Embedding_Preproc_Functions_in_Models.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Embedding_Preproc_Functions_in_Models.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLWasKtJjtWE",
        "colab_type": "text"
      },
      "source": [
        "# Embedding an image preprocessing function in a `tf.keras` model\n",
        "> Learn how to embed an image preprocessing function in a `tf.keras` model.\n",
        "\n",
        "- toc:true\n",
        "- branch: master\n",
        "- badges: true\n",
        "- hide_binder_badge: true\n",
        "- comments: true\n",
        "- author: Sayak Paul\n",
        "- categories: [tf.keras, preprocessing]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FMaEPAAgd34H",
        "colab_type": "text"
      },
      "source": [
        "In this tutorial, we are going to see how to embed a simple image preprocessing function within a _trained model_ (`tf.keras`) while exporting it for serving. This is a useful feature to have because it can help us reduce a lot of boilerplate code needed while using any model for serving purposes. With this capability, you get a lot more flexibility and modularity to your model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-tvVJgfPRlS",
        "colab_type": "code",
        "outputId": "84f8d98a-fbd9-47e3-a38e-8007d1009994",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#hide\n",
        "# Import TenosrFlow and check version\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-rc2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zuIMQEe-Pj1j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#hide\n",
        "# Other imports\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import cv2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OK2wra7KkRwz",
        "colab_type": "text"
      },
      "source": [
        "## Data loading, ~preprocessing~, and visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Wi0AKn0kELA",
        "colab_type": "text"
      },
      "source": [
        "To keep things simple we will be using the FashionMNIST dataset. Note that these techniques can easily be applied to more complex models as well (with some limitation). \n",
        "\n",
        "> We are **not** going to preprocess the images before hand. We will let the model do it. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dUFgKur1PxRR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load data\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxP5y23nQCKe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Class labels (don't change the order)\n",
        "CLASSES = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\",\n",
        "               \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZjxBB-JP6-s",
        "colab_type": "code",
        "outputId": "184b8098-f227-492d-cb8d-65e50f832bdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "# Show a few examples from the train set\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(True)\n",
        "    plt.imshow(x_train[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(CLASSES[y_train[i]])\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iC0ueHzwkVbv",
        "colab_type": "text"
      },
      "source": [
        "## Model building and training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "635lm-mQkbNS",
        "colab_type": "text"
      },
      "source": [
        "We are good to proceed towards building and training a neural network. We will first define a simple preprocessing function to scale the pixel values and then we will embed it into the model using a [`Lambda` layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Lambda). You can replace this anything fancy you would want. \n",
        "\n",
        "We will use a shallow network architecture so that we can train it quickly. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUnTPvBVQKTb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define the preprocessing function\n",
        "# We will embed it in the model later\n",
        "def preprocess_image(image_pixels):\n",
        "    img = image_pixels / 255\n",
        "    return img\n",
        "\n",
        "# A humble model\n",
        "def get_training_model():\n",
        "    # Construct the model using the Functional API\n",
        "    input_layer = tf.keras.layers.Input(shape=(28, 28), name=\"input_layer\")\n",
        "    preproc_layer = tf.keras.layers.Lambda(preprocess_image, name=\"lambda_layer\")(input_layer) # Preprocessing function\n",
        "    flatten = tf.keras.layers.Flatten()(preproc_layer)\n",
        "    dense_1 = tf.keras.layers.Dense(128, activation=\"relu\")(flatten)\n",
        "    dropout = tf.keras.layers.Dropout(0.2)(dense_1)\n",
        "    outputs = tf.keras.layers.Dense(len(CLASSES), activation=\"softmax\")(dropout)\n",
        "\n",
        "    # Create the model\n",
        "    model = tf.keras.models.Model(input_layer, outputs)\n",
        "\n",
        "    # Compile the model and return it\n",
        "    model.compile(optimizer='adam',\n",
        "                loss='sparse_categorical_crossentropy',\n",
        "                metrics=['accuracy'])\n",
        "        \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LHI50SxMKeDg",
        "colab_type": "code",
        "outputId": "5ceb1291-0c2f-45bc-9faf-4cd29bbabdcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        }
      },
      "source": [
        "# Topology of the model\n",
