TF_LSTM_Litemodel.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TF_LSTM_Litemodel.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNWyeXph3UtNvj0tLGvIRsD",
      "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/TF_LSTM_Litemodel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZkWvxzvYiIXP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        },
        "outputId": "f19fb7e6-4977-47bd-b6bc-389e5ab3a60f"
      },
      "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/c7/ff/e362223206b7f9b403bbd5fa1c54cdc19d9f581266ad951ca2ba0183f78f/tf_nightly-2.2.0.dev20200407-cp36-cp36m-manylinux2010_x86_64.whl (517.2MB)\n",
            "\u001b[K     |████████████████████████████████| 517.2MB 30kB/s \n",
            "\u001b[?25hCollecting tf-estimator-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f4/39/5b893818cd3deae62a22434b360de3c7ce76573c5dcda0e54d0defefcde9/tf_estimator_nightly-2.3.0.dev2020040701-py2.py3-none-any.whl (455kB)\n",
            "\u001b[K     |████████████████████████████████| 460kB 48.8MB/s \n",
            "\u001b[?25hRequirement 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: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.6.3)\n",
            "Requirement already satisfied, skipping upgrade: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (3.2.0)\n",
            "Requirement 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: protobuf>=3.9.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (3.10.0)\n",
            "Requirement already satisfied, skipping upgrade: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.27.2)\n",
            "Collecting tb-nightly<2.4.0a0,>=2.3.0a0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a6/63/a0b69e4c814581e52bfa76ec456fe7b910242b9497d8d57732ac1729da53/tb_nightly-2.3.0a20200406-py3-none-any.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 38.7MB/s \n",
            "\u001b[?25hRequirement 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",
            "Requirement already satisfied, skipping upgrade: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.3.3)\n",
            "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: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.18.2)\n",
            "Requirement already satisfied, skipping upgrade: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.9.0)\n",
            "Requirement already satisfied, skipping upgrade: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.2.0)\n",
            "Requirement already satisfied, skipping upgrade: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.12.1)\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: 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: 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: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (2.21.0)\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.post2)\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: 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: 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: 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: 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: urllib3<1.25,>=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: chardet<3.1.0,>=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",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly<2.4.0a0,>=2.3.0a0->tf-nightly) (2019.11.28)\n",
            "Requirement already satisfied, skipping upgrade: idna<2.9,>=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) (2.8)\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: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->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-2.3.0a20200406 tf-estimator-nightly-2.3.0.dev2020040701 tf-nightly-2.2.0.dev20200407\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6CK-TdiiKXA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8IcqKVAjACs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "734b1cdc-586e-4ba4-841d-952181365d35"
      },
      "source": [
        "# Step 1: Build the MNIST LSTM model\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Input(shape=(28, 28), name='input'),\n",
        "    tf.keras.layers.LSTM(20),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(10, activation=tf.nn.softmax, name='output')\n",
        "])\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 20)                3920      \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 20)                0         \n",
            "_________________________________________________________________\n",
            "output (Dense)               (None, 10)                210       \n",
            "=================================================================\n",
            "Total params: 4,130\n",
            "Trainable params: 4,130\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQ0o3EDOjDeq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "91bb91a6-eb9f-48d7-dbf4-7415241ccab7"
      },
      "source": [
        "#2 Step 2: Train & Evaluate the model\n",
        "# Load MNIST dataset.\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "x_train = x_train.astype(np.float32)\n",
        "x_test = x_test.astype(np.float32)\n",
        "\n",
        "# Change this to True if you want to test the flow rapidly.\n",
        "# Train with a small dataset and only 1 epoch. The model will work poorly\n",
        "# but this provides a fast way to test if the conversion works end to end.\n",
        "_FAST_TRAINING = False\n",
        "_EPOCHS = 5\n",
        "if _FAST_TRAINING:\n",
        "  _EPOCHS = 1\n",
        "  _TRAINING_DATA_COUNT = 1000\n",
        "  x_train = x_train[:_TRAINING_DATA_COUNT]\n",
        "  y_train = y_train[:_TRAINING_DATA_COUNT]\n",
        "\n",
        "model.fit(x_train, y_train, epochs=_EPOCHS)\n",
        "model.evaluate(x_test, y_test, verbose=0)"
      ],
      "execution_count": 4,
      "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 [==============================] - 13s 7ms/step - loss: 0.8305 - accuracy: 0.7394\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 12s 7ms/step - loss: 0.3034 - accuracy: 0.9111\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.2066 - accuracy: 0.9396\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.1624 - accuracy: 0.9527\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.1375 - accuracy: 0.9597\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.14083439111709595, 0.9570000171661377]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNzA_gAZjjt8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Step 3: Convert the Keras model to TensorFlow Lite model\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
        "# Note: It will NOT work without enabling the experimental converter!\n",
        "# `experimental_new_converter` flag.\n",
        "converter.experimental_new_converter = True\n",
        "tflite_model = converter.convert()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MYbWYg-js1a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pathlib\n",
        "tflite_models_dir = pathlib.Path(\"./lstm_tflite_models/\")\n",
        "tflite_models_dir.mkdir(exist_ok=True, parents=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OocZ3pjdovo5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "037b6dbc-88de-449d-c3e6-f9f9b7b11546"
      },
      "source": [
        "tflite_model_file = tflite_models_dir/\"lstm_model.tflite\"\n",
        "tflite_model_file.write_bytes(tflite_model)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28756"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjvBLoG9pFWo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "65dba420-e4e1-4999-eea9-fe60e601e84e"
      },
      "source": [
        "# Post training quantization\n",
        "converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n",
        "tflite_quant_model = converter.convert()\n",
        "tflite_model_quant_file = tflite_models_dir/\"lstm_model_quant.tflite\"\n",
        "tflite_model_quant_file.write_bytes(tflite_quant_model)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18672"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SB4UfRlpVNK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "34acc3dc-dfc6-408e-b5f6-380b24838b98"
      },
      "source": [
        "ls -lh {tflite_models_dir}"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 52K\n",
            "-rw-r--r-- 1 root root 19K Apr  7 17:17 lstm_model_quant.tflite\n",
            "-rw-r--r-- 1 root root 29K Apr  7 17:16 lstm_model.tflite\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYsb9R2fpaYk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HuadZFL1rLVx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "files.download(tflite_model_quant_file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j27Eb0lmrVzg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "files.download(tflite_model_file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2na4-Nnrjw2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}