TFlite_from_keras_model_2_0.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TFlite_from_lkeras_model_2_0.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOlFseGihlD1jI0V+krqd6d",
      "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/TF_Lite/blob/master/TFlite_from_keras_model_2_0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZK8Mym6R4c7k",
        "colab_type": "code",
        "outputId": "9b06e601-3040-4762-98c0-e923d56292ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        }
      },
      "source": [
        "!pip install tf-nightly"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tf-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/af/39569cb00a299ba51dbf8083169eeb18dc4b88f3fcd4b1616fc42124c813/tf_nightly-2.2.0.dev20200505-cp36-cp36m-manylinux2010_x86_64.whl (520.8MB)\n",
            "\u001b[K     |████████████████████████████████| 520.8MB 30kB/s \n",
            "\u001b[?25hRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (3.2.1)\n",
            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.3.3)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.12.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.18.3)\n",
            "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.6.3)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.1.0)\n",
            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.34.2)\n",
            "Collecting tf-estimator-nightly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4f/81/ba2b124749b7c48a58e65dd8f36c326dc7ab327e5afa9cef723a12464ec0/tf_estimator_nightly-2.3.0.dev2020050501-py2.py3-none-any.whl (456kB)\n",
            "\u001b[K     |████████████████████████████████| 460kB 34.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (3.10.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.28.1)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (1.12.1)\n",
            "Collecting tb-nightly<2.4.0a0,>=2.3.0a0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/41/e2/83721958b6cc33cab3017029556e2864879a66b8a87a6443c7b1bc0cc6e8/tb_nightly-2.3.0a20200505-py3-none-any.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 44.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.9.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tf-nightly) (0.2.0)\n",
            "Requirement already satisfied: 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: 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: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.9.2->tf-nightly) (46.1.3)\n",
            "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",
            "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: 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: 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: 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: 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: 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: 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) (2020.4.5.1)\n",
            "Requirement already satisfied: 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",
            "Requirement already satisfied: 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: 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: 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: 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: 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: 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: 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-2.3.0a20200505 tf-estimator-nightly-2.3.0.dev2020050501 tf-nightly-2.2.0.dev20200505\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-c3VZfy4hS2",
        "colab_type": "code",
        "outputId": "52ff2bfa-0fe9-4547-eac9-2daa4d009596",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.2.0-dev20200505'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rGmvoXc47lX",
        "colab_type": "code",
        "outputId": "eace57d8-2ddf-47c0-8932-9c628aac2baa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "!unzip saved_model.zip -d saved_model"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  saved_model.zip\n",
            "   creating: saved_model/content/saved_model/\n",
            "   creating: saved_model/content/saved_model/my_model/\n",
            "   creating: saved_model/content/saved_model/my_model/variables/\n",
            "  inflating: saved_model/content/saved_model/my_model/variables/variables.data-00000-of-00002  \n",
            "  inflating: saved_model/content/saved_model/my_model/variables/variables.index  \n",
            "  inflating: saved_model/content/saved_model/my_model/variables/variables.data-00001-of-00002  \n",
            "  inflating: saved_model/content/saved_model/my_model/saved_model.pb  \n",
            "   creating: saved_model/content/saved_model/my_model/assets/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tN74NoM94fEC",
        "colab_type": "code",
        "outputId": "2ca588d1-5096-4c9e-c80f-85392cd110a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tflite_model = tf.keras.models.load_model('/content/saved_model/content/saved_model/my_model')\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(tflite_model)\n",
        "tflite_save = converter.convert()\n",
        "open(\"greek_smart_reply_model.tflite\", \"wb\").write(tflite_save)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1083272"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "319CC1KM5n3M",
        "colab_type": "code",
        "outputId": "b7df1a7e-14b5-46a8-bd3f-41ce40746e0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "tflite_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 40, 20)            245560    \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 64)                21760     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 3)                 195       \n",
            "=================================================================\n",
            "Total params: 267,515\n",
            "Trainable params: 267,515\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dRq9s26D5S8H",
        "colab_type": "code",
        "outputId": "d0a7b5c5-0b50-44bd-8d1b-58c5b0f4eab6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#print(tflite_model.get_input_shape_at(0))\n",
