DEDOMENA.ipynb

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  "metadata": {
    "colab": {
      "name": "DEDOMENA.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QnI6uEAIedIr",
        "colab_type": "text"
      },
      "source": [
        "# **FINAL_SUBMISSION**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2W1lvz8k921d",
        "colab_type": "text"
      },
      "source": [
        "**THE CHALLANGE**\n",
        "\n",
        "\n",
        "Help find ways to improve the performance of machine learning and predictive models by filling in gaps in the datasets prior to model training.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fDm8rIs0Jr7O",
        "colab_type": "text"
      },
      "source": [
        "ANATOMY OF THE CHALLANGE:\n",
        "\n",
        "\n",
        "DO WHAT?\n",
        "\n",
        "Improve performance of Machine Learning (ML) models by collecting a complete and continuous sensor data stream.\n",
        "\n",
        "WHY DID IT HAPPEN?\n",
        "\n",
        "*  Sensor issues or signal noise due to experimental environment/setup\n",
        "*  Corrupted of data\n",
        "*  Loss of data during transmission (also due to limited bandwidth of transmission)\n",
        "*  Interference\n",
        "*  Limited amount of power for data collection and transmission\n",
        "\n",
        "\n",
        "WHAT IT DOES?\n",
        "\n",
        "*  Limits the ability to train accurate ML models to predict features/characteristics in data, which in turn renders the data \"useless\"\n",
        "*  Hinders the collection of good-quality data silos\n",
        "\n",
        "\n",
        "HOW TO SOLVE/OBJECTIVE?\n",
        "\n",
        "*  By \"filling in\" the missing datapoints in the datasets\n",
        "*  By \"generating\" the missing datapoints in the datasets\n",
        "*  By eliminating/removing the noisy/corrupted information that is embedded in individual datapoints\n",
        "\n",
        "\n",
        "\n",
        "DO IT WHEN?\n",
        "\n",
        "*  Prior to training, i.e. during data cleaning and preprocessing.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Clp6WdehRgm_",
        "colab_type": "text"
      },
      "source": [
        "We started by investigating the reasons behind data loss when the data is acquired through a sensor or sensor array. In addition, we also started doing research finding the reasons behind the loss.\n",
        "\n",
        "Our research concluded that data loss in any dataset does not only occur due to missing data (be it discreet or continuous/timeseries) but also due to incomplete or corrupted or noisy collection of these data that are acquired by the sensors due to the reasons mentioned above.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "HYPOTHESIS:\n",
        "\n",
        "\n",
        "\n",
        "We propose an end-to-end Machine learning pipeline to -fill in the missing data using Generative modeling which involves using a model to generate new examples that plausibly come from an existing distribution of samples.\n",
        "\n",
        "Stacked Denoising Autoencoder for when the sensor data is corrupted or there is a bit of noise in it, we call this type of data noisy data.\n",
        "To obtain proper information about the data, we want Denoising.\n",
        "We define our autoencoder to remove (if not all)most of the noise our data.\n",
        "\n",
        "Transforms the input into a lower dimensional representation, and a decoder, which tries to reconstruct the original input from the lower dimensional representation. Therefore, these models present some some sort of “bottle neck” in the middle that forces the network to learn how to compress the data in a lower dimensional space. When training these algorithms, the objective is to be able to reconstruct the original input with the minimum amount of information loss. Once the model is trained, we can compress data at will by only using the encoder component of the autoencoder.\n",
        "\n",
        "\n",
        "---\n",
        "**(A)**\n",
        "\n",
        "DETAILS:\n",
        "One model is called the “generator” or “generative network” model that learns to generate new plausible samples. The other model is called the “discriminator” or “discriminative network” and learns to differentiate generated examples from real examples.\n",
        "\n",
        "The two models are set up in a contest or a game (in a game theory sense) where the generator model seeks to fool the discriminator model, and the discriminator is provided with both examples of real and generated samples.\n",
        "\n",
        "After training, the generative model can then be used to create new plausible samples on demand.\n",
        "\n",
        "----\n",
        "\n",
        "\n",
        "**(B)**\n",
        "\n",
        "An autoencoder is a neural network used for dimensionality reduction; that is, for feature selection and extraction. Autoencoders with more hidden layers than inputs run the risk of learning the identity function – where the output simply equals the input – thereby becoming useless.\n",
        "\n",
        "Denoising autoencoders are an extension of the basic autoencoder, and represent a stochastic version of it. Denoising autoencoders attempt to address identity-function risk by randomly corrupting input (i.e. introducing noise) that the autoencoder must then reconstruct, or denoise.\n",
        "\n",
        "Stacked Denoising Autoencoder\n",
        "\n",
        "A stacked denoising autoencoder is simply many denoising autoencoders strung together.\n",
        "\n",
        "A key function of SDAs, and deep learning more generally, is unsupervised pre-training, layer by layer, as input is fed through. Once each layer is pre-trained to conduct feature selection and extraction on the input from the preceding layer, a second stage of supervised fine-tuning can follow.\n",
        "\n",
        "A word on stochastic corruption in SDAs: Denoising autoencoders shuffle data around and learn about that data by attempting to reconstruct it. The act of shuffling is the noise, and the job of the network is to recognize the features within the noise that will allow it to classify the input. When a network is being trained, it generates a model, and measures the distance between that model and the benchmark through a loss function. Its attempts to minimize the loss function involve resampling the shuffled inputs and re-reconstructing the data, until it finds those inputs which bring its model closest to what it has been told is true.\n",
        "\n",
        "---\n",
        "\n",
        "**(C)**\n",
        "\n",
        "Encoder network: It translates the original high-dimension input into the latent low-dimensional code. The input size is larger than the output size.\n",
        "Decoder network: The decoder network recovers the data from the code, likely with larger and larger output layers.\n",
        "\n",
        "The encoder network essentially accomplishes the dimensionality reduction, just like how we would use Principal Component Analysis (PCA) or Matrix Factorization (MF) for. In addition, the autoencoder is explicitly optimized for the data reconstruction from the code.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "**(D)**\n",
        "\n",
        "Disentangled Variational autoencoders\n",
        "\n",
        "The idea of Variational Autoencoder  is actually less similar to all the autoencoder models above, but deeply rooted in the methods of variational bayesian and graphical model.\n",
        "Instead of mapping the input into a fixed vector, we want to map it into a distribution.\n",
        "If each variable in the inferred latent representation is only sensitive to one single generative factor and relatively invariant to other factors, we will say this representation is disentangled or factorized. One benefit that often comes with disentangled representation is good interpretability and easy generalization to a variety of tasks.\n",
        "\n",
        "For example, a model trained on photos of human faces might capture the gentle, skin color, hair color, hair length, emotion, whether wearing a pair of glasses and many other relatively independent factors in separate dimensions. Such a disentangled representation is very beneficial to facial image generation.\n",
        "\n",
        "\n",
        "https://lilianweng.github.io/lil-log/2018/08/12/from-autoencoder-to-beta-vae.html#beta-vae\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-iIY83ms6Str",
        "colab_type": "text"
      },
      "source": [
        "# **Download NASA Satellite Images of the Earth (NASA MODIS)**\n",
        "\n",
        "This function can download an entire history of any NASA image dataset that follow the GIBS RESTful API pattern of calls\n",
        "\n",
        "**Sample call**: https://gibs.earthdata.nasa.gov/wmts/epsg4326/best/MODIS_Terra_CorrectedReflectance_TrueColor/default/2012-07-09/250m/6/13/36.jpg\n",
        "\n",
        "**Pattern**: https://gibs.earthdata.nasa.gov/wmts/epsg{EPSG:Code}/best/{ProductName}/default/{Time}/{TileMatrixSet}/{ZoomLevel}/{TileRow}/{TileCol}.png\n",
        "\n",
        "**GIBS API webpage**: https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+API+for+Developers\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t2lFD7mTdJrm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import requests\n",
        "import shutil\n",
        "import os\n",
        " \n",
        "\"\"\"\n",
        "This function can download an entire history of any NASA image dataset that follow the GIBS RESTful API pattern of calls\n",
        "\n",
        "Sample call: https://gibs.earthdata.nasa.gov/wmts/epsg4326/best/MODIS_Terra_CorrectedReflectance_TrueColor/default/2012-07-09/250m/6/13/36.jpg\n",
        "\n",
        "Pattern: https://gibs.earthdata.nasa.gov/wmts/epsg{EPSG:Code}/best/{ProductName}/default/{Time}/{TileMatrixSet}/{ZoomLevel}/{TileRow}/{TileCol}.png\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "def download_MODIS_image(num_images, year, month, day, max_day, max_month, end_date):\n",
        "    \n",
        "    # input parameters\n",
        "    # num_images: number of images to download (for this URL, don't go over 80)\n",
        "    # year: year when image was taken; e.g. 2019\n",
        "    # month: month when image was taken; e.g. 7\n",
        "    # day: day when image was taken; e.g. 12\n",
        "    # max_day: day of each month on which you want to stop and move on to the next month; e.g. 30\n",
        "    # max_month: month of each year on which you want to stop and move on to the next year; e.g. 12\n",
        "    # end_date: a data (string) on which you want to break the loop; e.g. ''2019-10-20\n",
        "    \n",
        "    if month < 10 and day < 10:\n",
        "        date = str(year) + '-0' + str(month) + '-0' + str(day)\n",
        "    elif month < 10 and day >= 10:\n",
        "        date = str(year) + '-0' + str(month) + '-' + str(day)\n",
        "    elif month >= 10 and day < 10:\n",
        "    elif month >= 10 and day < 10:\n",
        "        date = str(year) + '-' + str(month) + '-0' + str(day)\n",
        "    else:\n",
        "        date = str(year) + '-' + str(month) + '-' + str(day)\n",
        "        \n",
        "    \n",
        "    #max_day = 30   # not going to take the data from 31st day of any month (future imoprovement)\n",
        "    #max_month = 12\n",
        "    \n",
        "    image_num = 0\n",
        "    \n",
        "    for m in range(month, max_month+1):\n",
        "        for d in range(day, max_day+1):            \n",
        "            for i in range(num_images):\n",
        "                \n",
        "                if date == end_date:\n",
        "                    print('end date','end_date', ' reached')\n",
        "                    break            \n",
        "                image_id = i\n",
        "                #image_num = str(img_counter)\n",
        "                url = 'https://gibs.earthdata.nasa.gov/wmts/epsg4326/best/MODIS_Terra_CorrectedReflectance_TrueColor/default/' + date + '/250m/6/13/' + str(image_id) + '.jpg'\n",
        "                \n",
        "                #Save file in local hard drive\n",
        "                filepath = 'D:\\SpaceApps2019\\Chasers_of_lost_data\\downloads\\images_modis_nasa\\\\'\n",
        "                filename = 'nasa_modis_image_' + date + '_' + str(image_num) + '.jpg'\n",
        "                full_filepath = filepath + filename\n",
        "                \n",
        "                # Open the url image, set stream to True, this will return the stream content.\n",
        "                response = requests.get(url, stream=True)\n",
        "                    \n",
        "                # Open a local file with wb ( write binary ) permission.\n",
        "                local_file = open(full_filepath, 'wb')\n",
        "                \n",
        "                # Set decode_content value to True, otherwise the downloaded image file's size will be zero.\n",
        "                response.raw.decode_content = True\n",
        "                \n",
        "                # Copy the response stream raw data to local image file.\n",
        "                shutil.copyfileobj(response.raw, local_file)\n",
        "                \n",
        "                # Remove the image url response object.\n",
        "                local_file.close()\n",
        "                del response\n",
        "                \n",
        "                filesize = os.path.getsize(full_filepath)\n",
        "                if filesize > 428:\n",
        "                    print('image #', 'image_num', 'downloaded')\n",
        "                else:\n",
        "                    print('image #','image_num', 'is a zero sized file --> invalid image')\n",
        "                    \n",
        "                image_num += 1\n",
        "            \n",
        "\n",
        "#### MAIN ####\n",
        "\n",
        "# Loop over dates in a month to download in larger batches\n",
        "#num_images = 80\n",
        "#day = 1\n",
        "#month = 7\n",
        "#year = 2019\n",
        "#max_day = 30\n",
        "#max_month = 9\n",
        "#end_date = '2019-09-15'\n",
        "\n",
