TensorFlow: Advanced Techniques/C4_W2_Lab_2_MNIST_Autoencoder.ipynb

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      "cell_type": "markdown",
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        "id": "nBXbFax4YP34"
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      "source": [
        "## Ungraded Lab: MNIST Autoencoder\n",
        "\n",
        "You will now work on an autoencoder that works on the [MNIST dataset](https://www.tensorflow.org/datasets/catalog/mnist). This will encode the inputs to lower resolution images. The decoder should then be able to generate the original input from this compressed representation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZYaLxnBYUKA"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EXwoz-KHtWO"
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "  \n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Is05FyRgYX0c"
      },
      "source": [
        "## Prepare the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xuhe2ksPI8A0"
      },
      "source": [
        "You will load the MNIST data from TFDS into train and test sets. Let's first define a preprocessing function for normalizing and flattening the images. Since we'll be training an autoencoder, this will return `image, image` because the input will also be the target or label while training.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "def map_image(image, label):\n",
        "  '''Normalizes and flattens the image. Returns image as input and label.'''\n",
        "  image = tf.cast(image, dtype=tf.float32)\n",
        "  image = image / 255.0\n",
        "  image = tf.reshape(image, shape=(784,))\n",
        "\n",
        "  return image, image"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
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      "source": [
        "# Load the train and test sets from TFDS\n",
        "\n",
        "BATCH_SIZE = 128\n",
        "SHUFFLE_BUFFER_SIZE = 1024\n",
        "\n",
        "train_dataset = tfds.load('mnist', as_supervised=True, split=\"train\")\n",
        "train_dataset = train_dataset.map(map_image)\n",
        "train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE).repeat()\n",
        "\n",
        "test_dataset = tfds.load('mnist', as_supervised=True, split=\"test\")\n",
        "test_dataset = test_dataset.map(map_image)\n",
        "test_dataset = test_dataset.batch(BATCH_SIZE).repeat()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset mnist/3.0.1 (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /root/tensorflow_datasets/mnist/3.0.1...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your\n",
            "local data directory. If you'd instead prefer to read directly from our public\n",
            "GCS bucket (recommended if you're running on GCP), you can instead pass\n",
            "`try_gcs=True` to `tfds.load` or set `data_dir=gs://tfds-data/datasets`.\n",
            "\n"
          ],
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        },
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            "\n",
            "\n",
            "\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z70w2KXjYk32"
      },
      "source": [
        "## Build the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1MKu48lBM2Qg"
      },
      "source": [
        "You will now build a simple autoencoder to ingest the data. Like before, the encoder will compress the input and reconstructs it in the decoder output."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KRrE2BV4IpzR"
      },
      "source": [
        "def simple_autoencoder(inputs):\n",
        "  '''Builds the encoder and decoder using Dense layers.'''\n",
        "  encoder = tf.keras.layers.Dense(units=32, activation='relu')(inputs)\n",
        "  decoder = tf.keras.layers.Dense(units=784, activation='sigmoid')(encoder)\n",
        "  \n",
        "  return encoder, decoder\n",
        "\n",
        "# set the input shape\n",
        "inputs =  tf.keras.layers.Input(shape=(784,))\n",
        "\n",
        "# get the encoder and decoder output\n",
        "encoder_output, decoder_output = simple_autoencoder(inputs)\n",
        "\n",
        "# setup the encoder because you will visualize its output later\n",
        "encoder_model = tf.keras.Model(inputs=inputs, outputs=encoder_output)\n",
        "\n",
        "# setup the autoencoder\n",
        "autoencoder_model = tf.keras.Model(inputs=inputs, outputs=decoder_output)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8k9OnSM4YxJd"
      },
      "source": [
        "## Compile the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvvDqY_XQPyb"
      },
      "source": [
        "You will setup the model for training. You can use binary crossentropy to measure the loss between pixel values that range from 0 (black) to 1 (white)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFwmAhWAYwcc"
      },
      "source": [
        "autoencoder_model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(), \n",
        "    loss='binary_crossentropy')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zzLf0oQ1Y0cI"
      },
      "source": [
        "## Train the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsaSjlAgYz-7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1a796d2-b246-4a52-f881-583c47fc7ef6"
      },
      "source": [
        "train_steps = 60000 // BATCH_SIZE\n",
        "simple_auto_history = autoencoder_model.fit(train_dataset, steps_per_epoch=train_steps, epochs=50)"
      ],
      "execution_count": 6,
      "outputs": [
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            "Epoch 50/50\n",
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        "id": "1_sKLXnBZFSc"
      },
      "source": [
        "## Display sample results\n",
        "\n",
        "You can now visualize the results. The utility functions below will help in plotting the encoded and decoded values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5tgFgilORr0M"
      },
      "source": [
        "def display_one_row(disp_images, offset, shape=(28, 28)):\n",
        "  '''Display sample outputs in one row.'''\n",
        "  for idx, test_image in enumerate(disp_images):\n",
        "    plt.subplot(3, 10, offset + idx + 1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    test_image = np.reshape(test_image, shape)\n",
        "    plt.imshow(test_image, cmap='gray')\n",
        "\n",
        "\n",
        "def display_results(disp_input_images, disp_encoded, disp_predicted, enc_shape=(8,4)):\n",
        "  '''Displays the input, encoded, and decoded output values.'''\n",
        "  plt.figure(figsize=(15, 5))\n",
        "  display_one_row(disp_input_images, 0, shape=(28,28,))\n",
        "  display_one_row(disp_encoded, 10, shape=enc_shape)\n",
        "  display_one_row(disp_predicted, 20, shape=(28,28,))"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtQyQRxRN_hH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "aed3ff12-8ec5-4b10-b40f-f8be8ed50571"
      },
      "source": [
        "# take 1 batch of the dataset\n",
        "test_dataset = test_dataset.take(1)\n",
        "\n",
        "# take the input images and put them in a list\n",
        "output_samples = []\n",
        "for input_image, image in tfds.as_numpy(test_dataset):\n",
        "      output_samples = input_image\n",
        "\n",
        "# pick 10 random numbers to be used as indices to the list above\n",
        "idxs = np.random.choice(BATCH_SIZE, size=10)\n",
        "\n",
        "# get the encoder output\n",
        "encoded_predicted = encoder_model.predict(test_dataset)\n",
        "\n",
        "# get a prediction for the test batch\n",
        "simple_predicted = autoencoder_model.predict(test_dataset)\n",
        "\n",
        "# display the 10 samples, encodings and decoded values!\n",
        "display_results(output_samples[idxs], encoded_predicted[idxs], simple_predicted[idxs])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xW5TCXZKu5X"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}