        "tf.keras.utils.plot_model(get_training_model(), show_shapes=True)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JIx9bj5PKr0V",
        "colab_type": "text"
      },
      "source": [
        "The `Lambda` layer is our preprocessing layer. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsvsIX27QfNs",
        "colab_type": "code",
        "outputId": "e2150e7d-15c2-46d5-c386-259c91b8f980",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "# Train the model for 10 epochs\n",
        "apparel_model = get_training_model()\n",
        "history = apparel_model.fit(x_train, y_train, \n",
        "    validation_data=(x_test, y_test), \n",
        "    epochs=10, \n",
        "    batch_size=128)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.6004 - accuracy: 0.7937 - val_loss: 0.4682 - val_accuracy: 0.8347\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.4246 - accuracy: 0.8495 - val_loss: 0.4089 - val_accuracy: 0.8521\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.3795 - accuracy: 0.8642 - val_loss: 0.3928 - val_accuracy: 0.8564\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.3576 - accuracy: 0.8711 - val_loss: 0.3632 - val_accuracy: 0.8687\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.3407 - accuracy: 0.8762 - val_loss: 0.3593 - val_accuracy: 0.8688\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.3294 - accuracy: 0.8788 - val_loss: 0.3532 - val_accuracy: 0.8721\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.3165 - accuracy: 0.8846 - val_loss: 0.3609 - val_accuracy: 0.8685\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.3084 - accuracy: 0.8859 - val_loss: 0.3503 - val_accuracy: 0.8701\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.2982 - accuracy: 0.8915 - val_loss: 0.3560 - val_accuracy: 0.8713\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.2886 - accuracy: 0.8929 - val_loss: 0.3381 - val_accuracy: 0.8776\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7UEkghaK26e",
        "colab_type": "text"
      },
      "source": [
        "Now that we have a trained model, we can go ahead and export it and then we will see how to use it on new images for inference. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bWPixPAVlA7C",
        "colab_type": "text"
      },
      "source": [
        "## Sample test image and model export"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCxiOq0ylE88",
        "colab_type": "text"
      },
      "source": [
        "We are getting close. Now that we have a trained model here are the things we would do from here:\n",
        "- Serialize a randomly selected image from the test set.  \n",
        "- Export the model and parse model predictions. \n",
        "\n",
        "Let's go."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LS2FkJsrlzoL",
        "colab_type": "text"
      },
      "source": [
        "### Step 1: Serializing a randomly selected image from the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iaPzSvXQyTh",
        "colab_type": "code",
        "outputId": "60b5fec1-21de-4482-da04-23d71532b2a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Select a random image from the test set for serialization\n",
        "sampe_test_img_id = np.random.choice(x_test.shape[0], 1)\n",
        "sampe_test_img = x_test[sampe_test_img_id].squeeze() # Remove the batch dimension\n",
        "sampe_test_img = (sampe_test_img * 255).astype(\"int32\") # Scale back to integer\n",
        "\n",
        "# Verify image label and shape\n",
        "print(\"Image class: \",CLASSES[y_test[int(sampe_test_img_id)]])\n",
        "print(sampe_test_img.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image class:  Ankle boot\n",
            "(28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RA1ALdEwRNMw",
        "colab_type": "code",
        "outputId": "a6284755-df1a-42ee-b68b-00549b695662",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#hide_output\n",
        "# Serialize the image\n",
        "cv2.imwrite(\"sample_image.png\", sampe_test_img)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TmulPVCDmDRV",
        "colab_type": "text"
      },
      "source": [
        "Note that while writing a grayscale image, [OpenCV adds the channel dimension of 3 to it](https://stackoverflow.com/questions/18870603/in-opencv-python-why-am-i-getting-3-channel-images-from-a-grayscale-image). We will need to handle carefully. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPADGjjpRbal",
        "colab_type": "code",
        "outputId": "1ffde99a-9854-4db1-dfe4-f42a756dc4dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# Make sure the serialized image is good to go\n",
        "plt.imshow(plt.imread(\"sample_image.png\"), cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cuRtlD8XLVvU",
        "colab_type": "text"
      },
      "source": [
        "### Step 2: Model exporting and parsing predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pDQxuC2zLkcc",
        "colab_type": "text"
      },
      "source": [
        "Let's first serialize our model and load it. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwUYec6RLekF",
        "colab_type": "code",
        "outputId": "41ad14a0-c94c-4362-8552-32c109559ce9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Serialize the model and load it\n",
        "apparel_model.save(\"apparel_model.h5\")\n",
        "restored_model = tf.keras.models.load_model(\"apparel_model.h5\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VEMhEf6NL3jN",