        "#download_MODIS_image(num_images, year, month, day, max_day, max_month, end_date)\n",
        "            \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFb6ARL_ooxg",
        "colab_type": "text"
      },
      "source": [
        "# **Image Completion with Deep Learning in TensorFlow**\n",
        "Content-aware fill is a powerful tool designers and photographers use to fill in unwanted or missing parts of images. Image completion and inpainting are closely related technologies used to fill in missing or corrupted parts of images. There are many ways to do content-aware fill, image completion, and inpainting. \n",
        "\n",
        "We use Semantic Image Inpainting with Deep Generative Models\n",
        "\n",
        "\"Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data. Existing methods which extract information from only a single image generally produce unsatisfactory results due to the lack of high level context. In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data. Given a trained generative model, we search for the closest encoding of the corrupted image in the latent image manifold using our context and prior losses. This encoding is then passed through the generative model to infer the missing content. In our method, inference is possible irrespective of how the missing content is structured, while the state-of-the-art learning based method requires specific information about the holes in the training phase. Experiments  show that the method successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.\" "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4l7KE00KBwVe",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6WwOa15JIph",
        "colab_type": "text"
      },
      "source": [
        "# **Proof of Concept #1**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rF2x3qooyBTI"
      },
      "source": [
        "# **Filling up missing portions of NASA earth images with **\n",
        "\n",
        "# **# Algorithm: Deep Convolutional Generative Adversarial Network (DCGAN)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ITZuApL56Mny"
      },
      "source": [
        "This is a modified version of a DCGAN tutorial that was originally developed to generate fake MNIST images. **We have modified the code to take a single, corrupted NASA earth image as input and repair it.**\n",
        "\n",
        "The code is written using the [Keras Sequential API](https://www.tensorflow.org/guide/keras) with a `tf.GradientTape` training loop."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OQAprBkmCkld"
      },
      "source": [
        "## What are GANs?\n",
        "[Generative Adversarial Networks](https://arxiv.org/abs/1406.2661) (GANs) are one of the most interesting ideas in computer science today. Two models are trained simultaneously by an adversarial process. A *generator* (\"the artist\") learns to create images that look real, while a *discriminator* (\"the art critic\") learns to tell real images apart from fakes.\n",
        "\n",
        "![A diagram of a generator and discriminator](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/images/gan1.png?raw=1)\n",
        "\n",
        "During training, the *generator* progressively becomes better at creating images that look real, while the *discriminator* becomes better at telling them apart. The process reaches equilibrium when the *discriminator* can no longer distinguish real images from fakes.\n",
        "\n",
        "![A second diagram of a generator and discriminator](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/images/gan2.png?raw=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "e1_Y75QXJS6h"
      },
      "source": [
        "### Import TensorFlow and other libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "J5oue0oqCkZZ",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "g5RstiiB8V-z",
        "outputId": "4c46fcf6-b00a-48eb-e164-087fd936fb3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow is already loaded. Please restart the runtime to change versions.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WZKbyU2-AiY-",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wx-zNbLqB4K8",
        "outputId": "0431ed04-10f4-48cc-fc60-bad2e302d331",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.15.0-rc3'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YzTlj4YdCip_",
        "outputId": "5acff3be-3ae1-4b87-c7f5-2177388f0236",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "# To generate GIFs\n",
        "!pip install imageio"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: imageio in /usr/local/lib/python3.6/dist-packages (2.4.1)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from imageio) (4.3.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from imageio) (1.16.5)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->imageio) (0.46)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YfIk2es3hJEd",
        "colab": {}
      },
      "source": [
        "import glob\n",
        "import imageio\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import PIL\n",
        "from tensorflow.keras import layers\n",
        "import time\n",
        "\n",
        "from IPython import display"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "iYn4MdZnKCey"
      },
      "source": [
        "### Load and prepare the dataset\n",
        "\n",
        "You will use the MNIST dataset to train the generator and the discriminator. The generator will generate handwritten digits resembling the MNIST data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "a4fYMGxGhrna",
        "colab": {}
      },
      "source": [
        "(train_images, train_labels), (_, _) = tf.keras.datasets.mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NFC2ghIdiZYE",
        "colab": {}
      },
      "source": [
        "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype('float32')\n",
        "train_images = (train_images - 127.5) / 127.5 # Normalize the images to [-1, 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "S4PIDhoDLbsZ",
        "colab": {}
      },
      "source": [
        "BUFFER_SIZE = 60000\n",
        "BATCH_SIZE = 256"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-yKCCQOoJ7cn",
        "colab": {}
      },
      "source": [
        "# Batch and shuffle the data\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices(train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "THY-sZMiQ4UV"
      },
      "source": [
        "## Create the models\n",
        "\n",
        "Both the generator and discriminator are defined using the [Keras Sequential API](https://www.tensorflow.org/guide/keras#sequential_model)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-tEyxE-GMC48"
      },
      "source": [
        "### The Generator\n",
        "\n",
        "The generator uses `tf.keras.layers.Conv2DTranspose` (upsampling) layers to produce an image from a seed (random noise). It starts with a **`Dense` layer that takes this seed as input**, then **upsamples it several times until it reaches the desired image size** of 28x28x1. Notice the `tf.keras.layers.LeakyReLU` activation for each layer, except the output layer which uses tanh."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6bpTcDqoLWjY",
        "colab": {}
      },
      "source": [
        "def make_generator_model():\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.LeakyReLU())\n",
        "\n",
        "    model.add(layers.Reshape((7, 7, 256)))\n",
        "    assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size\n",
        "\n",
        "    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))\n",
        "    assert model.output_shape == (None, 7, 7, 128)\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.LeakyReLU())\n",
        "\n",
        "    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))\n",
        "    assert model.output_shape == (None, 14, 14, 64)\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.LeakyReLU())\n",
        "\n",
        "    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))\n",
        "    assert model.output_shape == (None, 28, 28, 1)\n",
        "\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GyWgG09LCSJl"
      },
      "source": [
        "Use the (as yet untrained) generator to create an image."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gl7jcC7TdPTG",
        "colab": {}
      },
      "source": [
        "generator = make_generator_model()\n",
        "\n",
        "noise = tf.random.normal([1, 100])\n",
        "generated_image = generator(noise, training=False)\n",
        "\n",
        "plt.imshow(generated_image[0, :, :, 0], cmap='gray')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "D0IKnaCtg6WE"
      },
      "source": [
        "### The Discriminator\n",
        "\n",
        "The discriminator is a CNN-based image classifier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "dw2tPLmk2pEP",
        "colab": {}
      },
      "source": [
        "def make_discriminator_model():\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',\n",
        "                                     input_shape=[28, 28, 1]))\n",
        "    model.add(layers.LeakyReLU())\n",
        "    model.add(layers.Dropout(0.3))\n",
        "\n",
        "    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))\n",
        "    model.add(layers.LeakyReLU())\n",
        "    model.add(layers.Dropout(0.3))\n",
        "\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dense(1))\n",
        "\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "QhPneagzCaQv"
      },
      "source": [
        "Use the (as yet untrained) discriminator to classify the generated images as real or fake. The model will be trained to output positive values for real images, and negative values for fake images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gDkA05NE6QMs",
        "colab": {}
      },
      "source": [
        "discriminator = make_discriminator_model()\n",
        "decision = discriminator(generated_image)\n",
        "print (decision)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0FMYgY_mPfTi"
      },
      "source": [
        "## Define the loss and optimizers\n",
        "\n",
        "Define loss functions and optimizers for both models.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "psQfmXxYKU3X",
        "colab": {}
      },
      "source": [
        "# This method returns a helper function to compute cross entropy loss\n",
        "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PKY_iPSPNWoj"
      },
      "source": [
        "### Discriminator loss\n",
        "\n",
        "This method quantifies how well the discriminator is able to distinguish real images from fakes. It compares the discriminator's predictions on real images to an array of 1s, and the discriminator's predictions on fake (generated) images to an array of 0s."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wkMNfBWlT-PV",
        "colab": {}
      },
      "source": [
        "def discriminator_loss(real_output, fake_output):\n",
        "    real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n",
        "    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n",
        "    total_loss = real_loss + fake_loss\n",
        "    return total_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Jd-3GCUEiKtv"
      },
      "source": [
        "### Generator loss\n",
        "The generator's loss quantifies how well it was able to trick the discriminator. Intuitively, if the generator is performing well, the discriminator will classify the fake images as real (or 1). Here, we will compare the discriminators decisions on the generated images to an array of 1s."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "90BIcCKcDMxz",
        "colab": {}
      },
      "source": [
        "def generator_loss(fake_output):\n",
        "    return cross_entropy(tf.ones_like(fake_output), fake_output)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MgIc7i0th_Iu"
      },
      "source": [
        "The discriminator and the generator optimizers are different since we will train two networks separately."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iWCn_PVdEJZ7",
        "colab": {}
      },
      "source": [
        "generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
        "discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mWtinsGDPJlV"
      },
      "source": [
        "### Save checkpoints\n",
        "Save and restore models, which can be helpful in case a long running training task is interrupted."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CA1w-7s2POEy",
        "colab": {}
      },
      "source": [
        "checkpoint_dir = './training_checkpoints'\n",
        "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n",
        "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n",
        "                                 discriminator_optimizer=discriminator_optimizer,\n",
        "                                 generator=generator,\n",
        "                                 discriminator=discriminator)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Rw1fkAczTQYh"
      },
      "source": [
        "## Define the training loop\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NS2GWywBbAWo",
        "colab": {}
      },
      "source": [
        "EPOCHS = 50\n",
        "noise_dim = 100\n",
        "num_examples_to_generate = 16\n",
        "\n",
        "# We will reuse this seed overtime (so it's easier)\n",
        "# to visualize progress in the animated GIF)\n",
        "seed = tf.random.normal([num_examples_to_generate, noise_dim])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "jylSonrqSWfi"
      },
      "source": [
        "The training loop begins with generator receiving a random seed as input. That seed is used to produce an image. The discriminator is then used to classify real images (drawn from the training set) and fakes images (produced by the generator). The loss is calculated for each of these models, and the gradients are used to update the generator and discriminator."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3t5ibNo05jCB",
        "colab": {}
      },
      "source": [
        "# Notice the use of `tf.function`\n",
        "# This annotation causes the function to be \"compiled\".\n",
        "@tf.function\n",
        "def train_step(images):\n",
