Keras_Model_2_TFLite.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Keras_Model_2_TFLite.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jvishnuvardhan/Stackoverflow_Questions/blob/master/Keras_Model_2_TFLite.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfLYsR05XtdB",
        "colab_type": "code",
        "outputId": "c9e8a248-3ccf-4e47-fa4d-fca279761558",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        }
      },
      "source": [
        "!pip install tf-nightly --upgrade"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tf-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5d/72/bd820fe428905d04fe28b64d46037b610a17ea295861db365ea4efdcdea7/tf_nightly-2.2.0.dev20200503-cp36-cp36m-manylinux2010_x86_64.whl (520.8MB)\n",
            "\u001b[K     |████████████████████████████████| 520.8MB 30kB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.3.3)\n",
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            "Requirement already satisfied, skipping upgrade: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.34.2)\n",
            "Requirement already satisfied, skipping upgrade: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: protobuf>=3.9.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (3.10.0)\n",
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            "Requirement already satisfied, skipping upgrade: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (2.10.0)\n",
            "Collecting tf-estimator-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/98/25/3f4c83c0b2ba9ec190fcf9134304136148a83207bb4805eef321536bd7e6/tf_estimator_nightly-2.3.0.dev2020050301-py2.py3-none-any.whl (456kB)\n",
            "\u001b[K     |████████████████████████████████| 460kB 45.0MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.18.3)\n",
            "Collecting tb-nightly<2.4.0a0,>=2.3.0a0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d7/a6/be2a3744fed0ad935f1918ce4b6bf1652a927257058113ec44a02b4d9a9e/tb_nightly-2.3.0a20200503-py3-none-any.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 45.2MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.6.3)\n",
            "Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.9.2->tf-nightly) (46.1.3)\n",
            "Requirement already satisfied, skipping upgrade: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.7.2)\n",
            "Requirement already satisfied, skipping upgrade: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.6.0.post3)\n",
            "Requirement already satisfied, skipping upgrade: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (0.4.1)\n",
            "Requirement already satisfied, skipping upgrade: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.0.1)\n",
            "Requirement already satisfied, skipping upgrade: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.2.1)\n",
            "Requirement already satisfied, skipping upgrade: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (4.0)\n",
            "Requirement already satisfied, skipping upgrade: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (0.2.8)\n",
            "Requirement already satisfied, skipping upgrade: cachetools<3.2,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.1.1)\n",
            "Requirement already satisfied, skipping upgrade: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.3.0)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.0.4)\n",
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            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (2.9)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<4.1,>=3.1.4->google-auth<2,>=1.6.3->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (0.4.8)\n",
            "Requirement already satisfied, skipping upgrade: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.1.0)\n",
            "Installing collected packages: tf-estimator-nightly, tb-nightly, tf-nightly\n",
            "Successfully installed tb-nightly-2.3.0a20200503 tf-estimator-nightly-2.3.0.dev2020050301 tf-nightly-2.2.0.dev20200503\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F6A0KVbVXuxH",
        "colab_type": "code",
        "outputId": "dd372089-fcc3-480f-9191-f37002950e5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "# without the following two lines, it will throw\n",
        "# ValueError: Cannot set tensor: Got value of type NOTYPE but expected type FLOAT32 for input 0, name: flatten_input \n",
        "#x_train = tf.dtypes.cast(x_train,tf.float32)\n",
        "#x_test = tf.dtypes.cast(x_test,tf.float32)\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "  tf.keras.layers.Dense(128, activation='relu'),\n",
        "  tf.keras.layers.Dropout(0.2),\n",
        "  tf.keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train, y_train, epochs=1)\n",
        "model.evaluate(x_test, y_test)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2934 - accuracy: 0.9146\n",
            "313/313 [==============================] - 0s 1ms/step - loss: 0.1414 - accuracy: 0.9564\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.14138390123844147, 0.9563999772071838]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7i6yzZ4XxzI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "converter.experimental_new_converter = True\n",
        "#converter.experimental_enable_mlir_converter = True\n",
        "tflite_model = converter.convert()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHZKnF3FYBE7",
        "colab_type": "code",
        "outputId": "47939c51-e0d5-47d0-d22f-6c06403b55c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "import numpy as np\n",
        "expected = model.predict(x_test[0:1])\n",
        "\n",
        "# Run the model with TensorFlow Lite\n",
        "interpreter = tf.lite.Interpreter(model_content=tflite_model)\n",
        "interpreter.allocate_tensors()\n",
        "input_details = interpreter.get_input_details()\n",
        "output_details = interpreter.get_output_details()\n",
        "interpreter.set_tensor(input_details[0][\"index\"], x_test[0:1, :, :])\n",
        "interpreter.invoke()\n",
        "result = interpreter.get_tensor(output_details[0][\"index\"])\n",
        "\n",
        "# Assert if the result of TFLite model is consistent with the TF model.\n",
        "np.testing.assert_almost_equal(expected, result)\n",
        "print(\"Done. The result of TensorFlow matches the result of TensorFLow Lite.\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-28dc34bdc2c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0minput_details\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterpreter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_input_details\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0moutput_details\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterpreter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_output_details\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0minterpreter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_details\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"index\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0minterpreter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvoke\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterpreter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_details\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"index\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/lite/python/interpreter.py\u001b[0m in \u001b[0;36mset_tensor\u001b[0;34m(self, tensor_index, value)\u001b[0m\n\u001b[1;32m    405\u001b[0m       \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m \u001b[0mthe\u001b[0m \u001b[0minterpreter\u001b[0m \u001b[0mcould\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mset\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m     \"\"\"\n\u001b[0;32m--> 407\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpreter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSetTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mresize_tensor_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Cannot set tensor: Got value of type NOTYPE but expected type FLOAT32 for input 0, name: flatten_input "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTZb2lkfYEg3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}