        "colab_type": "text"
      },
      "source": [
        "This warning is not desirable. When the optimizer is loaded with a fresh state, the model predictions can be erroneous. So, to resolve this problem we will only be serializing the weights of the model with the `save_weights()` function. There can be other nuances like this when you work with `Lambda` layers and you can check [this article](https://blog.paperspace.com/working-with-the-lambda-layer-in-keras/#saving-and-loading-a-model-with-a-lambda-layer) out to know about them. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WiEFKhY8NF7Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "apparel_model.save_weights(\"apparel_model.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R0GCBdehNOOt",
        "colab_type": "text"
      },
      "source": [
        "We will now initialize a dummy model with the same architecture as the one we trained and we will then load the weights of our trained model into it. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HfyyYPNDNNWP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "restored_model = get_training_model()\n",
        "restored_model.load_weights(\"apparel_model.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2h8rztmNhut",
        "colab_type": "text"
      },
      "source": [
        "Now we should be good to go with the predictions part. First, let's load the image we serialized in step 1. As mentioned before, OpenCV adds 3-channels to grayscale images while saving them. We can take care of this issue with `cv2.cvtColor(image_pixels, cv2.COLOR_BGR2GRAY)`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZrkeXePNg5S",
        "colab_type": "code",
        "outputId": "d2655f40-476e-4765-d579-7a472410d5c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# Load the image\n",
        "image_pixels = cv2.imread(\"sample_image.png\")\n",
        "image_pixels = cv2.cvtColor(image_pixels, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "# Preview the image\n",
        "plt.imshow(image_pixels, cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WANttaGDOmmd",
        "colab_type": "code",
        "outputId": "9ca64126-87b3-4a01-9ce6-0f8d21a23aa2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Run inference and parse the prediction\n",
        "class_probabilities = restored_model.predict(np.expand_dims(image_pixels, 0))[0]\n",
        "print(\"Predicted \",CLASSES[np.argmax(class_probabilities)])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted  Ankle boot\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfvz1U6PPHZJ",
        "colab_type": "text"
      },
      "source": [
        "We can see that it is working as expected. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufpBlQsH57a6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load and *preprocess* data\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n",
        "x_train = x_train / 255\n",
        "x_test = x_test / 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_oSsL3Ta_7_u",
        "colab_type": "text"
      },
      "source": [
        "## Taking it a step further with concrete functions and `SavedModel`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bBuXJXXoAFtY",
        "colab_type": "text"
      },
      "source": [
        "The [`SavedModel`](https://www.tensorflow.org/guide/saved_model) format is the standard serialization format in TensorFlow 2.x since it communicates very well with the entire TensorFlow ecosystem. Be it GCP AI Platform, be it `tf.keras`, be it TFLite, etc,, `SavedModel` format unifies the entire ecosystem. For serializing custom models (developed using [subclassing](https://www.pyimagesearch.com/2019/10/28/3-ways-to-create-a-keras-model-with-tensorflow-2-0-sequential-functional-and-model-subclassing/)) `SavedModel` would be needed as well. \n",
        "\n",
        "In this section, let's see how can we do the same i.e. embed a preprocessing function inside a model so that it can be serialized in the `SavedModel` format. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bb5ja75TBLCh",
        "colab_type": "text"
      },
      "source": [
        "### Step 1: Create a sequential model _without_ any preprocessing layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzjNXOx-5xHa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_training_model_v2():\n",
        "    # Construct the model using the Functional API\n",
        "    input_layer = tf.keras.layers.Input(shape=(28, 28), name=\"input_layer\")\n",
        "    flatten = tf.keras.layers.Flatten()(input_layer)\n",
        "    dense_1 = tf.keras.layers.Dense(128, activation=\"relu\")(flatten)\n",
        "    dropout = tf.keras.layers.Dropout(0.2)(dense_1)\n",
        "    outputs = tf.keras.layers.Dense(len(CLASSES), activation=\"softmax\")(dropout)\n",
        "\n",
        "    # Create the model\n",
        "    model = tf.keras.models.Model(input_layer, outputs)\n",
        "\n",
        "    # Compile the model and return it\n",
        "    model.compile(optimizer=\"adam\",\n",
        "                loss=\"sparse_categorical_crossentropy\",\n",
        "                metrics=[\"accuracy\"])\n",
        "        \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VFviz0ABBhDl",
        "colab_type": "text"
      },
      "source": [