        "    noise = tf.random.normal([BATCH_SIZE, noise_dim])\n",
        "\n",
        "    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
        "      generated_images = generator(noise, training=True)\n",
        "\n",
        "      real_output = discriminator(images, training=True)\n",
        "      fake_output = discriminator(generated_images, training=True)\n",
        "\n",
        "      gen_loss = generator_loss(fake_output)\n",
        "      disc_loss = discriminator_loss(real_output, fake_output)\n",
        "\n",
        "    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
        "    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
        "\n",
        "    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
        "    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2M7LmLtGEMQJ",
        "colab": {}
      },
      "source": [
        "def train(dataset, epochs):\n",
        "  for epoch in range(epochs):\n",
        "    start = time.time()\n",
        "\n",
        "    for image_batch in dataset:\n",
        "      train_step(image_batch)\n",
        "\n",
        "    # Produce images for the GIF as we go\n",
        "    display.clear_output(wait=True)\n",
        "    generate_and_save_images(generator,\n",
        "                             epoch + 1,\n",
        "                             seed)\n",
        "\n",
        "    # Save the model every 15 epochs\n",
        "    if (epoch + 1) % 15 == 0:\n",
        "      checkpoint.save(file_prefix = checkpoint_prefix)\n",
        "\n",
        "    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n",
        "\n",
        "  # Generate after the final epoch\n",
        "  display.clear_output(wait=True)\n",
        "  generate_and_save_images(generator,\n",
        "                           epochs,\n",
        "                           seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2aFF7Hk3XdeW"
      },
      "source": [
        "**Generate and save images**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RmdVsmvhPxyy",
        "colab": {}
      },
      "source": [
        "def generate_and_save_images(model, epoch, test_input):\n",
        "  # Notice `training` is set to False.\n",
        "  # This is so all layers run in inference mode (batchnorm).\n",
        "  predictions = model(test_input, training=False)\n",
        "\n",
        "  fig = plt.figure(figsize=(4,4))\n",
        "\n",
        "  for i in range(predictions.shape[0]):\n",
        "      plt.subplot(4, 4, i+1)\n",
        "      plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')\n",
        "      plt.axis('off')\n",
        "\n",
        "  plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dZrd4CdjR-Fp"
      },
      "source": [
        "## Train the model\n",
        "Call the `train()` method defined above to train the generator and discriminator simultaneously. Note, training GANs can be tricky. It's important that the generator and discriminator do not overpower each other (e.g., that they train at a similar rate).\n",
        "\n",
        "At the beginning of the training, the generated images look like random noise. As training progresses, the generated digits will look increasingly real. After about 50 epochs, they resemble MNIST digits. This may take about one minute / epoch with the default settings on Colab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ly3UN0SLLY2l",
        "colab": {}
      },
      "source": [
        "%%time\n",
        "train(train_dataset, EPOCHS)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rfM4YcPVPkNO"
      },
      "source": [
        "Restore the latest checkpoint."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XhXsd0srPo8c",
        "colab": {}
      },
      "source": [
        "checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "P4M_vIbUi7c0"
      },
      "source": [
        "## Create a GIF\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WfO5wCdclHGL",
        "colab": {}
      },
      "source": [
        "# Display a single image using the epoch number\n",
        "def display_image(epoch_no):\n",
        "  return PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5x3q9_Oe5q0A",
        "colab": {}
      },
      "source": [
        "display_image(EPOCHS)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUG8NQ6rKx_V",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQluQQDrExsR",
        "colab_type": "text"
      },
      "source": [
        "# **Proof of Concept #2**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_1p3p7AcE9vM",
        "colab_type": "text"
      },
      "source": [
        "# **Cleaning up noisy/missing data from NASA datasets**\n",
        "\n",
        "# **# Algorithm: Denoising Autoencoder**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_gaGTrwwksC",
        "colab_type": "text"
      },
      "source": [
        "**Denoising_autoencoders_NASA_earth_data**\n",
        "\n",
        "\n",
        "https://colab.research.google.com/drive/1Sj_J9yKrNXkFQCBMcZAqF03MOJqUIt_N#forceEdit=true&sandboxMode=true\n",
        "\n",
        "\n",
        "https://colab.research.google.com/drive/1Sj_J9yKrNXkFQCBMcZAqF03MOJqUIt_N\n",
        "\n",
        "https://github.com/pilillo/img-notebooks\n",
        "\n",
        "https://github.com/pilillo/img-notebooks/blob/master/Denoising_autoencoders.ipynb"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXzwRF1iUsOX",
        "colab_type": "code",
        "outputId": "cd664dfa-fa4f-4283-a0d1-293a518acce9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!pwd"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6tMTCnsYUwxx",
        "colab_type": "code",
        "outputId": "3fa6d508-b990-49bf-b045-d2652b5d7687",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd Colab\\ Notebooks"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Colab Notebooks\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wY1Iz9_lUzBg",
        "colab_type": "code",
        "outputId": "10b0d55c-708e-47c4-966e-200e1796cfa3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 28x28-nasa-earth-images_bad   'Copy of Denoising_autoencoders.ipynb'\n",
            " 28x28-nasa-earth-images_good   dcgan_tutorial_nasa.ipynb\n",
            " 56x56-nasa-earth-bad-images    Denoising_autoencoders.ipynb\n",
            " 56x56-nasa-earth-images        nasa_modis_image_2019-03-016_lowres5.jpg\n",
            "'Copy of Copy of dcgan.ipynb'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8r44qjVuS4ul",
        "colab_type": "code",
        "outputId": "5cdfa46b-800c-426a-cbae-b52013797f50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# mount google drive location where you saved a .zip archive of your folder that contains images; then unzip the file\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5yuM7X9ZUdXO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get RGB images from Google drive and store them as numpy array\n",
        "\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "import math\n",
        "import cv2\n",
        "\n",
        "#filelist = glob.glob('Bulk/*.jpg')\n",
        "#good_im = np.array([np.array(Image.open(fname).convert('LA')) for fname in filelist])   # Already converts to grayscale but has an extra dummy layer\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0klQ-Nc_qk8f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "filelist = glob.glob('Bulk/*.jpg')\n",
        "data = []\n",
        "for file in filelist:\n",
        "    img = cv2.imread(file)\n",
        "    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "    data.append(img_gray)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nR1yG67Wq3hl",
        "colab_type": "code",
        "outputId": "37704d9f-f94b-4c80-cae0-2648f9aa1ccc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "np.shape(data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2073, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-jC6c0vr2IV",
        "colab_type": "code",
        "outputId": "ec636fd4-5150-4b63-9e01-7a04ba623264",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data_array = np.array(data)\n",
        "data_array.shape, type(data_array)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2073, 28, 28), numpy.ndarray)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_nUXMpaJZDsT",
        "colab_type": "code",
        "outputId": "20d653ca-1ad5-48d9-8c55-0ebdd988e9d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.imshow(data_array[56,:,:])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb14e0f73d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qsrqvknFhtKx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# split the batch into training and test set: 80-20 partition\n",
        "\n",
        "batch_size = len(data_array[:,0,0])\n",
        "train_size = int(math.ceil(batch_size*0.8))\n",
        "test_size = batch_size - train_size\n",
        "\n",
        "x_train = data_array[0:train_size,:,:]\n",
        "x_test = data_array[train_size:,:,:]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0e0_IjzgWUE",
        "colab_type": "code",
        "outputId": "b7d05746-30e0-4016-846e-b149541a8f2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_size, test_size"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1659, 414)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMAIj_PSjFBb",
        "colab_type": "code",
        "outputId": "564ef7fe-e2f2-444d-995f-cfb82365abc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_train.shape, x_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1659, 28, 28), (414, 28, 28))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eodRbWWM1RkA",
        "colab_type": "code",
        "outputId": "b630386e-8256-4a9a-8aa4-a098ba143ca6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import keras\n",
        "#from keras.datasets import cifar10\n",
        "#from keras.datasets import mnist\n",
        "\n",
        "from matplotlib import pyplot\n",
        "from matplotlib.pyplot import imshow\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# https://keras.io/datasets/#mnist-database-of-handwritten-digits\n",
        "# load mnist in a grayscale format bunch of images\n",
        "#(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')\n",
        "\n",
        "for i in range(0, 9):\n",
        "    pyplot.subplot(330 + 1 + i)\n",
        "    imshow(x_train[i])\n",
        "    imgplot = pyplot.imshow(x_train[i])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('x_train shape:', (1659, 28, 28))\n",
            "(1659, 'train samples')\n",
            "(414, 'test samples')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lDLnmAbyWjEmy97XwBl1I5rLVCnSZ2Iw2DJgZaAf77HImGtSAwuWow3Ne4gIV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\nhHFiU0R9p2BTtnXtmhwet824UUE7VCxf4F6u5T0siL+hfCxznVh7pCK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kTSakjCmUpZhb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\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sl7CKyEN1RkR",
        "colab_type": "code",
        "outputId": "7644df78-5248-4885-9a4c-530a82dfe184",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "print \"training set shape is\", x_train.shape\n",
        "#print y_train.shape\n",
        "\n",
        "# since we deal with square images\n",
        "image_size = x_train.shape[1]\n",
        "\n",
        "# inspect the format of x and y\n",
        "print \"First image in the training set\"\n",
        "pyplot.imshow(x_train[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training set shape is (1659, 28, 28)\n",
            "First image in the training set\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb14ddf0810>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vRKZI/EJkisQvRKZI/EJkisQvRKZI/EJkSl99/maziuePpctvVy/xeshTz6b9z6ELvERY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\nvxCZIvELkSn/B5CXAH3TgsAnAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeFfmpC81RkZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "initial_seed = 1234\n",
        "np.random.seed(initial_seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jchWKhYV1Rkg",
        "colab_type": "code",
        "outputId": "6fe09ebd-c28f-435b-afa2-5dd3b7bbd735",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print np.amax(x_train)\n",
        "# normalize the pixel values to have everything between 0 and 1\n",
        "x_train = x_train.astype('float32') / 255\n",
        "x_test = x_test.astype('float32') / 255"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "255\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGigvMju1Rkl",
        "colab_type": "code",
        "outputId": "deb66439-2f16-4736-9d2b-7137e50ee71d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# https://github.com/keras-team/keras/blob/master/examples/mnist_denoising_autoencoder.py\n",
        "# https://blog.keras.io/building-autoencoders-in-keras.html\n",
        "# x_train_ = np.reshape(x_train, [-1, image_size, image_size, 1])\n",
        "x_train_ = np.reshape(x_train, (len(x_train), image_size, image_size, 1))\n",
        "print \"Reshaped train from\", x_train.shape, \"to\", x_train_.shape\n",
        "\n",
        "#x_test_ = np.reshape(x_test, [-1, image_size, image_size, 1])\n",
        "x_test_ = np.reshape(x_test, (len(x_test), image_size, image_size, 1))\n",
        "print \"Reshaped test from\", x_test.shape, \"to\", x_test_.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reshaped train from (1659, 28, 28) to (1659, 28, 28, 1)\n",
            "Reshaped test from (414, 28, 28) to (414, 28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EtzvEDk-1Rkt",
        "colab_type": "code",
        "outputId": "c33ea8c7-9615-434a-8157-82771a98af4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Generate corrupted MNIST images by adding noise with normal dist\n",
        "# centered at 0.5 and std=0.5\n",
        "noise = np.random.normal(loc=0.5,\n",
        "                         scale=0.5,\n",
        "                         size=x_train_.shape)\n",
        "print \"x_train noise.shape\", noise.shape\n",
        "x_train_noisy = x_train_ + noise\n",
        "noise = np.random.normal(loc=0.5,\n",
        "                         scale=0.5,\n",
        "                         size=x_test_.shape)\n",
        "x_test_noisy = x_test_ + noise\n",
        "print \"x_test noise.shape\", noise.shape\n",
        "\n",