        "print(tflite_model)\n",
        "for node in tflite_model.outputs:\n",
        "  print(node)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tensorflow.python.keras.engine.sequential.Sequential object at 0x7fe69dac0c88>\n",
            "Tensor(\"dense/Identity:0\", shape=(None, 3), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1F5EqV2i5TML",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load TFLite model and allocate tensors.\n",
        "interpreter = tf.lite.Interpreter('greek_smart_reply_model.tflite')\n",
        "interpreter.allocate_tensors()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "071Wqp0k8Hk9",
        "colab_type": "code",
        "outputId": "06e2ca1f-da2e-4bfb-c0ae-86a89696652f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "# Get input and output tensors.\n",
        "input_details = interpreter.get_input_details()\n",
        "print(input_details)\n",
        "output_details = interpreter.get_output_details()\n",
        "print(output_details)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[{'name': 'embedding_input', 'index': 0, 'shape': array([ 1, 40], dtype=int32), 'shape_signature': array([-1, 40], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n",
            "[{'name': 'Identity', 'index': 38, 'shape': array([1, 3], dtype=int32), 'shape_signature': array([-1,  3], dtype=int32), 'dtype': <class 'numpy.float32'>, 'quantization': (0.0, 0), 'quantization_parameters': {'scales': array([], dtype=float32), 'zero_points': array([], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_Ju_TnZ65mm",
        "colab_type": "code",
        "outputId": "59a58d6b-5781-42a7-d6c3-4051f474f710",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "interpreter.get_tensor_details()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'dtype': numpy.float32,\n",
              "  'index': 0,\n",
              "  'name': 'embedding_input',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([ 1, 40], dtype=int32),\n",
              "  'shape_signature': array([-1, 40], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 1,\n",
              "  'name': 'sequential/dense/BiasAdd/ReadVariableOp/resource',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([3], dtype=int32),\n",
              "  'shape_signature': array([3], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 2,\n",
              "  'name': 'sequential/embedding/embedding_lookup/7264',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([12278,    20], dtype=int32),\n",
              "  'shape_signature': array([12278,    20], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 3,\n",
              "  'name': 'transpose/perm;sequential/lstm/PartitionedCall/transpose/perm',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([3], dtype=int32),\n",
              "  'shape_signature': array([3], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 4,\n",
              "  'name': 'sequential/lstm/Read_2/ReadVariableOp/resource',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([256], dtype=int32),\n",
              "  'shape_signature': array([256], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 5,\n",
              "  'name': 'sequential/lstm/zeros/packed/1',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([], dtype=int32),\n",
              "  'shape_signature': array([], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 6,\n",
              "  'name': 'TensorArrayV2_1;sequential/lstm/PartitionedCall/TensorArrayV2_1',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([2], dtype=int32),\n",
              "  'shape_signature': array([2], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 7,\n",
              "  'name': 'TensorArrayV2_1;sequential/lstm/PartitionedCall/TensorArrayV2_11',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([], dtype=int32),\n",
              "  'shape_signature': array([], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 8,\n",
              "  'name': 'TensorArrayV2_1;sequential/lstm/PartitionedCall/TensorArrayV2_12',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([], dtype=int32),\n",
              "  'shape_signature': array([], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 9,\n",
              "  'name': 'sequential/dense/MatMul',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([ 3, 64], dtype=int32),\n",
              "  'shape_signature': array([ 3, 64], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 10,\n",
              "  'name': 'sequential/lstm/strided_slice',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([1], dtype=int32),\n",
              "  'shape_signature': array([1], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 11,\n",
              "  'name': 'sequential/lstm/strided_slice1',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([1], dtype=int32),\n",
              "  'shape_signature': array([1], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 12,\n",
              "  'name': 'strided_slice_2;sequential/lstm/PartitionedCall/strided_slice_2',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([3], dtype=int32),\n",
              "  'shape_signature': array([3], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 13,\n",
              "  'name': 'strided_slice_2;sequential/lstm/PartitionedCall/strided_slice_21',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([3], dtype=int32),\n",
              "  'shape_signature': array([3], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 14,\n",
              "  'name': 'strided_slice_2;sequential/lstm/PartitionedCall/strided_slice_22',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([3], dtype=int32),\n",
              "  'shape_signature': array([3], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 15,\n",
              "  'name': 'sequential/embedding/Cast',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([ 1, 40], dtype=int32),\n",
              "  'shape_signature': array([-1, 40], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 16,\n",
              "  'name': 'sequential/embedding/embedding_lookup;TensorArrayV2_1;sequential/lstm/PartitionedCall/TensorArrayV2_1',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([ 1, 40, 20], dtype=int32),\n",
              "  'shape_signature': array([-1, 40, 20], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 17,\n",
