41-autoencoder_part1_transposed_conv.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ndo4ERqnwQOU"
      },
      "source": [
        "##### Copyright 2020 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "MTKwbguKwT4R"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfNT-mlFwxVM"
      },
      "source": [
        "# Intro to Autoencoders"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0TD5ZrvEMbhZ"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/generative/autoencoder\">\n",
        "    <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />\n",
        "    View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/generative/autoencoder.ipynb\">\n",
        "    <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />\n",
        "    Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/autoencoder.ipynb\">\n",
        "    <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />\n",
        "    View source on GitHub</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/generative/autoencoder.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITZuApL56Mny"
      },
      "source": [
        "This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection.\n",
        "\n",
        "An autoencoder is a special type of neural network that is trained to copy its input to its output. For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower dimensional latent representation, then decodes the latent representation back to an image. An autoencoder learns to compress the data while minimizing the reconstruction error.\n",
        "\n",
        "To learn more about autoencoders, please consider reading chapter 14 from [Deep Learning](https://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e1_Y75QXJS6h"
      },
      "source": [
        "## Import TensorFlow and other libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "YfIk2es3hJEd"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras import layers, losses\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "from tensorflow.keras.models import Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iYn4MdZnKCey"
      },
      "source": [
        "## Load the dataset\n",
        "To start, you will train the basic autoencoder using the Fashion MNIST dataset. Each image in this dataset is 28x28 pixels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "YZm503-I_tji",
        "outputId": "2cbca7c8-f66c-42aa-dd96-670410b81b44",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 0s 0us/step\n",
            "(60000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ]
        }
      ],
      "source": [
        "(x_train, _), (x_test, _) = fashion_mnist.load_data()\n",
        "\n",
        "x_train = x_train.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "\n",
        "print (x_train.shape)\n",
        "print (x_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VEdCXSwCoKok"
      },
      "source": [
        "## First example: Basic autoencoder\n",
        "![Basic autoencoder results](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/images/intro_autoencoder_result.png?raw=1)\n",
        "\n",
        "Define an autoencoder with two Dense layers: an `encoder`, which compresses the images into a 64 dimensional latent vector, and a `decoder`, that reconstructs the original image from the latent space.\n",
        "\n",
        "To define your model, use the [Keras Model Subclassing API](https://www.tensorflow.org/guide/keras/custom_layers_and_models).\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.math.reduce_prod([28,28,1])"
      ],
      "metadata": {
        "id": "8mTus49-dUIj",
        "outputId": "9147aaf3-668f-4ffd-c3dd-ae8dc2c491f2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=int32, numpy=784>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "0MUxidpyChjX"
      },
      "outputs": [],
      "source": [
        "class Autoencoder(Model):\n",
        "  def __init__(self, latent_dim, shape):\n",
        "    super(Autoencoder, self).__init__()\n",
        "    self.latent_dim = latent_dim\n",
        "    self.shape = shape\n",
        "    self.encoder = tf.keras.Sequential([\n",
        "      layers.Flatten(),\n",
        "      layers.Dense(latent_dim, activation='relu'),\n",
        "    ])\n",
        "    self.decoder = tf.keras.Sequential([\n",
        "      layers.Dense(tf.math.reduce_prod(shape).numpy(), activation='sigmoid'),\n",
        "      layers.Reshape(shape)\n",
        "    ])\n",
        "\n",
        "  def call(self, x):\n",
        "    encoded = self.encoder(x)\n",
        "    decoded = self.decoder(encoded)\n",
        "    return decoded\n",
        "\n",
        "\n",
        "shape = x_test.shape[1:]\n",
        "latent_dim = 64\n",
        "autoencoder = Autoencoder(latent_dim, shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "9I1JlqEIDCI4"
      },
      "outputs": [],
      "source": [
        "autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7oJSeMTroABs"
      },
      "source": [
        "Train the model using `x_train` as both the input and the target. The `encoder` will learn to compress the dataset from 784 dimensions to the latent space, and the `decoder` will learn to reconstruct the original images.\n",
        "."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "h1RI9OfHDBsK",