        "# make sure the data are in the [0,1] range by clipping lower and higher ones\n",
        "# https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.clip.html\n",
        "x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n",
        "x_test_noisy = np.clip(x_test_noisy, 0., 1.)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train noise.shape (1659, 28, 28, 1)\n",
            "x_test noise.shape (414, 28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDGHPwaD1Rky",
        "colab_type": "text"
      },
      "source": [
        "Let's see how noisy became the first training entry after adding the noise:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GuvN3LbP1Rk0",
        "colab_type": "code",
        "outputId": "e69b8260-06f4-48cc-e28d-8cb91a8d09c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "pyplot.imshow(\n",
        "    np.reshape(x_train_noisy[0], (1, image_size, image_size))[0]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb14dd653d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8zdmnp5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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJOmuO2H1Rk7",
        "colab_type": "code",
        "outputId": "c90b2d0b-92f8-45b7-c3b2-e115b6c4c4cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        }
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from keras.constraints import maxnorm\n",
        "from keras.optimizers import SGD, rmsprop\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D, UpSampling2D\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(16, (3, 3), \n",
        "                 input_shape=x_train_.shape[1:], # (28, 28)\n",
        "                 activation='relu',\n",
        "                 padding='same'))\n",
        "model.add(MaxPooling2D((2, 2), padding=\"same\"))\n",
        "model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n",
        "model.add(MaxPooling2D((2, 2), padding=\"same\"))\n",
        "model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n",
        "model.add(MaxPooling2D((2, 2), padding=\"same\"))\n",
        "\n",
        "# ** encoded representation **\n",
        "# at this point the representation is (4, 4, 8) i.e. 128-dimensional\n",
        "\n",
        "model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n",
        "model.add(UpSampling2D((2, 2)))\n",
        "model.add(Conv2D(8, (3, 3), activation='relu', padding='same'))\n",
        "model.add(UpSampling2D((2, 2)))\n",
        "model.add(Conv2D(16, (3, 3), activation='relu'))\n",
        "model.add(UpSampling2D((2, 2)))\n",
        "model.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))\n",
        "          \n",
        "model.compile(optimizer='adadelta', loss='binary_crossentropy')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W1020 19:09:57.593410 140400888121216 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W1020 19:09:57.604981 140400888121216 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W1020 19:09:57.615333 140400888121216 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W1020 19:09:57.662156 140400888121216 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "W1020 19:09:57.707104 140400888121216 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
            "\n",
            "W1020 19:09:57.749277 140400888121216 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "W1020 19:09:57.765497 140400888121216 module_wrapper.py:139] From /usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py:3376: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "W1020 19:09:57.771167 140400888121216 deprecation.py:323] From /usr/local/lib/python2.7/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t3TK_PDc1RlA",
        "colab_type": "code",
        "outputId": "1b005386-a39e-4558-eabf-f2f727d7306e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        }
      },
      "source": [
        "model.fit(x_train_noisy, \n",
        "          x_train_,\n",
        "          epochs=250,\n",
        "          batch_size=128,\n",
        "          shuffle=True,\n",
        "          validation_data=(x_test_noisy, x_test_))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 1659 samples, validate on 414 samples\n",
            "Epoch 1/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6399 - val_loss: 0.6594\n",
            "Epoch 2/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6438 - val_loss: 0.6752\n",
            "Epoch 3/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6429 - val_loss: 0.6310\n",
            "Epoch 4/250\n",
            "1659/1659 [==============================] - 2s 2ms/step - loss: 0.6490 - val_loss: 0.6695\n",
            "Epoch 5/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6558 - val_loss: 0.6406\n",
            "Epoch 6/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6477 - val_loss: 0.6716\n",
            "Epoch 7/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6398 - val_loss: 0.6397\n",
            "Epoch 8/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6358 - val_loss: 0.6337\n",
            "Epoch 9/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6713 - val_loss: 0.6310\n",
            "Epoch 10/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6514 - val_loss: 0.6724\n",
            "Epoch 11/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6411 - val_loss: 0.6450\n",
            "Epoch 12/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6345 - val_loss: 0.6388\n",
            "Epoch 13/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6377 - val_loss: 0.6531\n",
            "Epoch 14/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6453 - val_loss: 0.6333\n",
            "Epoch 15/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6393 - val_loss: 0.6569\n",
            "Epoch 16/250\n",
            "1659/1659 [==============================] - 3s 2ms/step - loss: 0.6558 - val_loss: 0.6731\n",
            "Epoch 17/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6637 - val_loss: 0.6437\n",
            "Epoch 18/250\n",
            "1659/1659 [==============================] - 2s 1ms/step - loss: 0.6493 - val_loss: 0.6722\n",
            "Epoch 19/250\n",
            "1152/1659 [===================>..........] - ETA: 0s - loss: 0.6576"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypiLXB9s1RlI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# example prediction on the corrupted test images\n",
        "decoded = model.predict(\n",
        "    # only predict the first element as example\n",
        "    np.reshape(x_test_noisy[0], (1, image_size, image_size, 1))\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6nACxg8Z1RlP",
        "colab_type": "code",
        "outputId": "5c63dbf6-7eaf-419f-9262-25202372be6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "pyplot.imshow(\n",
        "    np.reshape(x_test_noisy[0], (1, image_size, image_size))[0]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb14dcd50d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZfzTJ3RI+M6sQucsP8uhuInJj8RN5isVP5CkWP5GnWPxEnmLxE3mKxU/kqcAtvdF08a/s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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RLL12L1L1RlV",
        "colab_type": "code",
        "outputId": "2228c8fd-4166-40bb-a490-7b25c69dc2ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "pyplot.imshow(\n",
        "    np.reshape(decoded, (1, image_size, image_size))[0]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb14dcb6c90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAAESZJREFUeJzt3VuMXdV9x/Hff87cPDM2HnOxXeOE\nS61GiCqknaBKIRURTQooEuQFhYfIlVCcB5CSKg9F5KG8FVVNojxEqE6xQqqUtFKC4AE1IVYkFKmK\nGBA1GEIM1AQb43HwZTwznplz+fdhNmEws/9rfO54fT/SaM6cdfbe/zlzfrPPzNprLXN3AcjPQK8L\nANAbhB/IFOEHMkX4gUwRfiBThB/IFOEHMkX4gUwRfiBTg908WGV83Ic2b+nmIZHgI43EAyzRnjhA\ncHoZGqyFm9Yb8bnJE7UNDJR/b5WBuPBGI953I3ns5vef+r6ii3JrJ06rfnY+8UNb0VL4zexWSd+T\nVJH0b+7+UPT4oc1btPPev2/lkGiz+jXnwvZGNRHAWtxuQ+UB3H7F6XDbucWRsH25Gr98N4wsl7ZN\njsXf9+ziaNi+mDj2xOhS2B59b8vL8b7rtUpp29FvfT/cdrWm3/abWUXS9yXdJuk6SXeb2XXN7g9A\nd7XyN/+Nkl5z9zfcfVnSTyTd0Z6yAHRaK+HfIemtVV8fKe77ADPbY2bTZjZdn59v4XAA2qnj/+13\n973uPuXuU5Xx8U4fDsA6tRL+o5J2rvr6yuI+AB8BrYT/WUm7zOxqMxuW9GVJT7anLACd1nRXn7vX\nzOw+ST/XSlffPnc/2LbK0BXjY3GX1NnZDfEOEj3KYxPl+79608lw29/VL493nnDN5LulbdtGz4bb\nHqj/SUvH/sTkTNj++7nJ8raZ+FoYT1yasV4t9fO7+1OSnmpPKQC6ict7gUwRfiBThB/IFOEHMkX4\ngUwRfiBTXR3Pj/5TDYaHSpLXEx35iT7narV8/wPWpg7rJrR6bLN4vP5AYqKDmbMTpW315RZ+Jol5\nCFbjzA9kivADmSL8QKYIP5Apwg9kivADmaKrL3OpaaI3bIyH/Nbrqem1y9sqie6yK8bnwvZztaGw\nfTCYunsgdeyxeMjvQm04bE/tvxZ0sY6Ml886LMXTflslNZf6+zjzA5ki/ECmCD+QKcIPZIrwA5ki\n/ECmCD+QKfr5M/fpK98M2w/PXhq2n6vGfe07N50qbbtty4Fw2/2n43Vfz1TjacU/O3motG3X8Dvh\ntj/Xn4ftJ5bLh+RK0s2bfxu2n6uXP2+HZ+Opu5dq5bGtBNc2nI8zP5Apwg9kivADmSL8QKYIP5Ap\nwg9kivADmWqpn9/MDks6K6kuqebuU+0oCt2zfXQ2bE/186ds21A+Ln7b4JmW9p2yY6j8GoNtg/F4\n/ZShxNTfOwbLjy1JOzacLm07uTQWbrswUD6XQGoegdXacZHP59z9D23YD4Au4m0/kKlWw++SfmFm\nz5nZnnYUBKA7Wn3bf5O7HzWzKyQ9bWa/dfdnVj+g+KWwR5IGL5ls8XAA2qWlM7+7Hy0+z0h6XNKN\nazxmr7tPuftUZXy8lcMBaKOmw29m42a28b3bkr4g6aV2FQags1p5279V0uNm9t5+/sPd/7stVQHo\nuKbD7+5vSPpkG2tBD5xOjImvNuI3h/XEktCz1dHStrON8jZJqjXipapridpm6+X7XxyM911PvCmu\netw+7/G8/tuHy/v5D1UuD7etB993aunw1ejqAzJF+IFMEX4gU4QfyBThBzJF+IFMdXfqbpM87mGJ\nN6+3rxSsWG7EL4GFpbjLqhosNb2y//L2qnf25Rftv5HoqpuoxEuT1xJdhSljA+XLcF86shBuO1wp\nD0K0LPn5OPMDmSL8QKYIP5Apwg9kivADmSL8QKYIP5Cprvbze6qfPzEaMeqSTnTLosSR+c1h+3I1\nfonUa/H5Y2ZhY2nbkNXCbVPTUKfao/1fM7QYbnvJYNzXvpS4PmLnYPmQXUnaVimfMv3w4mXhtgPB\ntOGVxJTiH9jPuh8J4KJC+IFMEX4gU4QfyBThBzJF+IFMEX4gU10fz18fKe+bTc46HHZhxlNIY21L\ntdb68RuJ6bMXg/1XUhd2tKgSvKDGLB6PH423l6SxSty+MXENQ2LG867gzA9kivADmSL8QKYIP5Ap\nwg9kivADmSL8QKaS/fxmtk/SFyXNuPv1xX1bJP2npKskHZZ0l7ufSh7NXD4UdNYnOj/DbRe6e8nC\nxeJcdShsb3jiZ5IYPr4UzAdQ7/C1GfWg9omBeHnwocQiEan28YH4e3s7uP4hNVdAtHS5J35eq63n\nzP9DSbeed9/9kva7+y5J+4uvAXyEJMPv7s9IOnne3XdIerS4/aikO9tcF4AOa/Zv/q3ufqy4/Y6k\nrW2qB0CXtPwPP3d3BbPvmdkeM5s2s+n63HyrhwPQJs2G/7iZbZek4vNM2QPdfa+7T7n7VGVivMnD\nAWi3ZsP/pKTdxe3dkp5oTzkAuiUZfjN7TNL/SPozMztiZvdIekjS583skKS/Kb4G8BGS7Bx397tL\nmm654KM1TAOL5b9vUlOO10c7O/47R/XEtRX1M8Nhu9Xi7WsT5Qsq1D0+97x0clvYvrAU1/a3lzZ/\n7ceBuZ1h+8zSRLyDyefC5kUvr+31ucvDbc8sl1+jkLpGYDWu8AMyRfiBTBF+IFOEH8gU4QcyRfiB\nTHV1HKy5VFlsfhinnYyHn+LCnXl9MmwfauHnJUkLb24qbfun0dvCbY8fj5cP90TP77++8dnStjMf\nj7viDp6OuxmjKckl6eGTnw7b3166pLTt92fi77taLx/SW6uv/3zOmR/IFOEHMkX4gUwRfiBThB/I\nFOEHMkX4gUx1d75rTw/bjVi8KjKaMDjf2emzB+fK93/i1MZ44zPxdR0DidfSu4Plw25fuWx7uO3M\nbDxkt1qNl/h+dVM8reXp5Q2lbXPz8bTi9aAvv3EBa39z5gcyRfiBTBF+IFOEH8gU4QcyRfiBTBF+\nIFOsa43eeTvuzx5cbu0ahMbxkdK25zfvCLc9N1e+rSR5oj/94Il4PoBo6fPqYiKW0bHp5weQQviB\nTBF+IFOEH8gU4QcyRfiBTBF+IFPJfn4z2yfpi5Jm3P364r4HJX1V0oniYQ+4+1OdKhIXp1bWcGh1\n/3ML8TUGmk3MJVCPa18Yia8TMCtfdMDOxrG06Nht7uf/oaRb17j/u+5+Q/FB8IGPmGT43f0ZSSe7\nUAuALmrlb/77zOyAme0zs3jNJwB9p9nwPyzpWkk3SDom6dtlDzSzPWY2bWbT9fn5Jg8HoN2aCr+7\nH3f3urs3JP1A0o3BY/e6+5S7T1XGx5utE0CbNRV+M1s99emXJL3UnnIAdMt6uvoek3SzpMvM7Iik\nf5R0s5ndIMklHZb0tQ7WCKADkuF397vXuPuRDtQCdE39yFjYPriU6C9PNDeOxdcRlPfyr+PYgQtZ\nF4Mr/IBMEX4gU4QfyBThBzJF+IFMEX4gU0zdjSxVznV4OHGH918q6kM8D2d+IFOEH8gU4QcyRfiB\nTBF+IFOEH8gU4QcyRfiBTBF+IFOEH8gU4QcyRfiBTBF+IFOEH8gU4Qcy1Vfj+QfnOzcGOlgRWZJU\nnbiAgdDARYAzP5Apwg9kivADmSL8QKYIP5Apwg9kivADmUqG38x2mtmvzOxlMztoZl8v7t9iZk+b\n2aHi82Sni/VKCx8WfwC5Wc+Zvybpm+5+naS/knSvmV0n6X5J+919l6T9xdcAPiKS4Xf3Y+7+fHH7\nrKRXJO2QdIekR4uHPSrpzk4VCaD9LuhvfjO7StKnJP1G0lZ3P1Y0vSNpa1srA9BR6w6/mU1I+qmk\nb7j77Oo2d3eVrBJmZnvMbNrMpuvz8y0VC6B91hV+MxvSSvB/7O4/K+4+bmbbi/btkmbW2tbd97r7\nlLtPVcbH21EzgDZYz3/7TdIjkl5x9++sanpS0u7i9m5JT7S/PACdsp4hvZ+R9BVJL5rZC8V9D0h6\nSNJ/mdk9kt6UdFfL1XSyy43uPOADkuF391+rPDq3tLccAN3CFX5Apgg/kCnCD2SK8AOZIvxApgg/\nkKm+mrp77QuE32f17pQB5IAzP5Apwg9kivADmSL8QKYIP5Apwg9kivADmepqP//IyZqufexkaXv9\n4Kvh9rVb/rK0bfhEPEXY7Cc2h+2ndsW/B2vj5RchWCPcFBehTa/H7V5J7CC4pmUgcT3LmT9N7Hud\nOPMDmSL8QKYIP5Apwg9kivADmSL8QKYIP5CprvbzN4YrWvjYptL2saOXhNvX6+Wdo0tXxKsBDdQS\nkwUkfg2GcwmwJkB2PJGc2mjzL4rBxcRrtU048wOZIvxApgg/kCnCD2SK8AOZIvxApgg/kKlkP7+Z\n7ZT0I0lbtTIKea+7f8/MHpT0VUknioc+4O5PRftqDJkWrig/5Oiuj4W1VDeWbztQjftGR05Vw/bh\n0/EA7MpSeZsnfoWe+mQ8QHv43dTgb/Sb1M989FQ8yUNtJLgOILHvgeVg2wu4RGA9F/nUJH3T3Z83\ns42SnjOzp4u277r7v6z/cAD6RTL87n5M0rHi9lkze0XSjk4XBqCzLuhvfjO7StKnJP2muOs+Mztg\nZvvMbLJkmz1mNm1m07XFeKotAN2z7vCb2YSkn0r6hrvPSnpY0rWSbtDKO4Nvr7Wdu+919yl3nxoc\nja+/B9A96wq/mQ1pJfg/dvefSZK7H3f3urs3JP1A0o2dKxNAuyXDb2Ym6RFJr7j7d1bdv33Vw74k\n6aX2lwegU9bz3/7PSPqKpBfN7IXivgck3W1mN2ilc+GwpK+ldjSw7Np4ZLm03c7F3XGLkxOlbZv+\nbzHcdu7KkbC9viFsVnWivHtl/FjcvzL2Vvw018a6M4QT7dOoxEN260Px9kML5T/z1LTf5u0ZQ76e\n//b/WmuPWA/79AH0N67wAzJF+IFMEX4gU4QfyBThBzJF+IFMdXXq7upG05HPDZe2VxbjvvjBhfK2\n+vBouO3IbNyXfumL8TUGQwu10rZzl5d/T5J0rsaQ3YvN0pojWd7XGIr74qsby9vr8cspnEbeLuCS\nEc78QKYIP5Apwg9kivADmSL8QKYIP5Apwg9kyty7N5bczE5IenPVXZdJ+kPXCrgw/Vpbv9YlUVuz\n2lnbx9398vU8sKvh/9DBzabdfapnBQT6tbZ+rUuitmb1qjbe9gOZIvxApnod/r09Pn6kX2vr17ok\namtWT2rr6d/8AHqn12d+AD3Sk/Cb2a1m9qqZvWZm9/eihjJmdtjMXjSzF8xsuse17DOzGTN7adV9\nW8zsaTM7VHxODC7tam0PmtnR4rl7wcxu71FtO83sV2b2spkdNLOvF/f39LkL6urJ89b1t/1mVpH0\nO0mfl3RE0rOS7nb3l7taSAkzOyxpyt173idsZn8taU7Sj9z9+uK+f5Z00t0fKn5xTrr7P/RJbQ9K\nmuv1ys3FgjLbV68sLelOSX+nHj53QV13qQfPWy/O/DdKes3d33D3ZUk/kXRHD+roe+7+jKST5919\nh6RHi9uPauXF03UltfUFdz/m7s8Xt89Kem9l6Z4+d0FdPdGL8O+Q9Naqr4+ov5b8dkm/MLPnzGxP\nr4tZw9Zi2XRJekfS1l4Ws4bkys3ddN7K0n3z3DWz4nW78Q+/D7vJ3f9C0m2S7i3e3vYlX/mbrZ+6\na9a1cnO3rLGy9B/18rlrdsXrdutF+I9K2rnq6yuL+/qCux8tPs9Ielz9t/rw8fcWSS0+z/S4nj/q\np5Wb11pZWn3w3PXTite9CP+zknaZ2dVmNizpy5Ke7EEdH2Jm48U/YmRm45K+oP5bffhJSbuL27sl\nPdHDWj6gX1ZuLltZWj1+7vpuxWt37/qHpNu18h//1yV9qxc1lNR1jaT/LT4O9ro2SY9p5W1gVSv/\nG7lH0qWS9ks6JOmXkrb0UW3/LulFSQe0ErTtPartJq28pT8g6YXi4/ZeP3dBXT153rjCD8gU//AD\nMkX4gUwRfiBThB/IFOEHMkX4gUwRfiBThB/I1P8D33JpEHRrqtkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D750C6WxJR49",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IkuHeWXaJTeb",
        "colab_type": "text"
      },
      "source": [
        "# **Proof of Concept #3**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SBwaJJLCJT67"
      },
      "source": [
        "# **NASA Meteorite Landing dataset: Recovering/approximating/imputing missing values**\n",
        "\n",
        "# **# Algorithm: Deep Convolutional Generative Adversarial Network (DCGAN)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBWlq21JaepW",
        "colab_type": "text"
      },
      "source": [
        "# **Our approach**\n",
        "\n",
