ncqf5nYiMA+4HJqhqUS3L+PJd8Fd8nn1aV9ay3cVAHxHp6dYoJ+Ps98ZyAbBeVbO9zWys/VfHcaVx\nvoP+7IFvTQ/gXJxq3ipghfu4BLgduN1d5i4gC+eMjG+Acxo5xl7utle6cTzkTveMUYDncc4gWQ1k\nNnKM7XAO+DEe0wK2D3ES1R6gDKcN90dAe+AzYBMwF4h3l80EXvEoezOw2X38v0aMbzNO23PV9/Cv\n7rJdgY/r+i40UnxvuN+tVTgHui4143PfX4Jz1s6WxozPnf6Pqu+cx7KB2H+1HVca5TtoQ20YY4zx\nypqYjDHGeGUJwhhjjFeWIIwxxnhlCcIYY4xXliCMMcZ4ZQnCmAASZ3Tafwc6DmO8sQRhjDHGK0sQ\nxvhARH4oIt+5Y/+/JCLBInJERP7sjtP/mYgkuMumi8g3cvx+DHHu9DNEZK470OAyEentrj5SRGaI\ncw+HNz2uFH/SvQ/AKhF5OkAf3bRiliCMOQkRORO4FhihqulABXA9zlXfS1Q1BVgAPOYW+SfwgKqm\n4VwxXDX9TeB5dQYaPAfnCl5wRuj8Oc44/72AESLSHmcYihR3Pb/176c05vssQRhzcucDQ4DF7t3F\nzsc5kFdyfDC3/wXOFZEYIFZVF7jTXwdGueP2JKrqBwCqWqzHx0n6TlWz1Rm8bgXQA8gHioFXRWQS\n4HVMJWP8yRKEMScnwOuqmu4++qnqNC/L1XfcmhKP1xU4d4MrxxkddAbOqKz/qee6jak3SxDGnNxn\nwNUi0hGq7wfcHef/52p3meuARaqaDxwSkZHu9BuABercDSxbRK5w1xEuIm1r26A7/n+MOkOa3wMM\n8scHM6YuIYEOwJimTlXXisjDOHcPC8IZ+fNO4CgwzJ23D6efApzhl//qJoCtwP9zp98AvCQij7vr\nuKaOzUYB/yciETg1mHsb+GMZc1I2mqsx9SQiR1Q1MtBxGOMv1sRkjDHGK6tBGGOM8cpqEMYYY7yy\nBGGMMcYrSxDGGGO8sgRhjDHGK0sQxhhjvPr/LT6wmn6258MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KlClMnz6defPm0bVrV7Kzs8nPzycvL48OHToQERHBvn37+OCDD5g8eTIFBQUUFRUxdepU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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQ8GcVoVTPVA",
        "colab_type": "code",
        "outputId": "95b3bcb0-3136-49cf-dae0-aa61af1519b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(max_words, embedding_dim, input_length=maxlen))\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.3)) # adding regularization\n",
        "\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.summary()\n",
        "\n",
        "model.compile(optimizer=RMSprop(lr=1e-4), loss='binary_crossentropy', metrics = ['acc', f1])\n",
        "history = model.fit(data, labels, epochs=20, batch_size=32, validation_split=0.1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_3 (Embedding)      (None, 50, 100)           1000000   \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 5000)              0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 64)                320064    \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 1,320,129\n",
            "Trainable params: 1,320,129\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 9451 samples, validate on 1051 samples\n",
            "Epoch 1/20\n",
            "9451/9451 [==============================] - 5s 513us/step - loss: 0.6229 - acc: 0.6716 - f1: 0.6280 - val_loss: 0.5506 - val_acc: 0.7307 - val_f1: 0.7306\n",
            "Epoch 2/20\n",
            "9451/9451 [==============================] - 5s 491us/step - loss: 0.4596 - acc: 0.8075 - f1: 0.8033 - val_loss: 0.3628 - val_acc: 0.8735 - val_f1: 0.8518\n",
            "Epoch 3/20\n",
            "9451/9451 [==============================] - 5s 489us/step - loss: 0.2928 - acc: 0.8975 - f1: 0.8921 - val_loss: 0.2397 - val_acc: 0.9201 - val_f1: 0.9110\n",
            "Epoch 4/20\n",
            "9451/9451 [==============================] - 5s 493us/step - loss: 0.2153 - acc: 0.9252 - f1: 0.9216 - val_loss: 0.1950 - val_acc: 0.9277 - val_f1: 0.9199\n",
            "Epoch 5/20\n",
            "9451/9451 [==============================] - 5s 490us/step - loss: 0.1777 - acc: 0.9349 - f1: 0.9325 - val_loss: 0.1674 - val_acc: 0.9372 - val_f1: 0.9324\n",
            "Epoch 6/20\n",
            "9451/9451 [==============================] - 5s 476us/step - loss: 0.1557 - acc: 0.9438 - f1: 0.9418 - val_loss: 0.1526 - val_acc: 0.9429 - val_f1: 0.9378\n",
            "Epoch 7/20\n",
