tflite_simple_floatmodel.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "tflite_simple_floatmodel.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMUUGJbQPgl3+gg96vk93ug",
      "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/tflite_simple_floatmodel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CsKQIbsEvGwa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "outputId": "986854f3-101c-4421-a94f-bd51c9d7e31c"
      },
      "source": [
        "!pip install tf-nightly"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tf-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/54/ad/9150ee6d50dfcf0bd672e27928f97ed1a20d74bbba3a7fd3e63ea0da1444/tf_nightly-2.3.0.dev20200613-cp36-cp36m-manylinux2010_x86_64.whl (350.2MB)\n",
            "\u001b[K     |████████████████████████████████| 350.2MB 45kB/s \n",
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            "Collecting tf-estimator-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8e/22/6fd1e108eef3ae43ee194d2089fca0221e6a8729293410ebb35fa6a119ac/tf_estimator_nightly-2.3.0.dev2020061301-py2.py3-none-any.whl (459kB)\n",
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            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.1.0)\n",
            "Collecting tb-nightly<2.4.0a0,>=2.3.0a0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b9/c8/8abbb41da33322bb30090900c537c2dc46b1fa86edb66d14b5aa1f8b344c/tb_nightly-2.3.0a20200613-py3-none-any.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0MB 38.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: 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: 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",
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            "Requirement already satisfied: 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: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (1.6.0)\n",
            "Requirement already satisfied: 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",
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            "Requirement already satisfied: 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: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (3.1.0)\n",
            "Requirement already satisfied: 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",
            "Requirement already satisfied: 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",
            "Installing collected packages: tf-estimator-nightly, tb-nightly, tf-nightly\n",
            "Successfully installed tb-nightly-2.3.0a20200613 tf-estimator-nightly-2.3.0.dev2020061301 tf-nightly-2.3.0.dev20200613\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdG8QzNuvT0A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "d085021d-6cc7-4426-a6e5-8b316ea6c2c1"
      },
      "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",
        "\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=5)\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",
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2935 - accuracy: 0.9131\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1403 - accuracy: 0.9574\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1049 - accuracy: 0.9679\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0871 - accuracy: 0.9731\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0724 - accuracy: 0.9767\n",
            "313/313 [==============================] - 0s 1ms/step - loss: 0.0740 - accuracy: 0.9764\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0740458220243454, 0.9764000177383423]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuaAZ_6evURN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('my_model.h5',save_format='h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsRBwrGDvbY5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "outputId": "d3202a5f-5089-479e-9e1e-382bc273a642"
      },
      "source": [
        "# Converting a SavedModel to a TensorFlow Lite model. \n",
        "saved_model_dir = '/content/my_model.h5' #r\"C:\\Users\\Munib\\New folder\" \n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) \n",
        "tflite_model = converter.convert()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-a9a46a590f90>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Converting a SavedModel to a TensorFlow Lite model.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msaved_model_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/content/my_model.h5'\u001b[0m \u001b[0;31m#r\"C:\\Users\\Munib\\New folder\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mconverter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTFLiteConverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_saved_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msaved_model_dir\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      4\u001b[0m \u001b[0mtflite_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\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/lite.py\u001b[0m in \u001b[0;36mfrom_saved_model\u001b[0;34m(cls, saved_model_dir, signature_keys, tags)\u001b[0m\n\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meager_mode\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[0;32m--> 975\u001b[0;31m       \u001b[0msaved_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msaved_model_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtags\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    976\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignature_keys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    977\u001b[0m       \u001b[0msignature_keys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msaved_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignatures\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/python/saved_model/load.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(export_dir, tags)\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mtags\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mdon\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0mt\u001b[0m \u001b[0mmatch\u001b[0m \u001b[0ma\u001b[0m \u001b[0mMetaGraph\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mSavedModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    581\u001b[0m   \"\"\"\n\u001b[0;32m--> 582\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mload_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexport_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtags\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    583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/load.py\u001b[0m in \u001b[0;36mload_internal\u001b[0;34m(export_dir, tags, loader_cls)\u001b[0m\n\u001b[1;32m    590\u001b[0m     \u001b[0mtags\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m   saved_model_proto, debug_info = (\n\u001b[0;32m--> 592\u001b[0;31m       loader_impl.parse_saved_model_with_debug_info(export_dir))\n\u001b[0m\u001b[1;32m    593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m   if (len(saved_model_proto.meta_graphs) == 1 and\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/loader_impl.py\u001b[0m in \u001b[0;36mparse_saved_model_with_debug_info\u001b[0;34m(export_dir)\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0mparsed\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mMissing\u001b[0m \u001b[0mgraph\u001b[0m \u001b[0mdebug\u001b[0m \u001b[0minfo\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mfine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m   \"\"\"\n\u001b[0;32m---> 56\u001b[0;31m   \u001b[0msaved_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_parse_saved_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexport_dir\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     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m   debug_info_path = os.path.join(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/saved_model/loader_impl.py\u001b[0m in \u001b[0;36mparse_saved_model\u001b[0;34m(export_dir)\u001b[0m\n\u001b[1;32m    111\u001b[0m                   (export_dir,\n\u001b[1;32m    112\u001b[0m                    \u001b[0mconstants\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSAVED_MODEL_FILENAME_PBTXT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m                    constants.SAVED_MODEL_FILENAME_PB))\n\u001b[0m\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: SavedModel file does not exist at: /content/my_model.h5/{saved_model.pbtxt|saved_model.pb}"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34wrKGhHv9K3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "f6a24832-ce46-4562-a5c1-e7c373969c6a"
      },
      "source": [
        "# Converting a SavedModel to a TensorFlow Lite model. \n",
        "saved_model_dir = '/content/my_model.h5' #r\"C:\\Users\\Munib\\New folder\" \n",
        "loaded_model = tf.keras.models.load_model(saved_model_dir)\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(loaded_model)# .from_saved_model(saved_model_dir) \n",
        "tflite_model = converter.convert()\n",
        "open(\"converted_model.tflite\", \"wb\").write(tflite_model)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:110: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:110: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "INFO:tensorflow:Assets written to: /tmp/tmpn5j661tx/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9oZSnmYiwbHh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "93d465b7-d156-4bbc-b282-858a1cf99bdf"
      },
      "source": [
        "open(\"converted_model.tflite\", \"wb\").write(tflite_model)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "408668"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueVpg1OPwsfY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}