        "Here, we are transforming each sample (row) of the Meteorite CSV dataset into an image. The dataset has 45716 rows and 9 columns, including metorites' names and IDs. Our algorithm transforms each row it into a 3x3 image and zero pads the outer periphery of the 3x3 matrix (to make it a 7x7 matrix) so that each row fits into a 2D Convolutional filter. Then, once the training and evaluation is complete, we plan to recover the original data (CSV) from the images generated by the DCGAN.\n",
        "\n",
        "# **Challenges**\n",
        "\n",
        "There are mainly 2 challenges that we faced (#1 and #2) while implementing this demo. #3 is our planned future work.\n",
        "\n",
        "\n",
        "\n",
        "1.   The dataset is probably not big enough for a regular Convolutional Neural Network (CNN)-based DCGAN architecture.\n",
        "2.   We had to encode all categorical values to numeric values, including the names. The problem here was our label encoder generated different numeric values for the meteorite names compared to their IDs. We chose not to exclude these two columns from the dataset for the sake of architectural simplicity at this moment.\n",
        "\n",
        "\n",
        "# **Future work**\n",
        "Due to time constraints, we are yet to recover the generated csv data from the output images and evaluate whether the algorithm converged or not. If it did not converge, then we plan to tune the hyperparameters, modify the CNN architecture if necessary and re-train the DCGAN algorithm.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3wPcFbxLJT7B"
      },
      "source": [
        "### Import TensorFlow and other libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CytkWIlIJT7H",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "e4b89705-dcef-4733-c024-9b1df29e0760",
        "id": "Yr_IsI0XJT7S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.0.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "ef6fbde2-1ea7-48ec-d8a4-b166bd1323e4",
        "id": "f2JlVi_aJT7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "# To generate GIFs\n",
        "!pip install imageio"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: imageio in /usr/local/lib/python3.6/dist-packages (2.4.1)\n",
            "Requirement already satisfied: numpy in /tensorflow-2.0.0/python3.6 (from imageio) (1.17.3)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from imageio) (4.3.0)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->imageio) (0.46)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Lx-gzPmgJT7h",
        "colab": {}
      },
      "source": [
        "import glob\n",
        "import imageio\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import PIL\n",
        "import time\n",
        "from tensorflow.keras import layers\n",
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import normalize\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from IPython import display"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "x4TGihDDJT7k"
      },
      "source": [
        "### Load and prepare NASA Meteorite dataset for **training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhULQyuGek0V",
        "colab_type": "code",
        "outputId": "19389bf7-d168-48e4-fc1b-502998c223e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "##### Initialize training dataset #####\n",
        "\n",
        "# mount google drive location where you saved a .zip archive of your folder that contains images; then unzip the file\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jXTHBn3c7ZSa",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "9305c530-7c6e-426f-884c-8dab665ed644",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "cd /content/drive/My\\ Drive/Colab\\ Notebooks/NASA-challenge-sample-datasets/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Colab Notebooks/NASA-challenge-sample-datasets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vClB8Xo-gKA8",
        "colab_type": "text"
      },
      "source": [
        "**Load dataset and encode categorical values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIOmIx7jvLgz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load dataset and encode categorical values\n",
        "import pandas as pd\n",
        "df = pd.read_csv('/content/drive/My Drive/Colab Notebooks/NASA-challenge-sample-datasets/Meteorite_Landings_clean.csv')\n",
        "df_filled = df.fillna(0)\n",
        "\n",
        "# Encode categorical values\n",
        "label_enc = preprocessing.LabelEncoder()\n",
        "data_clean = df_filled.apply(lambda series: pd.Series(label_enc.fit_transform(series), index=series.index))\n",
        "#data_clean.to_csv('Meteorite_not_normalized.csv',index=True)\n",
        "\n",
        "# Normalize numeric data only (range: 0-255)\n",
        "first_2_col = data_clean[data_clean.columns[:2]]\n",
        "minmax = MinMaxScaler(feature_range=(0, 255), copy=True)\n",
        "data_norm_minmax = minmax.fit_transform(data_clean[['nametype', 'recclass', 'mass (g)', 'fall', 'year_numeric', 'reclat', 'reclong']])\n",
        "\n",
        "#data_norm = normalize(data_clean[['nametype', 'recclass', 'mass (g)', 'fall', 'year_numeric', 'reclat', 'reclong']])\n",
        "\n",
        "dataset_complete = np.hstack((first_2_col, data_norm_minmax))\n",
        "#np.savetxt(\"Meteorite_clean_minmax.csv\", dataset_complete, delimiter=\",\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NerOTN2vgEJy",
        "colab_type": "text"
      },
      "source": [
        "**Start zero padding the data so that it fits into a 2D Convolutional filter**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZEmbmYxLzNUM",
        "colab_type": "code",
        "outputId": "2ebc0cfe-3dff-4945-f74b-8425313eabdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "## Start zero padding the data so that it fits into a 2D Convolutional filter\n",
        "zeros_vert = np.zeros((3, 2))\n",
        "zeros_hori = np.zeros((2, 7))\n",
        "zeros_vert.shape, zeros_hori.shape, dataset_complete.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3, 2), (2, 7), (45716, 9))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Bnkqr58tA9t",
        "colab_type": "code",
        "outputId": "ad95fe80-9618-4d80-a8b6-7e75c534a455",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Rehsape data and pad zeros to increase dimensionality\n",
        "dataset_intermed = dataset_complete.reshape(dataset_complete.shape[0], 3, 3).astype('float32')\n",
        "dataset_intermed.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45716, 3, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l9qQ9f600Vvj",
        "colab_type": "code",
        "outputId": "bd601fd4-dd5d-4931-a820-c0e689ad3523",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Pad zeros horizontally\n",
        "dataset_hor_pad = []\n",
        "for i in range(dataset_intermed.shape[0]):\n",
        "  dataset_hor_pad_1 = np.hstack((zeros_vert, dataset_intermed[i,:,:], zeros_vert))\n",
        "  dataset_hor_pad.append(dataset_hor_pad_1)\n",
        "\n",
        "dataset_hor_pad = np.array(dataset_hor_pad)\n",
        "dataset_hor_pad_1.shape, dataset_hor_pad.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3, 7), (45716, 3, 7))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qjtbdh854O2_",
        "colab_type": "code",
        "outputId": "bc3a7972-de71-4047-d008-4e9eb23d5165",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Pad zeros vertically\n",
        "dataset_ver_pad = []\n",
        "for i in range(dataset_hor_pad.shape[0]):\n",
        "  dataset_ver_pad_1 = np.vstack((zeros_hori, dataset_hor_pad[i,:,:], zeros_hori))\n",
        "  dataset_ver_pad.append(dataset_ver_pad_1)\n",
        "\n",
        "dataset_padded = np.array(dataset_ver_pad)\n",
        "dataset_ver_pad_1.shape, dataset_padded.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7, 7), (45716, 7, 7))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ko5z0Yqjvw_F",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "2ed930aa-6c70-46ed-f0b1-05ba3c26acd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        }
      },
      "source": [
        "dataset_padded[1111,:,:]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0.        ,   0.        ,   0.        ,   0.        ,\n",
              "          0.        ,   0.        ,   0.        ],\n",
              "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
              "          0.        ,   0.        ,   0.        ],\n",
              "       [  0.        ,   0.        ,   2.        ,   2.        ,\n",
              "        255.        ,   0.        ,   0.        ],\n",
              "       [  0.        ,   0.        , 100.90322876, 157.19761658,\n",
              "        255.        ,   0.        ,   0.        ],\n",
              "       [  0.        ,   0.        ,  80.52631378, 190.19392395,\n",
              "          9.96379566,   0.        ,   0.        ],\n",
              "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
              "          0.        ,   0.        ,   0.        ],\n",
              "       [  0.        ,   0.        ,   0.        ,   0.        ,\n",
              "          0.        ,   0.        ,   0.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Jbmom_jgbX9",
        "colab_type": "text"
      },
      "source": [
        "**Reshape training data, define batch and buffer sizes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iwOatDqmVmV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reshape training data, define batch and buffer sizes\n",
        "dataset = dataset_padded.reshape(dataset_padded.shape[0], 7, 7, 1).astype('float32')\n",
        "\n",
        "# Initialize buffer and batch size\n",
        "BUFFER_SIZE = dataset_padded.shape[0]\n",
        "BATCH_SIZE = 256\n",
        "\n",
        "# BATCH and SHUFFLE the data\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices(dataset).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INhzpXh6F1RP",
        "colab_type": "code",
        "outputId": "36cd4625-1447-426e-d045-eba7e3b487a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "dataset.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(45716, 7, 7, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "WZjMLwFcJT8l"
      },
      "source": [
        "## Create the models\n",
        "\n",
        "Both the generator and discriminator are defined using the [Keras Sequential API](https://www.tensorflow.org/guide/keras#sequential_model)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mI-Ne_5_JT8m"
      },
      "source": [
        "### The Generator\n",
        "\n",
        "The generator uses `tf.keras.layers.Conv2DTranspose` (upsampling) layers to produce an image from a seed (random noise). It starts with a **`Dense` layer that takes this seed as input**, then **upsamples it several times until it reaches the desired image size** of 28x28x1. Notice the `tf.keras.layers.LeakyReLU` activation for each layer, except the output layer which uses tanh."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbuMZGRLnaEE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create generator\n",
        "\n",
        "def make_generator_model():\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.LeakyReLU())\n",
        "\n",
        "    model.add(layers.Reshape((7, 7, 256)))\n",
        "    assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size\n",
        "\n",
        "    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))\n",
        "    assert model.output_shape == (None, 7, 7, 128)\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.LeakyReLU())\n",
        "\n",
        "    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(1, 1), padding='same', use_bias=False))\n",
        "    assert model.output_shape == (None, 7, 7, 64)\n",
        "    model.add(layers.BatchNormalization())\n",
        "    model.add(layers.LeakyReLU())\n",
        "\n",
        "    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(1, 1), padding='same', use_bias=False, activation='tanh'))\n",
        "    assert model.output_shape == (None, 7, 7, 1)\n",
        "\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3UYdT2TdJT8o"
      },
      "source": [
        "**Use the (as yet untrained) generator to create an image.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tAGLa53Hq-54",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "3a31c5ff-02c6-43e3-c439-ea93f624a55f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "generator = make_generator_model()\n",
        "\n",
        "noise = tf.random.normal([1, 100])\n",
        "generated_image = generator(noise, training=False)\n",
        "\n",