        "### Step 2: Train it!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ef3ErDvW6JR7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "b40b564c-d345-493a-ba2b-5f86635c7d10"
      },
      "source": [
        "# Train the model for 10 epochs\n",
        "apparel_model_v2 = get_training_model_v2()\n",
        "history = apparel_model_v2.fit(x_train, y_train, \n",
        "    validation_data=(x_test, y_test), \n",
        "    epochs=10, \n",
        "    batch_size=128)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.5995 - accuracy: 0.7914 - val_loss: 0.4549 - val_accuracy: 0.8347\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.4200 - accuracy: 0.8501 - val_loss: 0.4094 - val_accuracy: 0.8520\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.3823 - accuracy: 0.8616 - val_loss: 0.3831 - val_accuracy: 0.8635\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.3575 - accuracy: 0.8713 - val_loss: 0.3896 - val_accuracy: 0.8563\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.3405 - accuracy: 0.8758 - val_loss: 0.3569 - val_accuracy: 0.8720\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.3249 - accuracy: 0.8813 - val_loss: 0.3490 - val_accuracy: 0.8733\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.3176 - accuracy: 0.8840 - val_loss: 0.3480 - val_accuracy: 0.8735\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.3055 - accuracy: 0.8878 - val_loss: 0.3355 - val_accuracy: 0.8809\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.2971 - accuracy: 0.8914 - val_loss: 0.3331 - val_accuracy: 0.8792\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.2905 - accuracy: 0.8920 - val_loss: 0.3344 - val_accuracy: 0.8808\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CsojVByFBlog",
        "colab_type": "text"
      },
      "source": [
        "### Step 3: `SavedModel` plunge"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZEsa_rcBuuy",
        "colab_type": "text"
      },
      "source": [
        "Okay! Now we are ready to the crux of the section. We will first create a custom model class (inherited from `tf.keras.Model`) and it will contain two things:\n",
        "- A model that is loaded with the weights of a trained model\n",
        "- A _serving function_ that will contain the preprocessing function along with the necessary signature. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2n3gjDKf3_j2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# A custom class for serving\n",
        "class ExportModel(tf.keras.Model):\n",
        "    def __init__(self, model):\n",
        "        super().__init__(self)\n",
        "        self.model = model\n",
        "\n",
        "    @tf.function(input_signature=[tf.TensorSpec([None, 28, 28], dtype=tf.uint8)])\n",
        "    def my_serve(self, images):\n",
        "        images = tf.cast(images, tf.float32) / 255        # pre-processing\n",
        "        probabilities = self.model(images)                # prediction from model\n",
        "        class_index = tf.argmax(probabilities, axis=-1)   # post-processing\n",
        "        return {\"class_index\": class_index}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEQbjFo_CV5-",
        "colab_type": "text"
      },
      "source": [
        "`my_serve` is our serving function. You can see that is _decorated_ with `tf.function` and the reason behind doing so is it allows us to embed an arbitrary function in a model's graph which can later be exported using the `SavedModel` format. \n",
        "\n",
        "We can also see - `input_signature=[tf.TensorSpec([None, 28, 28], dtype=tf.uint8)]`. This is needed in order to indicate which part of the model's graph would be needed while serving. By specifying `tf.TensorSpec([None, 28, 28]`, we instruct the function that the inputs should respect this shape - `[None, 28, 28]` and the `dtype` argument is self-explanatory. \n",
        "\n",
        "We will get to why the return type of the function is done in such a way - `{\"class_index\": class_index}` in a moment.\n",
        "\n",
        "If you are interested to know more using `SavedModel` and different serialization options that come with it, be sure to check [this tutorial](https://www.tensorflow.org/guide/saved_model) out. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7lXv9uL4D3c2",
        "colab_type": "text"
      },
      "source": [
        "### Step 4: Instantiate a dummy model and set its weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T_m3Fcbq41AM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set the weights of this dummy model to the weights of the model we trained\n",
        "restored_model = get_training_model_v2()\n",
        "restored_model.set_weights(apparel_model_v2.get_weights()) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cRI1ievjEHGT",
        "colab_type": "text"
      },
      "source": [
        "### Step 5: Export the model and run inference\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dSyy-EDrETV5",
        "colab_type": "text"
      },
      "source": [
        "Now, to serialize the model in the `SavedModel` format we will make use of [`tf.saved_model.save`](https://www.tensorflow.org/api_docs/python/tf/saved_model/save). It can automatically determine which input signature to use for serving for most of the models _if the details are available_. However, in our case, it won't be able to do so. So, we will need to explicitly indicate which function to use as the signature while serving. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4AZvQdB7DS6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "08bc2798-9874-4596-bac6-1375e72ad5e8"
