42-autoencoder_part2_upsampling.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 29,
      "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": "code",
      "execution_count": 30,
      "metadata": {
        "id": "YZm503-I_tji"
      },
      "outputs": [],
      "source": [
        "(x_train, _), (x_test, _) = fashion_mnist.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "uJZ-TcaqDBr5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ccf753c9-1f4c-4d23-9f46-854051a16de4"
      },
      "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": 32,
      "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": 33,
      "metadata": {
        "id": "thKUmbVVCQpt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "8de79aeb-ae7f-4152-d0df-c84deb251cf3"
      },
      "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\n",
        "### Using upsampling instead of Conv2DTranspose\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "R5KjoIlYCQko"
      },
      "outputs": [],
      "source": [
        "class Denoise_v2(Model):\n",
        "  def __init__(self):\n",
        "    super(Denoise_v2, 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.UpSampling2D(size=(2, 2)),\n",
        "      layers.Conv2D(8, kernel_size=(3, 3), activation='relu', padding='same'), #14x14x8\n",
        "      layers.UpSampling2D(size=(2, 2)),\n",
        "      layers.Conv2D(16, kernel_size=3, 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_v2()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "QYKbiDFYCQfj"
      },
      "outputs": [],
      "source": [
        "autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "IssFr1BNCQX3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9e90b14-98fe-4549-9841-1f16e026ab99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 12s 5ms/step - loss: 0.0152 - val_loss: 0.0104\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0097 - val_loss: 0.0093\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0090 - val_loss: 0.0089\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 10s 6ms/step - loss: 0.0087 - val_loss: 0.0086\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0085 - val_loss: 0.0084\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0083 - val_loss: 0.0085\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0082 - val_loss: 0.0082\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0082 - val_loss: 0.0082\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0081 - val_loss: 0.0082\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0080 - val_loss: 0.0080\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x789cb8aed0c0>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "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": 40,
      "metadata": {
        "id": "oEpxlX6sTEQz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f60244f-b64c-4e0a-a5e6-d4116a71e14f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_3 (Conv2D)           (None, 14, 14, 16)        160       \n",
            "                                                                 \n",
            " conv2d_4 (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": 41,
      "metadata": {
        "id": "pbeQtYMaUpro",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9066e5fa-4748-4791-fe47-3de6a21ebadd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " up_sampling2d (UpSampling2  (None, 14, 14, 8)         0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 14, 14, 8)         584       \n",
            "                                                                 \n",
            " up_sampling2d_1 (UpSamplin  (None, 28, 28, 8)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 28, 28, 16)        1168      \n",
            "                                                                 \n",
            " conv2d_7 (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": 42,
      "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": 43,
      "metadata": {
        "id": "sfxr9NdBCP_x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "62fdf8b0-16d7-460c-bf1c-a7cc2e2333e8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 20 Axes>"
            ],
            "image/png": 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          },
          "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
}