        "plt.imshow(generated_image[0, :, :, 0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f2466bbc3c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAC9JJREFUeJzt3X2olvUdx/HPx3N86mjZg4V5JGOr\nWATLcLIwohyFragx9kdBsY2N/qlhbBC1MUbsr+2PqMEYiLo1sqRlQkRrCdksKCvLHnyouTLUHk5l\n5VNTz/G7P85lnEx2X3quh7vv3i84eN/Hq+v7Vfuc33Vd931fX0eEAOQ0pu0GANSHgAOJEXAgMQIO\nJEbAgcQIOJAYAQcSI+BAYgQcSKy3jp32TO6L3qlT6th1R+PHDrZSV5L27R3XWu0xB1orLUmKvoOt\n1e7paa/2wV21RKijA5/u0ODePe60XS3d9U6dotN/e1Mdu+7orOkDrdSVpM0vzWit9nHb2z0Y2//t\nXa3VPnHy3tZq71x9Wit1tyy5s9R2HKIDiRFwIDECDiRGwIHECDiQGAEHEiPgQGIEHEiMgAOJEXAg\nsVIBtz3f9uu2N9u+re6mAFSjY8Bt90j6o6QrJJ0r6Trb59bdGIDRK7OCz5G0OSLejIj9kpZJuqbe\ntgBUoUzAp0vaOuL5tuJ7ALpcZRfZbN9o+wXbLwzt3FPVbgGMQpmAb5c08oPO/cX3viAiFkbE7IiY\n3XN8X1X9ARiFMgF/XtJZts+0PU7StZIerrctAFXoeEeXiBi0fbOkf0jqkbQkItbX3hmAUSt1y6aI\neFTSozX3AqBivJMNSIyAA4kRcCAxAg4kRsCBxAg4kBgBBxIj4EBiBBxIjIADidUz+/TAGPn98bXs\nupN/fdDehM8pb3Sc5lqfiPZqS4rnJrdW++PJ7dWe9lI7c5u37S33780KDiRGwIHECDiQGAEHEiPg\nQGIEHEiMgAOJEXAgMQIOJEbAgcQIOJBYmemiS2wP2H6tiYYAVKfMCv4XSfNr7gNADToGPCJWS9rR\nQC8AKsY5OJBYPeOD9zA+GOgGlQX8C+OD+xgfDHQDDtGBxMq8THa/pGcknWN7m+2f1N8WgCqUmQ9+\nXRONAKgeh+hAYgQcSIyAA4kRcCAxAg4kRsCBxAg4kBgBBxIj4EBiBBxIrJbxwT0TBnX82R/XseuO\npvxhUit1JWnHN9oZmSxJfe8fbK22JE38qL36fcvXtFb7o59e2ErdoXHlRlWzggOJEXAgMQIOJEbA\ngcQIOJAYAQcSI+BAYgQcSIyAA4kRcCAxAg4kVua+6DNsr7K9wfZ62wuaaAzA6JX5sMmgpF9ExIu2\nJ0taa3tlRGyouTcAo1RmfPC7EfFi8XiXpI2SptfdGIDRO6pzcNszJc2S1N7n8wCUVjrgtidJWi7p\nlojYeYTf/3x88OCne6vsEcAxKhVw22M1HO6lEfHQkbYZOT6494TjquwRwDEqcxXdkhZL2hgRd9bf\nEoCqlFnB50q6QdI82+uKr+/W3BeACpQZH/y0pHI3gALQVXgnG5AYAQcSI+BAYgQcSIyAA4kRcCAx\nAg4kRsCBxAg4kBgBBxKrZXxw7OrVgadOrmPXHb31/cFW6krShPdaK60Dl+xqr7ikz95pb2zzGXu+\n1Vrt8Z+2MzZ5zFDJ7eptA0CbCDiQGAEHEiPgQGIEHEiMgAOJEXAgMQIOJEbAgcQIOJAYAQcSKzP4\nYILt52y/XIwPvqOJxgCMXpkPm+yTNC8idhcjjJ62/feIeLbm3gCMUpnBByFpd/F0bPEVdTYFoBpl\nhw/22F4naUDSyohgfDDwFVAq4BExFBHnS+qXNMf2eYdvM3J88NBne6ruE8AxOKqr6BHxiaRVkuYf\n4fc+Hx/cM7Gvqv4AjEKZq+hTbU8pHk+UdJmkTXU3BmD0ylxFnybpHts9Gv6B8EBEPFJvWwCqUOYq\n+iuSZjXQC4CK8U42IDECDiRGwIHECDiQGAEHEiPgQGIEHEiMgAOJEXAgMQIOJEbAgcRqmQ/+9VPf\n00M3/76OXXf0q21Xt1JXktb0fK212m9duLS12pJ09j9/2Frt3t1urfa0X7/dSt3XX/lPqe1YwYHE\nCDiQGAEHEiPgQGIEHEiMgAOJEXAgMQIOJEbAgcQIOJBY6YAX88less090YGviKNZwRdI2lhXIwCq\nV3a6aL+kKyUtqrcdAFUqu4LfJelWSQdr7AVAxcoMH7xK0kBErO2w3efjg3fs4OcA0A3KrOBzJV1t\ne4ukZZLm2b738I1Gjg8+6SQuzgPdoGMSI+L2iOiPiJmSrpX0RERcX3tnAEaNpRZI7Khu2RQRT0p6\nspZOAFSOFRxIjIADiRFwIDECDiRGwIHECDiQGAEHEiPgQGIEHEiMgAOJEXAgMUdE5TuddGJ/nH/p\ngsr3W0bv3vY+i/7O3HGt1R4z2FppSdL4j9qr3Tcw1Frtdy5pp+67v7tb+97e2nFuMis4kBgBBxIj\n4EBiBBxIjIADiRFwIDECDiRGwIHECDiQGAEHEiPgQGKl7otejC3aJWlI0mBEzK6zKQDVOJrBB5dG\nxIe1dQKgchyiA4mVDXhIetz2Wts3HmmDkeODB/ftqa5DAMes7CH6RRGx3fapklba3hQRq0duEBEL\nJS2Uhj8PXnGfAI5BqRU8IrYXvw5IWiFpTp1NAahGx4Db7rM9+dBjSZdLeq3uxgCMXplD9NMkrbB9\naPv7IuKxWrsCUImOAY+INyV9s4FeAFSMl8mAxAg4kBgBBxIj4EBiBBxIjIADiRFwIDECDiRGwIHE\nCDiQ2NHc0aW0oXHWpzNr2XVHp6/6uJW6krTv9I7TXGtzyjPt/H0f8uHs9kb4nvy39mYnT/73hFbq\nvr+v3Has4EBiBBxIjIADiRFwIDECDiRGwIHECDiQGAEHEiPgQGIEHEiMgAOJlQq47Sm2H7S9yfZG\n2xfW3RiA0Sv7CYW7JT0WET+wPU7ScTX2BKAiHQNu+wRJF0v6kSRFxH5J++ttC0AVyhyinynpA0l/\ntv2S7UXFjLIvGDk+eOgzxgcD3aBMwHslXSDpTxExS9IeSbcdvlFELIyI2RExu2fil/IPoAVlAr5N\n0raIWFM8f1DDgQfQ5ToGPCLek7TV9jnFt74jaUOtXQGoRNmr6D+TtLS4gv6mpB/X1xKAqpQKeESs\nkzS75l4AVIx3sgGJEXAgMQIOJEbAgcQIOJAYAQcSI+BAYgQcSIyAA4kRcCAxR0T1O7U/kPT2Mf7n\np0j6sMJ2qE3tjLXPiIipnTaqJeCjYfuFiGjlfe/Upna22hyiA4kRcCCxbgz4QmpTm9rV6LpzcADV\n6cYVHEBFuirgtufbft32ZttfunNrjXWX2B6w/VpTNUfUnmF7le0NttfbXtBg7Qm2n7P9clH7jqZq\nj+ihp7gd9yMN191i+1Xb62y/0HDtxiYFdc0huu0eSW9IukzDd3J9XtJ1EVH7DR5tXyxpt6S/RsR5\nddc7rPY0SdMi4kXbkyWtlfS9hv7cltQXEbttj5X0tKQFEfFs3bVH9PBzDd8O7PiIuKrBulskzY6I\nxl8Ht32PpKciYtGhSUER8UkdtbppBZ8jaXNEvFlMT1km6ZomCkfEakk7mqh1hNrvRsSLxeNdkjZK\nmt5Q7YiI3cXTscVXYz/xbfdLulLSoqZqtm3EpKDF0vCkoLrCLXVXwKdL2jri+TY19D96t7A9U9Is\nSWv+95aV1uyxvU7SgKSVI+5/34S7JN0q6WCDNQ8JSY/bXmv7xgbrlpoUVJVuCvj/NduTJC2XdEtE\n7GyqbkQMRcT5kvolzbHdyCmK7askDUTE2ibqHcFFEXGBpCsk3VScpjWh1KSgqnRTwLdLmjHieX/x\nvfSK89/lkpZGxENt9FAcJq6SNL+hknMlXV2cCy+TNM/2vQ3VVkRsL34dkLRCw6eITWh0UlA3Bfx5\nSWfZPrO48HCtpIdb7ql2xYWuxZI2RsSdDdeeantK8Xiihi9wbmqidkTcHhH9ETFTw//WT0TE9U3U\ntt1XXNBUcXh8uaRGXkFpelJQ2ckmtYuIQds3S/qHpB5JSyJifRO1bd8v6RJJp9jeJuk3EbG4idoa\nXslukPRqcS4sSb+MiEcbqD1N0j3FKxhjJD0QEY2+XNWS0yStGP7Zql5J90XEYw3Wb2xSUNe8TAag\net10iA6gYgQcSIyAA4kRcCAxAg4kRsCBxAg4kBgBBxL7L/NFGEihtI3yAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "D_8k68hjJT8q"
      },
      "source": [
        "### The Discriminator\n",
        "\n",
        "The discriminator is a CNN-based image classifier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "len8EoxnuKS-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create a discriminator to police the generator (notice the input shape of the first Conv2D layer)\n",
        "\n",
        "def make_discriminator_model():\n",
        "    model = tf.keras.Sequential()\n",
        "    model.add(layers.Conv2D(32, (5, 5), strides=(2, 2), padding='same',\n",
        "                                     input_shape=[7, 7, 1]))\n",
        "    model.add(layers.LeakyReLU())\n",
        "    model.add(layers.Dropout(0.3))\n",
        "\n",
        "    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same'))\n",
        "    model.add(layers.LeakyReLU())\n",
        "    model.add(layers.Dropout(0.3))\n",
        "\n",
        "    model.add(layers.Flatten())\n",
        "    model.add(layers.Dense(1))\n",
        "\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GW-tP5LoJT8s"
      },
      "source": [
        "**Use the (as yet untrained) discriminator to classify the generated images as real or fake. The model will be trained to output positive values for real images, and negative values for fake images.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aKewzWxktMzb",
        "colab_type": "code",
        "cellView": "both",
        "outputId": "1d54c328-847c-4ef3-ad9d-fa44c4ff3718",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "discriminator = make_discriminator_model()\n",
        "decision = discriminator(generated_image)\n",
        "print (decision)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor([[-0.00597883]], shape=(1, 1), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GHJw0oziJT8u"
      },
      "source": [
        "## Define the loss and optimizers\n",
        "\n",
        "Define loss functions and optimizers for both models.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "N7pxVNpJJT8u",
        "colab": {}
      },
      "source": [
        "# This method returns a helper function to compute cross entropy loss\n",
        "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yS8AY32aJT8x"
      },
      "source": [
        "### Discriminator loss\n",
        "\n",
        "This method quantifies how well the discriminator is able to distinguish real images from fakes. It compares the discriminator's predictions on real images to an array of 1s, and the discriminator's predictions on fake (generated) images to an array of 0s."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yIuZ1jUYJT8y",
        "colab": {}
      },
      "source": [
        "def discriminator_loss(real_output, fake_output):\n",
        "    real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n",
        "    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n",
        "    total_loss = real_loss + fake_loss\n",
        "    return total_loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ik5qxVj5JT81"
      },
      "source": [
        "### Generator loss\n",
        "The generator's loss quantifies how well it was able to trick the discriminator. Intuitively, if the generator is performing well, the discriminator will classify the fake images as real (or 1). Here, we will compare the discriminators decisions on the generated images to an array of 1s."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "V29EcGwtJT82",
        "colab": {}
      },
      "source": [
        "def generator_loss(fake_output):\n",
        "    return cross_entropy(tf.ones_like(fake_output), fake_output)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JlWL9N2LJT86"
      },
      "source": [
        "The discriminator and the generator optimizers are different since we will train two networks separately."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pwAPk_rhJT87",
        "colab": {}
      },
      "source": [
        "generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n",
        "discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "abECcSA9JT89"
      },
      "source": [
        "### Save checkpoints\n",
        "Save and restore models, which can be helpful in case a long running training task is interrupted."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "R98MWKQ6JT8-",
        "colab": {}
      },
      "source": [
        "checkpoint_dir = './training_checkpoints'\n",
        "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n",
        "checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n",
        "                                 discriminator_optimizer=discriminator_optimizer,\n",
        "                                 generator=generator,\n",
        "                                 discriminator=discriminator)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hEG_qhsoJT9F"
      },
      "source": [
        "## Define the training loop\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbOzaGf7wde8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "EPOCHS = 50\n",
        "noise_dim = 100\n",
        "num_examples_to_generate = 16\n",
        "\n",
        "# We will reuse this seed overtime (so it's easier)\n",
        "# to visualize progress in the animated GIF)\n",
        "seed = tf.random.normal([num_examples_to_generate, noise_dim])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Chg4RIG-JT9J"
      },
      "source": [
        "The training loop begins with generator receiving a random seed as input. That seed is used to produce an image. The discriminator is then used to classify real images (drawn from the training set) and fakes images (produced by the generator). The loss is calculated for each of these models, and the gradients are used to update the generator and discriminator."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Sfrgc2CsJT9K",
        "colab": {}
      },
      "source": [
        "# Notice the use of `tf.function`\n",
        "# This annotation causes the function to be \"compiled\".\n",
        "@tf.function\n",
        "def train_step(images):\n",
        "    noise = tf.random.normal([BATCH_SIZE, noise_dim])\n",
        "\n",
        "    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
        "      generated_images = generator(noise, training=True)\n",
        "\n",
        "      real_output = discriminator(images, training=True)\n",
        "      fake_output = discriminator(generated_images, training=True)\n",
        "\n",
        "      gen_loss = generator_loss(fake_output)\n",
        "      disc_loss = discriminator_loss(real_output, fake_output)\n",
        "\n",
        "    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
        "    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
        "\n",
        "    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
        "    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "X5bgFv7dJT9N",
        "colab": {}
      },
      "source": [
        "def train(dataset, epochs):\n",
        "  for epoch in range(epochs):\n",
        "    start = time.time()\n",
        "\n",
        "    for image_batch in dataset:\n",
        "      train_step(image_batch)\n",
        "\n",
        "    # Produce images for the GIF as we go\n",
        "    display.clear_output(wait=True)\n",
        "    generate_and_save_images(generator,\n",
        "                             epoch + 1,\n",
        "                             seed)\n",
        "\n",
        "    # Save the model every 15 epochs\n",
        "    if (epoch + 1) % 15 == 0:\n",
        "      checkpoint.save(file_prefix = checkpoint_prefix)\n",
        "\n",
        "    print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n",
        "\n",
        "  # Generate after the final epoch\n",
        "  display.clear_output(wait=True)\n",
        "  generate_and_save_images(generator,\n",
        "                           epochs,\n",
        "                           seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9y0WxdT5JT9Q"
      },
      "source": [
        "**Generate and save images**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hSm77QfuJT9R",
        "colab": {}
      },
      "source": [
        "def generate_and_save_images(model, epoch, test_input):\n",
        "  # Notice `training` is set to False.\n",
        "  # This is so all layers run in inference mode (batchnorm).\n",
        "  predictions = model(test_input, training=False)\n",
        "  print('prediction shape = ', predictions.shape)\n",
        "\n",
        "  fig = plt.figure(figsize=(4,4))\n",
        "\n",
        "  for i in range(predictions.shape[0]):\n",
        "      plt.subplot(4, 4, i+1)\n",
        "      #plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')\n",
        "      plt.imshow(predictions[i, :, :, 0])\n",
        "      plt.axis('off')\n",
        "\n",
        "  #plt.imshow(predictions[0, :, :, 0])\n",
        "  plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hVkUZNCAJT9S"
      },
      "source": [
        "## Train the model\n",
        "Call the `train()` method defined above to train the generator and discriminator simultaneously. Note, training GANs can be tricky. It's important that the generator and discriminator do not overpower each other (e.g., that they train at a similar rate).\n",
        "\n",
        "At the beginning of the training, the generated image tiles will look like random noise. As training progresses, the tiles will look increasingly real This is the expectation when enough training data i.e. images, is provided. Usually with only a few images as input, the discriminator doesn't get the oppooertunity to police the generator properly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nw4iLv21vSOi",
        "colab_type": "code",
        "outputId": "d8b0ddd4-8017-478b-d3ab-004a3af2194b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "%%time\n",
        "train(train_dataset, EPOCHS)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "prediction shape =  (16, 7, 7, 1)\n",
            "CPU times: user 2min 15s, sys: 18 s, total: 2min 33s\n",