              "  'name': 'sequential/lstm/Shape',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([3], dtype=int32),\n",
              "  'shape_signature': array([3], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 18,\n",
              "  'name': 'transpose;sequential/lstm/PartitionedCall/transpose',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([40,  1, 20], dtype=int32),\n",
              "  'shape_signature': array([40, -1, 20], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 19,\n",
              "  'name': 'Shape;sequential/lstm/PartitionedCall/Shape',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([3], dtype=int32),\n",
              "  'shape_signature': array([3], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 20,\n",
              "  'name': 'strided_slice;sequential/lstm/PartitionedCall/strided_slice',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([], dtype=int32),\n",
              "  'shape_signature': array([], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 21,\n",
              "  'name': 'TensorArrayV2_1;sequential/lstm/PartitionedCall/TensorArrayV2_13',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([1], dtype=int32),\n",
              "  'shape_signature': array([1], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 22,\n",
              "  'name': 'TensorArrayV2_1;sequential/lstm/PartitionedCall/TensorArrayV2_14',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([3], dtype=int32),\n",
              "  'shape_signature': array([3], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 23,\n",
              "  'name': 'TensorArrayV2_1;sequential/lstm/PartitionedCall/TensorArrayV2_15',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([1, 1, 1], dtype=int32),\n",
              "  'shape_signature': array([-1, -1, -1], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 24,\n",
              "  'name': 'sequential/lstm/strided_slice2',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([], dtype=int32),\n",
              "  'shape_signature': array([], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 25,\n",
              "  'name': 'sequential/lstm/zeros/packed',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([2], dtype=int32),\n",
              "  'shape_signature': array([2], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 26,\n",
              "  'name': 'sequential/lstm/zeros',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([ 1, 64], dtype=int32),\n",
              "  'shape_signature': array([-1, 64], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 27,\n",
              "  'name': 'while;sequential/lstm/PartitionedCall/while',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([], dtype=int32),\n",
              "  'shape_signature': array([], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 28,\n",
              "  'name': 'while;sequential/lstm/PartitionedCall/while1',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([], dtype=int32),\n",
              "  'shape_signature': array([], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 29,\n",
              "  'name': 'while;sequential/lstm/PartitionedCall/while2',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([1, 1, 1], dtype=int32),\n",
              "  'shape_signature': array([-1, -1, -1], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 30,\n",
              "  'name': 'while;sequential/lstm/PartitionedCall/while3',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([ 1, 64], dtype=int32),\n",
              "  'shape_signature': array([-1, 64], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 31,\n",
              "  'name': 'while;sequential/lstm/PartitionedCall/while4',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([ 1, 64], dtype=int32),\n",
              "  'shape_signature': array([-1, 64], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 32,\n",
              "  'name': 'while;sequential/lstm/PartitionedCall/while5',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([], dtype=int32),\n",
              "  'shape_signature': array([], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 33,\n",
              "  'name': 'while;sequential/lstm/PartitionedCall/while6',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([40,  1, 20], dtype=int32),\n",
              "  'shape_signature': array([40, -1, 20], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.int32,\n",
              "  'index': 34,\n",
              "  'name': 'TensorArrayV2Stack/TensorListStack;sequential/lstm/PartitionedCall/TensorArrayV2Stack/TensorListStack',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([3], dtype=int32),\n",
              "  'shape_signature': array([3], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 35,\n",
              "  'name': 'TensorArrayV2Stack/TensorListStack;sequential/lstm/PartitionedCall/TensorArrayV2Stack/TensorListStack1',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([1, 1, 1], dtype=int32),\n",
              "  'shape_signature': array([-1, -1, -1], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 36,\n",
              "  'name': 'strided_slice_2;sequential/lstm/PartitionedCall/strided_slice_23',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([1, 1], dtype=int32),\n",
              "  'shape_signature': array([-1, -1], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 37,\n",
              "  'name': 'sequential/dense/BiasAdd',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([1, 3], dtype=int32),\n",
              "  'shape_signature': array([-1,  3], dtype=int32),\n",
              "  'sparsity_parameters': {}},\n",
              " {'dtype': numpy.float32,\n",
              "  'index': 38,\n",
              "  'name': 'Identity',\n",
              "  'quantization': (0.0, 0),\n",
              "  'quantization_parameters': {'quantized_dimension': 0,\n",
              "   'scales': array([], dtype=float32),\n",
              "   'zero_points': array([], dtype=int32)},\n",
              "  'shape': array([1, 3], dtype=int32),\n",
              "  'shape_signature': array([-1,  3], dtype=int32),\n",
              "  'sparsity_parameters': {}}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    }
  ]
}