            "9451/9451 [==============================] - 4s 468us/step - loss: 0.1380 - acc: 0.9486 - f1: 0.9460 - val_loss: 0.1424 - val_acc: 0.9477 - val_f1: 0.9447\n",
            "Epoch 8/20\n",
            "9451/9451 [==============================] - 5s 477us/step - loss: 0.1247 - acc: 0.9535 - f1: 0.9515 - val_loss: 0.1353 - val_acc: 0.9496 - val_f1: 0.9464\n",
            "Epoch 9/20\n",
            "9451/9451 [==============================] - 4s 469us/step - loss: 0.1134 - acc: 0.9584 - f1: 0.9577 - val_loss: 0.1308 - val_acc: 0.9486 - val_f1: 0.9447\n",
            "Epoch 10/20\n",
            "9451/9451 [==============================] - 4s 465us/step - loss: 0.1046 - acc: 0.9604 - f1: 0.9581 - val_loss: 0.1250 - val_acc: 0.9486 - val_f1: 0.9464\n",
            "Epoch 11/20\n",
            "9451/9451 [==============================] - 5s 482us/step - loss: 0.0948 - acc: 0.9653 - f1: 0.9642 - val_loss: 0.1223 - val_acc: 0.9496 - val_f1: 0.9472\n",
            "Epoch 12/20\n",
            "9451/9451 [==============================] - 4s 472us/step - loss: 0.0888 - acc: 0.9665 - f1: 0.9653 - val_loss: 0.1231 - val_acc: 0.9496 - val_f1: 0.9473\n",
            "Epoch 13/20\n",
            "9451/9451 [==============================] - 5s 476us/step - loss: 0.0810 - acc: 0.9696 - f1: 0.9683 - val_loss: 0.1197 - val_acc: 0.9477 - val_f1: 0.9453\n",
            "Epoch 14/20\n",
            "9451/9451 [==============================] - 5s 483us/step - loss: 0.0752 - acc: 0.9724 - f1: 0.9719 - val_loss: 0.1167 - val_acc: 0.9524 - val_f1: 0.9489\n",
            "Epoch 15/20\n",
            "9451/9451 [==============================] - 5s 498us/step - loss: 0.0697 - acc: 0.9742 - f1: 0.9737 - val_loss: 0.1185 - val_acc: 0.9534 - val_f1: 0.9503\n",
            "Epoch 16/20\n",
            "9451/9451 [==============================] - 5s 492us/step - loss: 0.0642 - acc: 0.9770 - f1: 0.9763 - val_loss: 0.1159 - val_acc: 0.9524 - val_f1: 0.9493\n",
            "Epoch 17/20\n",
            "9451/9451 [==============================] - 5s 478us/step - loss: 0.0616 - acc: 0.9770 - f1: 0.9764 - val_loss: 0.1171 - val_acc: 0.9524 - val_f1: 0.9495\n",
            "Epoch 18/20\n",
            "9451/9451 [==============================] - 5s 493us/step - loss: 0.0568 - acc: 0.9788 - f1: 0.9783 - val_loss: 0.1158 - val_acc: 0.9524 - val_f1: 0.9496\n",
            "Epoch 19/20\n",
            "9451/9451 [==============================] - 5s 496us/step - loss: 0.0529 - acc: 0.9811 - f1: 0.9805 - val_loss: 0.1169 - val_acc: 0.9515 - val_f1: 0.9489\n",
            "Epoch 20/20\n",
            "9451/9451 [==============================] - 5s 508us/step - loss: 0.0479 - acc: 0.9831 - f1: 0.9827 - val_loss: 0.1168 - val_acc: 0.9524 - val_f1: 0.9497\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9r842MbTcsG",
        "colab_type": "code",
        "outputId": "cf295316-d3ec-4818-b97f-f20f5a3dd3c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        }
      },
      "source": [
        "plot_result(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RnDA6gRPSlckJzUQ1VwENEAlEtkBDHbS6/5gRMYE/qvOlrdX3P3+79zuR9VlUHMSmQVwa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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SHVYrMMb0TF3tLP4u8CgwyX08qqrf7+x5InKJiGwTkZ0ico+P7beJyCYR2SAi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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DmBQYPN7n7MNlNWzMLbWL44wxfU4gE8RBYJTX61S3zNu3gJcBVHUdEAkkq2qd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\n4wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BgwJoLAKTdYC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}