            "Wall time: 7min 39s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959\nAM8L6I1fr1T4TaJ23CelEc7xP2aFmanD/es0heFFIz7X8OKp+j/j78di17dZ3+2LW8lth7vBzHzR\nxSIdgHdyIuFY5ETCsciJhGOREwnHIicSjkVOJJxHWmgdb+KWTn0/vFqoyxi8miqvNlY73phsh3Ms\nZT4wa4txf4+3tp/oz0kppcrH47+flnv4mNfeH6gdj4mshXPuBgTArH8Cfo2UUYEleB83Sz3+jkuj\n8XklnNa3UrsMxW23sgTcQrOkNsDMsBp8La7287t1Ab+6qCJEf503it+Bc2YXj4PZo5PxfnJK6V/N\nxTs5kXAsciLhWOREwrHIiYRjkRMJxyInEs7kdLq/eoeI/v/inZxIOBY5kXAsciLhWOREwrHIiYRj\nkRMJxyInEo5FTiQci5xIOI9sGpH66kb43+hMeO29Sn+oEGbnb8Zrx+31Fjgn+Ad8eZZ6/D/9zu1c\nZoJhJ6mv4OtsjcGbWYTn4o0zWsP0H/3Kwt1wzoqDj8MssBj/Hc/fuNTQdfb4w3p4nWFJeDMLuwN/\ndthG/SuVSyfijRu8m/DpWvBeEypvk7HrHD/yDXid5ePwBhjPPPYFzP74p8na8eZYXAjBV/Hp+t7D\n/25Pf7RcO5F3ciLhWOREwrHIiYRjkRMJxyInEs4jT9edZvwE0FKHnxx+d64XzA5PX68dT/bG2/Sm\nlS2BWfSJOpgZZS3H1/nQtO9h1mPUHZhldS3Rjg98bTGc4xzSDjOLi+6DUV5h+PjOz8JgFnQLv8H2\n9gD9P72gYvydhlzHbx2tHImfyht198UWmDny9N0ApZTKb4iDWVNf/Zt1nW24w6KcuCxrU3/8fZl3\nciLhWOREwrHIiYRjkRMJxyInEo5FTiScR1po3o24TdY8BL8h01TlC7PxR5/XjgddxK2T+E3ZMLu6\ncSjMjEp/5hLMzAovQChpDYdZVpU+q0/G5zEk5QbMyqJD8ESD/C74wawVd9BU7Wj85tiicfq3er5c\n0wfO2fP1CJiZW9zfWjw19DbMzgSEwuz6G2kwG7f6snb80r1ucE5dGX5LasRFvPAJ4Z2cSDgWOZFw\nLHIi4VjkRMKxyImEY5ETCeeRFlrGjDyY5f83bpHUZuJVQCa7/u9RfTpeIdW4ewDMvCoNbfvlUmVW\nIswuDMKr41wxt+pbQVENuEU0YnIRzA493hd/GO68GRZ4E7cKOx7ELbRNtT204ydeGw7neA12sffZ\nbfd/z5wC3KeM6V0DM1tOJMy+PpOuHS+etR3OGWN+FGbex7rCDOGdnEg4FjmRcCxyIuFY5ETCsciJ\nhPPI0/XcqgSYNWbgfb8igptgVntZv3DDHoaf4HY0esPM18Vec0Y1vdIIM3+Fs/hAvL9ckr9+/7cp\nwRfgnI9rM2BWsiEIZkZFndXvU6aUUjcX4d9zbZ+DMHs/Vf9GnMBeeJFI5Sj8FLspA5+jUdareD+8\nmnt40UjS3hx8UJN+IdTE9Clwyr1DMTCzPdeAPwvgnZxIOBY5kXAsciLhWOREwrHIiYRjkRMJ55EW\nWkfHP9aesn+O9z4LBd212t64TWaLxotXTA73L/3eKbxPV8QFvBdX7efFMOt9pVI7vmTzr+Cc4IlV\nMOvID4aZmo6jzsrH4n30uu3FLbS3D86GWe06/XjE93ghjl81vic1hbh/v7KW4cU2TXH4vNofGgyz\n6kn6f4OBYyvgnOa39O1FpZTy64K/b4R3ciLhWOREwrHIiYRjkRMJxyInEo5FTiScR1poIXvw/mZt\nQ3F7rSkOH9MW36Ydj4q4D+dUl7p4h88/gatX1gTcwOfldTwaZlU2fa8w7DJuB375wj6YrY3E+9wp\ntdRF9jfmVvybVWeYYebl4o0+thB9K+j2QHw8P9wpVMHncZtPzcdRZx3e+Dq9o/DrvWpc7OdnqtW3\n5cpfxHvZOb1wK6+x+sfvHcg7OZFwLHIi4VjkRMKxyImEY5ETCcciJxLO5HTi1TVE9K+Pd3Ii4Vjk\nRMKxyImEY5ETCcciJxKORU4kHIucSDgWOZFwLHIi4VjkRMKxyImEY5ETCcciJxKORU4kHIucSDgW\nOZFwLHIi4VjkRMKxyImEY5ETCcciJxKORU4kHIucSLj/BaeurPXswFUhAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959\nAM8L6I1fr1T4TaJ23CelEc7xP2aFmanD/es0heFFIz7X8OKp+j/j78di17dZ3+2LW8lth7vBzHzR\nxSIdgHdyIuFY5ETCsciJhGOREwnHIicSjkVOJJxHWmgdb+KWTn0/vFqoyxi8miqvNlY73phsh3Ms\nZT4wa4txf4+3tp/oz0kppcrH47+flnv4mNfeH6gdj4mshXPuBgTArH8Cfo2UUYEleB83Sz3+jkuj\n8XklnNa3UrsMxW23sgTcQrOkNsDMsBp8La7287t1Ab+6qCJEf503it+Bc2YXj4PZo5PxfnJK6V/N\nxTs5kXAsciLhWOREwrHIiYRjkRMJxyInEs7kdLq/eoeI/v/inZxIOBY5kXAsciLhWOREwrHIiYRj\nkRMJxyInEo5FTiQci5xIOI9sGpH66kb43+hMeO29Sn+oEGbnb8Zrx+31Fjgn+Ad8eZZ6/D/9zu1c\nZoJhJ6mv4OtsjcGbWYTn4o0zWsP0H/3Kwt1wzoqDj8MssBj/Hc/fuNTQdfb4w3p4nWFJeDMLuwN/\ndthG/SuVSyfijRu8m/DpWvBeEypvk7HrHD/yDXid5ePwBhjPPPYFzP74p8na8eZYXAjBV/Hp+t7D\n/25Pf7RcO5F3ciLhWOREwrHIiYRjkRMJxyInEs4jT9edZvwE0FKHnxx+d64XzA5PX68dT/bG2/Sm\nlS2BWfSJOpgZZS3H1/nQtO9h1mPUHZhldS3Rjg98bTGc4xzSDjOLi+6DUV5h+PjOz8JgFnQLv8H2\n9gD9P72gYvydhlzHbx2tHImfyht198UWmDny9N0ApZTKb4iDWVNf/Zt1nW24w6KcuCxrU3/8fZl3\nciLhWOREwrHIiYRjkRMJxyInEo5FTiScR1po3o24TdY8BL8h01TlC7PxR5/XjgddxK2T+E3ZMLu6\ncSjMjEp/5hLMzAovQChpDYdZVpU+q0/G5zEk5QbMyqJD8ESD/C74wawVd9BU7Wj85tiicfq3er5c\n0wfO2fP1CJiZW9zfWjw19DbMzgSEwuz6G2kwG7f6snb80r1ucE5dGX5LasRFvPAJ4Z2cSDgWOZFw\nLHIi4VjkRMKxyImEY5ETCeeRFlrGjDyY5f83bpHUZuJVQCa7/u9RfTpeIdW4ewDMvCoNbfvlUmVW\nIswuDMKr41wxt+pbQVENuEU0YnIRzA493hd/GO68GRZ4E7cKOx7ELbRNtT204ydeGw7neA12sffZ\nbfd/z5wC3KeM6V0DM1tOJMy+PpOuHS+etR3OGWN+FGbex7rCDOGdnEg4FjmRcCxyIuFY5ETCsciJ\nhPPI0/XcqgSYNWbgfb8igptgVntZv3DDHoaf4HY0esPM18Vec0Y1vdIIM3+Fs/hAvL9ckr9+/7cp\nwRfgnI9rM2BWsiEIZkZFndXvU6aUUjcX4d9zbZ+DMHs/Vf9GnMBeeJFI5Sj8FLspA5+jUdareD+8\nmnt40UjS3hx8UJN+IdTE9Clwyr1DMTCzPdeAPwvgnZxIOBY5kXAsciLhWOREwrHIiYRjkRMJ55EW\nWkfHP9aesn+O9z4LBd212t64TWaLxotXTA73L/3eKbxPV8QFvBdX7efFMOt9pVI7vmTzr+Cc4IlV\nMOvID4aZmo6jzsrH4n30uu3FLbS3D86GWe06/XjE93ghjl81vic1hbh/v7KW4cU2TXH4vNofGgyz\n6kn6f4OBYyvgnOa39O1FpZTy64K/b4R3ciLhWOREwrHIiYRjkRMJxyInEo5FTiScR1poIXvw/mZt\nQ3F7rSkOH9MW36Ydj4q4D+dUl7p4h88/gatX1gTcwOfldTwaZlU2fa8w7DJuB375wj6YrY3E+9wp\ntdRF9jfmVvybVWeYYebl4o0+thB9K+j2QHw8P9wpVMHncZtPzcdRZx3e+Dq9o/DrvWpc7OdnqtW3\n5cpfxHvZOb1wK6+x+sfvHcg7OZFwLHIi4VjkRMKxyImEY5ETCcciJxLO5HTi1TVE9K+Pd3Ii4Vjk\nRMKxyImEY5ETCcciJxKORU4kHIucSDgWOZFwLHIi4VjkRMKxyImEY5ETCcciJxKORU4kHIucSDgW\nOZFwLHIi4VjkRMKxyImEY5ETCcciJxKORU4kHIucSLj/BaeurPXswFUhAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959\nAM8L6I1fr1T4TaJ23CelEc7xP2aFmanD/es0heFFIz7X8OKp+j/j78di17dZ3+2LW8lth7vBzHzR\nxSIdgHdyIuFY5ETCsciJhGOREwnHIicSjkVOJJxHWmgdb+KWTn0/vFqoyxi8miqvNlY73phsh3Ms\nZT4wa4txf4+3tp/oz0kppcrH47+flnv4mNfeH6gdj4mshXPuBgTArH8Cfo2UUYEleB83Sz3+jkuj\n8XklnNa3UrsMxW23sgTcQrOkNsDMsBp8La7287t1Ab+6qCJEf503it+Bc2YXj4PZo5PxfnJK6V/N\nxTs5kXAsciLhWOREwrHIiYRjkRMJxyInEs7kdLq/eoeI/v/inZxIOBY5kXAsciLhWOREwrHIiYRj\nkRMJxyInEo5FTiQci5xIOI9sGpH66kb43+hMeO29Sn+oEGbnb8Zrx+31Fjgn+Ad8eZZ6/D/9zu1c\nZoJhJ6mv4OtsjcGbWYTn4o0zWsP0H/3Kwt1wzoqDj8MssBj/Hc/fuNTQdfb4w3p4nWFJeDMLuwN/\ndthG/SuVSyfijRu8m/DpWvBeEypvk7HrHD/yDXid5ePwBhjPPPYFzP74p8na8eZYXAjBV/Hp+t7D\n/25Pf7RcO5F3ciLhWOREwrHIiYRjkRMJxyInEs4jT9edZvwE0FKHnxx+d64XzA5PX68dT/bG2/Sm\nlS2BWfSJOpgZZS3H1/nQtO9h1mPUHZhldS3Rjg98bTGc4xzSDjOLi+6DUV5h+PjOz8JgFnQLv8H2\n9gD9P72gYvydhlzHbx2tHImfyht198UWmDny9N0ApZTKb4iDWVNf/Zt1nW24w6KcuCxrU3/8fZl3\nciLhWOREwrHIiYRjkRMJxyInEo5FTiScR1po3o24TdY8BL8h01TlC7PxR5/XjgddxK2T+E3ZMLu6\ncSjMjEp/5hLMzAovQChpDYdZVpU+q0/G5zEk5QbMyqJD8ESD/C74wawVd9BU7Wj85tiicfq3er5c\n0wfO2fP1CJiZW9zfWjw19DbMzgSEwuz6G2kwG7f6snb80r1ucE5dGX5LasRFvPAJ4Z2cSDgWOZFw\nLHIi4VjkRMKxyImEY5ETCeeRFlrGjDyY5f83bpHUZuJVQCa7/u9RfTpeIdW4ewDMvCoNbfvlUmVW\nIswuDMKr41wxt+pbQVENuEU0YnIRzA493hd/GO68GRZ4E7cKOx7ELbRNtT204ydeGw7neA12sffZ\nbfd/z5wC3KeM6V0DM1tOJMy+PpOuHS+etR3OGWN+FGbex7rCDOGdnEg4FjmRcCxyIuFY5ETCsciJ\nhPPI0/XcqgSYNWbgfb8igptgVntZv3DDHoaf4HY0esPM18Vec0Y1vdIIM3+Fs/hAvL9ckr9+/7cp\nwRfgnI9rM2BWsiEIZkZFndXvU6aUUjcX4d9zbZ+DMHs/Vf9GnMBeeJFI5Sj8FLspA5+jUdareD+8\nmnt40UjS3hx8UJN+IdTE9Clwyr1DMTCzPdeAPwvgnZxIOBY5kXAsciLhWOREwrHIiYRjkRMJ55EW\nWkfHP9aesn+O9z4LBd212t64TWaLxotXTA73L/3eKbxPV8QFvBdX7efFMOt9pVI7vmTzr+Cc4IlV\nMOvID4aZmo6jzsrH4n30uu3FLbS3D86GWe06/XjE93ghjl81vic1hbh/v7KW4cU2TXH4vNofGgyz\n6kn6f4OBYyvgnOa39O1FpZTy64K/b4R3ciLhWOREwrHIiYRjkRMJxyInEo5FTiScR1poIXvw/mZt\nQ3F7rSkOH9MW36Ydj4q4D+dUl7p4h88/gatX1gTcwOfldTwaZlU2fa8w7DJuB375wj6YrY3E+9wp\ntdRF9jfmVvybVWeYYebl4o0+thB9K+j2QHw8P9wpVMHncZtPzcdRZx3e+Dq9o/DrvWpc7OdnqtW3\n5cpfxHvZOb1wK6+x+sfvHcg7OZFwLHIi4VjkRMKxyImEY5ETCcciJxLO5HTi1TVE9K+Pd3Ii4Vjk\nRMKxyImEY5ETCcciJxKORU4kHIucSDgWOZFwLHIi4VjkRMKxyImEY5ETCcciJxKORU4kHIucSDgW\nOZFwLHIi4VjkRMKxyImEY5ETCcciJxKORU4kHIucSLj/BaeurPXswFUhAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959\nAM8L6I1fr1T4TaJ23CelEc7xP2aFmanD/es0heFFIz7X8OKp+j/j78di17dZ3+2LW8lth7vBzHzR\nxSIdgHdyIuFY5ETCsciJhGOREwnHIicSjkVOJJxHWmgdb+KWTn0/vFqoyxi8miqvNlY73phsh3Ms\nZT4wa4txf4+3tp/oz0kppcrH47+flnv4mNfeH6gdj4mshXPuBgTArH8Cfo2UUYEleB83Sz3+jkuj\n8XklnNa3UrsMxW23sgTcQrOkNsDMsBp8La7287t1Ab+6qCJEf503it+Bc2YXj4PZo5PxfnJK6V/N\nxTs5kXAsciLhWOREwrHIiYRjkRMJxyInEs7kdLq/eoeI/v/inZxIOBY5kXAsciLhWOREwrHIiYRj\nkRMJxyInEo5FTiQci5xIOI9sGpH66kb43+hMeO29Sn+oEGbnb8Zrx+31Fjgn+Ad8eZZ6/D/9zu1c\nZoJhJ6mv4OtsjcGbWYTn4o0zWsP0H/3Kwt1wzoqDj8MssBj/Hc/fuNTQdfb4w3p4nWFJeDMLuwN/\ndthG/SuVSyfijRu8m/DpWvBeEypvk7HrHD/yDXid5ePwBhjPPPYFzP74p8na8eZYXAjBV/Hp+t7D\n/25Pf7RcO5F3ciLhWOREwrHIiYRjkRMJxyInEs4jT9edZvwE0FKHnxx+d64XzA5PX68dT/bG2/Sm\nlS2BWfSJOpgZZS3H1/nQtO9h1mPUHZhldS3Rjg98bTGc4xzSDjOLi+6DUV5h+PjOz8JgFnQLv8H2\n9gD9P72gYvydhlzHbx2tHImfyht198UWmDny9N0ApZTKb4iDWVNf/Zt1nW24w6KcuCxrU3/8fZl3\nciLhWOREwrHIiYRjkRMJxyInEo5FTiScR1po3o24TdY8BL8h01TlC7PxR5/XjgddxK2T+E3ZMLu6\ncSjMjEp/5hLMzAovQChpDYdZVpU+q0/G5zEk5QbMyqJD8ESD/C74wawVd9BU7Wj85tiicfq3er5c\n0wfO2fP1CJiZW9zfWjw19DbMzgSEwuz6G2kwG7f6snb80r1ucE5dGX5LasRFvPAJ4Z2cSDgWOZFw\nLHIi4VjkRMKxyImEY5ETCeeRFlrGjDyY5f83bpHUZuJVQCa7/u9RfTpeIdW4ewDMvCoNbfvlUmVW\nIswuDMKr41wxt+pbQVENuEU0YnIRzA493hd/GO68GRZ4E7cKOx7ELbRNtT204ydeGw7neA12sffZ\nbfd/z5wC3KeM6V0DM1tOJMy+PpOuHS+etR3OGWN+FGbex7rCDOGdnEg4FjmRcCxyIuFY5ETCsciJ\nhPPI0/XcqgSYNWbgfb8igptgVntZv3DDHoaf4HY0esPM18Vec0Y1vdIIM3+Fs/hAvL9ckr9+/7cp\nwRfgnI9rM2BWsiEIZkZFndXvU6aUUjcX4d9zbZ+DMHs/Vf9GnMBeeJFI5Sj8FLspA5+jUdareD+8\nmnt40UjS3hx8UJN+IdTE9Clwyr1DMTCzPdeAPwvgnZxIOBY5kXAsciLhWOREwrHIiYRjkRMJ55EW\nWkfHP9aesn+O9z4LBd212t64TWaLxotXTA73L/3eKbxPV8QFvBdX7efFMOt9pVI7vmTzr+Cc4IlV\nMOvID4aZmo6jzsrH4n30uu3FLbS3D86GWe06/XjE93ghjl81vic1hbh/v7KW4cU2TXH4vNofGgyz\n6kn6f4OBYyvgnOa39O1FpZTy64K/b4R3ciLhWOREwrHIiYRjkRMJxyInEo5FTiScR1poIXvw/mZt\nQ3F7rSkOH9MW36Ydj4q4D+dUl7p4h88/gatX1gTcwOfldTwaZlU2fa8w7DJuB375wj6YrY3E+9wp\ntdRF9jfmVvybVWeYYebl4o0+thB9K+j2QHw8P9wpVMHncZtPzcdRZx3e+Dq9o/DrvWpc7OdnqtW3\n5cpfxHvZOb1wK6+x+sfvHcg7OZFwLHIi4VjkRMKxyImEY5ETCcciJxLO5HTi1TVE9K+Pd3Ii4Vjk\nRMKxyImEY5ETCcciJxKORU4kHIucSDgWOZFwLHIi4VjkRMKxyImEY5ETCcciJxKORU4kHIucSDgW\nOZFwLHIi4VjkRMKxyImEY5ETCcciJxKORU4kHIucSLj/BaeurPXswFUhAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959\nAM8L6I1fr1T4TaJ23CelEc7xP2aFmanD/es0heFFIz7X8OKp+j/j78di17dZ3+2LW8lth7vBzHzR\nxSIdgHdyIuFY5ETCsciJhGOREwnHIicSjkVOJJxHWmgdb+KWTn0/vFqoyxi8miqvNlY73phsh3Ms\nZT4wa4txf4+3tp/oz0kppcrH47+flnv4mNfeH6gdj4mshXPuBgTArH8Cfo2UUYEleB83Sz3+jkuj\n8XklnNa3UrsMxW23sgTcQrOkNsDMsBp8La7287t1Ab+6qCJEf503it+Bc2YXj4PZo5PxfnJK6V/N\nxTs5kXAsciLhWOREwrHIiYRjkRMJxyInEs7kdLq/eoeI/v/inZxIOBY5kXAsciLhWOREwrHIiYRj\nkRMJxyInEo5FTiQci5xIOI9sGpH66kb43+hMeO29Sn+oEGbnb8Zrx+31Fjgn+Ad8eZZ6/D/9zu1c\nZoJhJ6mv4OtsjcGbWYTn4o0zWsP0H/3Kwt1wzoqDj8MssBj/Hc/fuNTQdfb4w3p4nWFJeDMLuwN/\ndthG/SuVSyfijRu8m/DpWvBeEypvk7HrHD/yDXid5ePwBhjPPPYFzP74p8na8eZYXAjBV/Hp+t7D\n/25Pf7RcO5F3ciLhWOREwrHIiYRjkRMJxyInEs4jT9edZvwE0FKHnxx+d64XzA5PX68dT/bG2/Sm\nlS2BWfSJOpgZZS3H1/nQtO9h1mPUHZhldS3Rjg98bTGc4xzSDjOLi+6DUV5h+PjOz8JgFnQLv8H2\n9gD9P72gYvydhlzHbx2tHImfyht198UWmDny9N0ApZTKb4iDWVNf/Zt1nW24w6KcuCxrU3/8fZl3\nciLhWOREwrHIiYRjkRMJxyInEo5FTiScR1po3o24TdY8BL8h01TlC7PxR5/XjgddxK2T+E3ZMLu6\ncSjMjEp/5hLMzAovQChpDYdZVpU+q0/G5zEk5QbMyqJD8ESD/C74wawVd9BU7Wj85tiicfq3er5c\n0wfO2fP1CJiZW9zfWjw19DbMzgSEwuz6G2kwG7f6snb80r1ucE5dGX5LasRFvPAJ4Z2cSDgWOZFw\nLHIi4VjkRMKxyImEY5ETCeeRFlrGjDyY5f83bpHUZuJVQCa7/u9RfTpeIdW4ewDMvCoNbfvlUmVW\nIswuDMKr41wxt+pbQVENuEU0YnIRzA493hd/GO68GRZ4E7cKOx7ELbRNtT204ydeGw7neA12sffZ\nbfd/z5wC3KeM6V0DM1tOJMy+PpOuHS+etR3OGWN+FGbex7rCDOGdnEg4FjmRcCxyIuFY5ETCsciJ\nhPPI0/XcqgSYNWbgfb8igptgVntZv3DDHoaf4HY0esPM18Vec0Y1vdIIM3+Fs/hAvL9ckr9+/7cp\nwRfgnI9rM2BWsiEIZkZFndXvU6aUUjcX4d9zbZ+DMHs/Vf9GnMBeeJFI5Sj8FLspA5+jUdareD+8\nmnt40UjS3hx8UJN+IdTE9Clwyr1DMTCzPdeAPwvgnZxIOBY5kXAsciLhWOREwrHIiYRjkRMJ55EW\nWkfHP9aesn+O9z4LBd212t64TWaLxotXTA73L/3eKbxPV8QFvBdX7efFMOt9pVI7vmTzr+Cc4IlV\nMOvID4aZmo6jzsrH4n30uu3FLbS3D86GWe06/XjE93ghjl81vic1hbh/v7KW4cU2TXH4vNofGgyz\n6kn6f4OBYyvgnOa39O1FpZTy64K/b4R3ciLhWOREwrHIiYRjkRMJxyInEo5FTiScR1poIXvw/mZt\nQ3F7rSkOH9MW36Ydj4q4D+dUl7p4h88/gatX1gTcwOfldTwaZlU2fa8w7DJuB375wj6YrY3E+9wp\ntdRF9jfmVvybVWeYYebl4o0+thB9K+j2QHw8P9wpVMHncZtPzcdRZx3e+Dq9o/DrvWpc7OdnqtW3\n5cpfxHvZOb1wK6+x+sfvHcg7OZFwLHIi4VjkRMKxyImEY5ETCcciJxLO5HTi1TVE9K+Pd3Ii4Vjk\nRMKxyImEY5ETCcciJxKORU4kHIucSDgWOZFwLHIi4VjkRMKxyImEY5ETCcciJxKORU4kHIucSDgW\nOZFwLHIi4VjkRMKxyImEY5ETCcciJxKORU4kHIucSLj/BaeurPXswFUhAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DuPWjcJfi73S5qW93KaVU7IfdYbbuGt777KO1+kUdzl14QU3jiUiYOdLc3+Mt+GP8e959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\n",
            "text/plain": [
              "<Figure size 288x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2u5VLnPxJT9U"
      },
      "source": [
        "Restore the latest checkpoint."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oDx32LnJJT9U",
        "colab": {}
      },
      "source": [
        "checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "FyfsAYlPJT9W"
      },
      "source": [
        "## Create a GIF\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LYEQitvPJT9W",
        "colab": {}
      },
      "source": [
        "# Display a single image using the epoch number\n",
        "def display_image(epoch_no):\n",
        "  return PIL.Image.open('image_at_epoch_{:04d}.png'.format(epoch_no))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vDRN_Y5DJT9Z",
        "colab": {}
      },
      "source": [
        "display_image(EPOCHS)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "NywiH3nL8guF"
      },
      "source": [
        "Use `imageio` to create an animated gif using the images saved during training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IGKQgENQ8lEI",
        "colab": {}
      },
      "source": [
        "anim_file = 'dcgan.gif'\n",
        "\n",
        "with imageio.get_writer(anim_file, mode='I') as writer:\n",
        "  filenames = glob.glob('image*.png')\n",
        "  filenames = sorted(filenames)\n",
        "  last = -1\n",
        "  for i,filename in enumerate(filenames):\n",
        "    frame = 2*(i**0.5)\n",
        "    if round(frame) > round(last):\n",
        "      last = frame\n",
        "    else:\n",
        "      continue\n",
        "    image = imageio.imread(filename)\n",
        "    writer.append_data(image)\n",
        "  image = imageio.imread(filename)\n",
        "  writer.append_data(image)\n",
        "\n",
        "import IPython\n",
        "if IPython.version_info > (6,2,0,''):\n",
        "  display.Image(filename=anim_file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cGhC3-fMWSwl"
      },
      "source": [
        "If you're working in Colab you can download the animation with the code below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uV0yiKpzNP1b",
        "colab": {}
      },
      "source": [
        "try:\n",
        "  from google.colab import files\n",
        "except ImportError:\n",
        "   pass\n",
        "else:\n",
        "  files.download(anim_file)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "29pmCDa9QmMa",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDKVNFuF92YZ",
        "colab_type": "code",
        "outputId": "7568ea2a-ec4c-49fd-9d9f-9d8a3f8343ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "import keras\n",
        "from keras.layers import Activation, Dense, Input\n",
        "from keras.layers import Conv2D, Flatten\n",
        "from keras.layers import Reshape, Conv2DTranspose\n",
        "from keras.models import Model\n",
        "from keras import backend as K\n",
        "from keras.datasets import mnist\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "\n",
        "np.random.seed(1337)\n",
        "\n",
        "# MNIST dataset\n",
        "(x_train, _), (x_test, _) = mnist.load_data()\n",
        "\n",
        "image_size = x_train.shape[1]\n",
        "x_train = np.reshape(x_train, [-1, image_size, image_size, 1])\n",
        "x_test = np.reshape(x_test, [-1, image_size, image_size, 1])\n",
        "x_train = x_train.astype('float32') / 255\n",
        "x_test = x_test.astype('float32') / 255\n",
        "\n",
        "# Generate corrupted MNIST images by adding noise with normal dist\n",
        "# centered at 0.5 and std=0.5\n",
        "noise = np.random.normal(loc=0.5, scale=0.5, size=x_train.shape)\n",
        "x_train_noisy = x_train + noise\n",
        "noise = np.random.normal(loc=0.5, scale=0.5, size=x_test.shape)\n",
        "x_test_noisy = x_test + noise\n",
        "\n",
        "x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n",
        "x_test_noisy = np.clip(x_test_noisy, 0., 1.)\n",
        "\n",
        "# Network parameters\n",
        "input_shape = (image_size, image_size, 1)\n",
        "batch_size = 128\n",
        "kernel_size = 3\n",
        "latent_dim = 16\n",
        "# Encoder/Decoder number of CNN layers and filters per layer\n",
        "layer_filters = [32, 64]\n",
        "\n",
        "# Build the Autoencoder Model\n",
        "# First build the Encoder Model\n",
        "inputs = Input(shape=input_shape, name='encoder_input')\n",
        "x = inputs\n",
        "# Stack of Conv2D blocks\n",
        "# Notes:\n",
        "# 1) Use Batch Normalization before ReLU on deep networks\n",
        "# 2) Use MaxPooling2D as alternative to strides>1\n",
        "# - faster but not as good as strides>1\n",
        "for filters in layer_filters:\n",
        "    x = Conv2D(filters=filters,\n",
        "               kernel_size=kernel_size,\n",
        "               strides=2,\n",
        "               activation='relu',\n",
        "               padding='same')(x)\n",
        "\n",