      },
      "source": [
        "export_path = \"/content/saved_model/1/\"\n",
        "tf.keras.backend.set_learning_phase(0) # Make sure no weight update happens\n",
        "serving_model = ExportModel(restored_model) # Instantiate a model with the preprocessing function\n",
        "tf.saved_model.save(serving_model, export_path, signatures={'serving_default': serving_model.my_serve})"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.ExportModel object at 0x7f4096b7b358>, because it is not built.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "INFO:tensorflow:Assets written to: /content/saved_model/1/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CBq-VUTsFIOl",
        "colab_type": "text"
      },
      "source": [
        "By specifying `'serving_default': serving_model.my_serve` we instructed `tf.saved_model.save` about which signature to use for serving. Now if we inspect what all were saved, things should seem consistent. For this we are going to use the [`saved_model_cli`](https://www.tensorflow.org/guide/saved_model#saved_model_cli) command-line interpreter. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPbKNYm78CST",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d26a3550-47c1-477b-bb0f-0ea34c6929b4"
      },
      "source": [
        "!saved_model_cli show --dir /content/saved_model/1 --tag_set serve --signature_def serving_default"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The given SavedModel SignatureDef contains the following input(s):\n",
            "  inputs['images'] tensor_info:\n",
            "      dtype: DT_UINT8\n",
            "      shape: (-1, 28, 28)\n",
            "      name: serving_default_images:0\n",
            "The given SavedModel SignatureDef contains the following output(s):\n",
            "  outputs['class_index'] tensor_info:\n",
            "      dtype: DT_INT64\n",
            "      shape: (-1)\n",
            "      name: StatefulPartitionedCall:0\n",
            "Method name is: tensorflow/serving/predict\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UlSWG55AFwVR",
        "colab_type": "text"
      },
      "source": [
        "So, we can see that the configuration that is expected from the inputs and the outputs of the serialized model is consistent with what we had instructed. We returned the outputs in form a dictionary (namely `class_index`) in `my_serve` and we can see that as well. \n",
        "\n",
        "We can also do the inspection in Pythonic ways. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dtzk5isF7xM0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c8112735-8804-4ae7-815f-1afd6336f4be"
      },
      "source": [
        "loaded = tf.saved_model.load(\"/content/saved_model/1/\")\n",
        "print(list(loaded.signatures.keys())) # This signature will be used while serving"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['serving_default']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOkGr9VU8TxB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "90e5e33c-dc7e-4cfa-c440-20e22a15da33"
      },
      "source": [
        "# Output configuration\n",
        "infer = loaded.signatures[\"serving_default\"]\n",
        "print(infer.structured_outputs)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'class_index': TensorSpec(shape=(None,), dtype=tf.int64, name='class_index')}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u47LVgi0Gefg",
        "colab_type": "text"
      },
      "source": [
        "Let's finally run the inference!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZR8ju8-49SxK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the sample image\n",
        "image_pixels = cv2.imread(\"sample_image.png\")\n",
        "image_pixels = cv2.cvtColor(image_pixels, cv2.COLOR_BGR2GRAY)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5R16PD0f-vwe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d29ef57-803c-4b96-aa37-b712982a822f"
      },
      "source": [
        "# Run inference\n",
        "CLASSES[infer(tf.constant(image_pixels))[\"class_index\"].numpy()[0]]"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Ankle boot'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TqGyO4WuGlag",
        "colab_type": "text"
      },
      "source": [
        "We can see that the prediction is correct in this case as well. So, when we ran `infer = loaded.signatures[\"serving_default\"]` we essentially loaded a [concrete function](https://www.tensorflow.org/guide/concrete_function) i.e. we loaded `my_serve`. Remember we assigned the value of `serving_default` in the beginning of this section?\n",
        "\n",
        "With `infer(tf.constant(image_pixels))` we are simply running our input image through the concrete function and we are parsing the output from the dictionary (`class_index` being the key) it returns . "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0gnVOTSH3wR",
        "colab_type": "text"
      },
      "source": [
        "## References:\n",
        "- [MNIST on TPU (Tensor Processing Unit)\n",
        "or GPU using tf.Keras and tf.data.Dataset](https://colab.research.google.com/github/GoogleCloudPlatform/training-data-analyst/blob/master/courses/fast-and-lean-data-science/01_MNIST_TPU_Keras.ipynb)\n",
        "- [Using the SavedModel format](https://www.tensorflow.org/guide/saved_model#exporting_custom_models)"
      ]
    }
  ]
}