        "outputId": "8a68b73c-11e4-45bf-c8f5-3b40bf1ddb05",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 11s 3ms/step - loss: 0.0241 - val_loss: 0.0134\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0118 - val_loss: 0.0108\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0102 - val_loss: 0.0099\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0096 - val_loss: 0.0095\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0093 - val_loss: 0.0092\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0091 - val_loss: 0.0092\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0090 - val_loss: 0.0091\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0089 - val_loss: 0.0090\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0089 - val_loss: 0.0089\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0088 - val_loss: 0.0089\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x789ccf104b80>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "autoencoder.fit(x_train, x_train,\n",
        "                epochs=10,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_test, x_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wAM1QBhtoC-n"
      },
      "source": [
        "Now that the model is trained, let's test it by encoding and decoding images from the test set."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_imgs = autoencoder.encoder(x_test).numpy()"
      ],
      "metadata": {
        "id": "JSgzEDEIerHM"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "Pbr5WCj7FQUi"
      },
      "outputs": [],
      "source": [
        "decoded_imgs = autoencoder.decoder(encoded_imgs).numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "s4LlDOS6FUA1",
        "outputId": "bf66ac05-a446-4fdb-ae4c-89a9e0144686",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 20 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "n = 10\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "  # display original\n",
        "  ax = plt.subplot(2, n, i + 1)\n",
        "  plt.imshow(x_test[i])\n",
        "  plt.title(\"original\")\n",
        "  plt.gray()\n",
        "  ax.get_xaxis().set_visible(False)\n",
        "  ax.get_yaxis().set_visible(False)\n",
        "\n",
        "  # display reconstruction\n",
        "  ax = plt.subplot(2, n, i + 1 + n)\n",
        "  plt.imshow(decoded_imgs[i])\n",
        "  plt.title(\"reconstructed\")\n",
        "  plt.gray()\n",
        "  ax.get_xaxis().set_visible(False)\n",
        "  ax.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4gv6G8PoRQE"
      },
      "source": [
        "## Second example: Image denoising\n",
        "\n",
        "\n",
        "![Image denoising results](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/images/image_denoise_fmnist_results.png?raw=1)\n",
        "\n",
        "An autoencoder can also be trained to remove noise from images. In the following section, you will create a noisy version of the Fashion MNIST dataset by applying random noise to each image. You will then train an autoencoder using the noisy image as input, and the original image as the target.\n",
        "\n",
        "Let's reimport the dataset to omit the modifications made earlier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "gDYHJA2PCQ3m"
      },
      "outputs": [],
      "source": [
        "(x_train, _), (x_test, _) = fashion_mnist.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "uJZ-TcaqDBr5",
        "outputId": "23181b99-950c-4682-bdeb-049a2bd0193a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28, 1)\n"
          ]
        }
      ],
      "source": [
        "x_train = x_train.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "\n",
        "x_train = x_train[..., tf.newaxis]\n",
        "x_test = x_test[..., tf.newaxis]\n",
        "\n",
        "print(x_train.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPZl_6P65_8R"
      },
      "source": [
        "Adding random noise to the images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "axSMyxC354fc"
      },
      "outputs": [],
      "source": [
        "noise_factor = 0.2\n",
        "x_train_noisy = x_train + noise_factor * tf.random.normal(shape=x_train.shape)\n",
        "x_test_noisy = x_test + noise_factor * tf.random.normal(shape=x_test.shape)\n",
        "\n",
        "x_train_noisy = tf.clip_by_value(x_train_noisy, clip_value_min=0., clip_value_max=1.)\n",
        "x_test_noisy = tf.clip_by_value(x_test_noisy, clip_value_min=0., clip_value_max=1.)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRxHe4XXltNd"
      },
      "source": [
        "Plot the noisy images.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "thKUmbVVCQpt",
        "outputId": "1a360b74-a83e-4c1f-81c3-daf19b9e1aaa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x200 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "n = 10\n",
        "plt.figure(figsize=(20, 2))\n",
        "for i in range(n):\n",
        "    ax = plt.subplot(1, n, i + 1)\n",
        "    plt.title(\"original + noise\")\n",
        "    plt.imshow(tf.squeeze(x_test_noisy[i]))\n",
        "    plt.gray()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sy9SY8jGl5aP"
      },
      "source": [
        "### Define a convolutional autoencoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vT_BhZngWMwp"
      },
      "source": [
        "In this example, you will train a convolutional autoencoder using  [Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D) layers in the `encoder`, and [Conv2DTranspose](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2DTranspose) layers in the `decoder`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "R5KjoIlYCQko"
      },
      "outputs": [],
      "source": [
        "class Denoise(Model):\n",
        "  def __init__(self):\n",
        "    super(Denoise, self).__init__()\n",
        "    self.encoder = tf.keras.Sequential([\n",
        "      layers.Input(shape=(28, 28, 1)),\n",
        "      layers.Conv2D(16, (3, 3), activation='relu', padding='same', strides=2), # 14, 14x 16\n",
        "      layers.Conv2D(8, (3, 3), activation='relu', padding='same', strides=2)]) # 7x7x8\n",
        "\n",
        "    self.decoder = tf.keras.Sequential([\n",
        "      layers.Conv2DTranspose(8, kernel_size=3, strides=2, activation='relu', padding='same'), #14x14x8\n",
        "      layers.Conv2DTranspose(16, kernel_size=3, strides=2, activation='relu', padding='same'), #28x28x16\n",
        "      layers.Conv2D(1, kernel_size=(3, 3), activation='sigmoid', padding='same')]) #28x28x1\n",
        "\n",
        "  def call(self, x):\n",
        "    encoded = self.encoder(x)\n",
        "    decoded = self.decoder(encoded)\n",
        "    return decoded\n",
        "\n",
        "autoencoder = Denoise()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "QYKbiDFYCQfj"
      },
      "outputs": [],
      "source": [
        "autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "IssFr1BNCQX3",
        "outputId": "5d6fe2f2-ce6c-491a-8855-1c9f78877e33",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 16s 4ms/step - loss: 0.0161 - val_loss: 0.0097\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0091 - val_loss: 0.0088\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0083 - val_loss: 0.0080\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 10s 6ms/step - loss: 0.0077 - val_loss: 0.0078\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0074 - val_loss: 0.0074\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0072 - val_loss: 0.0072\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0071 - val_loss: 0.0071\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0070 - val_loss: 0.0070\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0069 - val_loss: 0.0069\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0068 - val_loss: 0.0069\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x789cb8aeea10>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "autoencoder.fit(x_train_noisy, x_train,\n",
        "                epochs=10,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_test_noisy, x_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G85xUVBGTAKp"
      },
      "source": [
        "Let's take a look at a summary of the encoder. Notice how the images are downsampled from 28x28 to 7x7."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "oEpxlX6sTEQz",
        "outputId": "e7a17c8f-6792-4df3-bda8-6c261c103d98",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 14, 14, 16)        160       \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 7, 7, 8)           1160      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1320 (5.16 KB)\n",
            "Trainable params: 1320 (5.16 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "autoencoder.encoder.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DDZBfMx1UtXx"
      },
      "source": [
        "The decoder upsamples the images back from 7x7 to 28x28."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "pbeQtYMaUpro",
        "outputId": "94d8bd25-33f9-4b91-9f98-69db726c64bc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_transpose (Conv2DTr  (None, 14, 14, 8)         584       \n",
            " anspose)                                                        \n",
            "                                                                 \n",
            " conv2d_transpose_1 (Conv2D  (None, 28, 28, 16)        1168      \n",
            " Transpose)                                                      \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 28, 28, 1)         145       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1897 (7.41 KB)\n",
            "Trainable params: 1897 (7.41 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "autoencoder.decoder.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A7-VAuEy_N6M"
      },
      "source": [
        "Plotting both the noisy images and the denoised images produced by the autoencoder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "t5IyPi1fCQQz"
      },
      "outputs": [],
      "source": [
        "encoded_imgs = autoencoder.encoder(x_test_noisy).numpy()\n",
        "decoded_imgs = autoencoder.decoder(encoded_imgs).numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "sfxr9NdBCP_x",
        "outputId": "4564e24a-6fb5-4e7a-d874-90cb2cc5fef8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 20 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "n = 10\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "\n",
        "    # display original + noise\n",
        "    ax = plt.subplot(2, n, i + 1)\n",
        "    plt.title(\"original + noise\")\n",
        "    plt.imshow(tf.squeeze(x_test_noisy[i]))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "\n",
        "    # display reconstruction\n",
        "    bx = plt.subplot(2, n, i + n + 1)\n",
        "    plt.title(\"reconstructed\")\n",
        "    plt.imshow(tf.squeeze(decoded_imgs[i]))\n",
        "    plt.gray()\n",
        "    bx.get_xaxis().set_visible(False)\n",
        "    bx.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "autoencoder.ipynb",
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}