        "# Shape info needed to build Decoder Model\n",
        "shape = K.int_shape(x)\n",
        "\n",
        "# Generate the latent vector\n",
        "x = Flatten()(x)\n",
        "latent = Dense(latent_dim, name='latent_vector')(x)\n",
        "\n",
        "# Instantiate Encoder Model\n",
        "encoder = Model(inputs, latent, name='encoder')\n",
        "encoder.summary()\n",
        "\n",
        "# Build the Decoder Model\n",
        "latent_inputs = Input(shape=(latent_dim,), name='decoder_input')\n",
        "x = Dense(shape[1] * shape[2] * shape[3])(latent_inputs)\n",
        "x = Reshape((shape[1], shape[2], shape[3]))(x)\n",
        "\n",
        "# Stack of Transposed Conv2D blocks\n",
        "# Notes:\n",
        "# 1) Use Batch Normalization before ReLU on deep networks\n",
        "# 2) Use UpSampling2D as alternative to strides>1\n",
        "# - faster but not as good as strides>1\n",
        "for filters in layer_filters[::-1]:\n",
        "    x = Conv2DTranspose(filters=filters,\n",
        "                        kernel_size=kernel_size,\n",
        "                        strides=2,\n",
        "                        activation='relu',\n",
        "                        padding='same')(x)\n",
        "\n",
        "x = Conv2DTranspose(filters=1,\n",
        "                    kernel_size=kernel_size,\n",
        "                    padding='same')(x)\n",
        "\n",
        "outputs = Activation('sigmoid', name='decoder_output')(x)\n",
        "\n",
        "# Instantiate Decoder Model\n",
        "decoder = Model(latent_inputs, outputs, name='decoder')\n",
        "decoder.summary()\n",
        "\n",
        "# Autoencoder = Encoder + Decoder\n",
        "# Instantiate Autoencoder Model\n",
        "autoencoder = Model(inputs, decoder(encoder(inputs)), name='autoencoder')\n",
        "autoencoder.summary()\n",
        "\n",
        "autoencoder.compile(loss='mse', optimizer='adam')\n",
        "\n",
        "# Train the autoencoder\n",
        "autoencoder.fit(x_train_noisy,\n",
        "                x_train,\n",
        "                validation_data=(x_test_noisy, x_test),\n",
        "                epochs=30,\n",
        "                batch_size=batch_size)\n",
        "\n",
        "# Predict the Autoencoder output from corrupted test images\n",
        "x_decoded = autoencoder.predict(x_test_noisy)\n",
        "\n",
        "# Display the 1st 8 corrupted and denoised images\n",
        "rows, cols = 10, 30\n",
        "num = rows * cols\n",
        "imgs = np.concatenate([x_test[:num], x_test_noisy[:num], x_decoded[:num]])\n",
        "imgs = imgs.reshape((rows * 3, cols, image_size, image_size))\n",
        "imgs = np.vstack(np.split(imgs, rows, axis=1))\n",
        "imgs = imgs.reshape((rows * 3, -1, image_size, image_size))\n",
        "imgs = np.vstack([np.hstack(i) for i in imgs])\n",
        "imgs = (imgs * 255).astype(np.uint8)\n",
        "plt.figure()\n",
        "plt.axis('off')\n",
        "plt.title('Original images: top rows, '\n",
        "          'Corrupted Input: middle rows, '\n",
        "          'Denoised Input:  third rows')\n",
        "plt.imshow(imgs, interpolation='none', cmap='gray')\n",
        "Image.fromarray(imgs).save('corrupted_and_denoised.png')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"encoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "encoder_input (InputLayer)   (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 14, 14, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 7, 7, 64)          18496     \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 3136)              0         \n",
            "_________________________________________________________________\n",
            "latent_vector (Dense)        (None, 16)                50192     \n",
            "=================================================================\n",
            "Total params: 69,008\n",
            "Trainable params: 69,008\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"decoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "decoder_input (InputLayer)   (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 3136)              53312     \n",
            "_________________________________________________________________\n",
            "reshape_4 (Reshape)          (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_10 (Conv2DT (None, 14, 14, 64)        36928     \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_11 (Conv2DT (None, 28, 28, 32)        18464     \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_12 (Conv2DT (None, 28, 28, 1)         289       \n",
            "_________________________________________________________________\n",
            "decoder_output (Activation)  (None, 28, 28, 1)         0         \n",
            "=================================================================\n",
            "Total params: 108,993\n",
            "Trainable params: 108,993\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"autoencoder\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "encoder_input (InputLayer)   (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "encoder (Model)              (None, 16)                69008     \n",
            "_________________________________________________________________\n",
            "decoder (Model)              (None, 28, 28, 1)         108993    \n",
            "=================================================================\n",
            "Total params: 178,001\n",
            "Trainable params: 178,001\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/30\n",
            "60000/60000 [==============================] - 100s 2ms/step - loss: 0.0614 - val_loss: 0.0353\n",
            "Epoch 2/30\n",
            "60000/60000 [==============================] - 100s 2ms/step - loss: 0.0277 - val_loss: 0.0233\n",
            "Epoch 3/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0220 - val_loss: 0.0204\n",
            "Epoch 4/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0199 - val_loss: 0.0189\n",
            "Epoch 5/30\n",
            "60000/60000 [==============================] - 100s 2ms/step - loss: 0.0188 - val_loss: 0.0182\n",
            "Epoch 6/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0180 - val_loss: 0.0177\n",
            "Epoch 7/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0175 - val_loss: 0.0173\n",
            "Epoch 8/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0171 - val_loss: 0.0170\n",
            "Epoch 9/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0168 - val_loss: 0.0168\n",
            "Epoch 10/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0165 - val_loss: 0.0167\n",
            "Epoch 11/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0162 - val_loss: 0.0166\n",
            "Epoch 12/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0160 - val_loss: 0.0164\n",
            "Epoch 13/30\n",
            "60000/60000 [==============================] - 98s 2ms/step - loss: 0.0158 - val_loss: 0.0163\n",
            "Epoch 14/30\n",
            "60000/60000 [==============================] - 98s 2ms/step - loss: 0.0157 - val_loss: 0.0162\n",
            "Epoch 15/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0155 - val_loss: 0.0161\n",
            "Epoch 16/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0154 - val_loss: 0.0160\n",
            "Epoch 17/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0153 - val_loss: 0.0161\n",
            "Epoch 18/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0152 - val_loss: 0.0161\n",
            "Epoch 19/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0151 - val_loss: 0.0161\n",
            "Epoch 20/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0150 - val_loss: 0.0159\n",
            "Epoch 21/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0149 - val_loss: 0.0159\n",
            "Epoch 22/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0149 - val_loss: 0.0159\n",
            "Epoch 23/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0148 - val_loss: 0.0158\n",
            "Epoch 24/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0147 - val_loss: 0.0158\n",
            "Epoch 25/30\n",
            "60000/60000 [==============================] - 99s 2ms/step - loss: 0.0147 - val_loss: 0.0158\n",
            "Epoch 26/30\n",
            "60000/60000 [==============================] - 102s 2ms/step - loss: 0.0146 - val_loss: 0.0158\n",
            "Epoch 27/30\n",
            "60000/60000 [==============================] - 101s 2ms/step - loss: 0.0145 - val_loss: 0.0157\n",
            "Epoch 28/30\n",
            "60000/60000 [==============================] - 100s 2ms/step - loss: 0.0145 - val_loss: 0.0157\n",
            "Epoch 29/30\n",
            "60000/60000 [==============================] - 100s 2ms/step - loss: 0.0144 - val_loss: 0.0157\n",
            "Epoch 30/30\n",
            "60000/60000 [==============================] - 101s 2ms/step - loss: 0.0144 - val_loss: 0.0156\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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joHSA41AG71ZU1krfCHU9bCSUAfwulEb4AEqDfFuMhSKwc6EMyG8j9P4W/F8AM4QQuVD2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OnTPOogAL6lkpubizVr1qC8vJyvjokIjx8/xtOnT1FaWqqmUBQUFCAgIAC3bt1CdHQ03+7QpE6d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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8oIhN07vn9T0",
        "colab_type": "text"
      },
      "source": [
        "All the other demos are examples of Supervised Learning, so in this demo is an example of Unsupervised Learning we train an autoencoder on MNIST digits.\n",
        "\n",
        "An autoencoder is a regression task where the network is asked to predict its input (in other words, model the identity function). Sounds simple enough, except the network has a tight bottleneck of a few neurons in the middle (in the default example only two!), forcing it to create effective representations that compress the input into a low-dimensional code that can be used by the decoder to reproduce the original input"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K89h5obEx_17",
        "colab_type": "text"
      },
      "source": [
        "https://cs.stanford.edu/people/karpathy/convnetjs/demo/autoencoder.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1gSA0xeNlY_E",
        "colab_type": "text"
      },
      "source": [
        "CITATIONS:\n",
        "\n",
        "\n",
        "\n",
        "https://github.com/pilillo/img-notebooks/blob/master/Denoising_autoencoders.ipynb\n",
        "\n",
        "\n",
        "https://paperswithcode.com/paper/high-throughput-onboard-hyperspectral-image\n",
        "\n",
        "https://arxiv.org/abs/1607.07539\n",
        "\n",
        "http://bamos.github.io/2016/08/09/deep-completion/#ml-heavy-tensorflow-implementation-of-image-completion-with-dcgans\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-zKQMsHJUevQ",
        "colab_type": "text"
      },
      "source": [
        "# **Other similar algorithms as solutions to the missing data challenge**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CEfPt2M0yj03",
        "colab_type": "text"
      },
      "source": [
        "# **RDCGAN: Unsupervised Representation Learning With Regularized Deep Convolutional Generative Adversarial Networks**\n",
        "\n",
        "https://colab.research.google.com/drive/1xMdzSoZJXEvIKRxuzSCvDMjAkrweGPky\n",
        "\n",
        "http://bamos.github.io/2016/08/09/deep-completion/#ml-heavy-tensorflow-implementation-of-image-completion-with-dcgans\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BaIGi61RV5zy",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbDEGuc0UtmG",
        "colab_type": "text"
      },
      "source": [
        "# **ISR: Image Super Resolution**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIGJaz6wVr0s",
        "colab_type": "text"
      },
      "source": [
        "# **# Demo of training**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m12GgACxVzVz",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KCd2ZuS4V6Z0",
        "outputId": "dfbdd325-d709-427f-b34b-6fe2aa77c178",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!pip install ISR"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting ISR\n",
            "  Downloading https://files.pythonhosted.org/packages/d6/c3/7e6d659870e9a2a5f5a529ecc3f71f6387ee41aa5c486fc9f023f6cdea1f/ISR-2.1-py3-none-any.whl\n",
            "Collecting numpy==1.16.2 (from ISR)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/35/d5/4f8410ac303e690144f0a0603c4b8fd3b986feb2749c435f7cdbb288f17e/numpy-1.16.2-cp36-cp36m-manylinux1_x86_64.whl (17.3MB)\n",
            "\u001b[K     |████████████████████████████████| 17.3MB 19.2MB/s \n",
            "\u001b[?25hCollecting Keras==2.2.4 (from ISR)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5e/10/aa32dad071ce52b5502266b5c659451cfd6ffcbf14e6c8c4f16c0ff5aaab/Keras-2.2.4-py2.py3-none-any.whl (312kB)\n",
            "\u001b[K     |████████████████████████████████| 317kB 43.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from ISR) (4.28.1)\n",
            "Requirement already satisfied: imageio in /usr/local/lib/python3.6/dist-packages (from ISR) (2.4.1)\n",
            "Collecting tensorflow==1.13.1 (from ISR)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/77/63/a9fa76de8dffe7455304c4ed635be4aa9c0bacef6e0633d87d5f54530c5c/tensorflow-1.13.1-cp36-cp36m-manylinux1_x86_64.whl (92.5MB)\n",
            "\u001b[K     |████████████████████████████████| 92.5MB 272kB/s \n",
            "\u001b[?25hRequirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from Keras==2.2.4->ISR) (1.1.0)\n",
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            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ISR) (0.33.6)\n",
            "Collecting tensorflow-estimator<1.14.0rc0,>=1.13.0 (from tensorflow==1.13.1->ISR)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bb/48/13f49fc3fa0fdf916aa1419013bb8f2ad09674c275b4046d5ee669a46873/tensorflow_estimator-1.13.0-py2.py3-none-any.whl (367kB)\n",
            "\u001b[K     |████████████████████████████████| 368kB 33.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ISR) (1.1.0)\n",
            "Collecting tensorboard<1.14.0,>=1.13.0 (from tensorflow==1.13.1->ISR)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0f/39/bdd75b08a6fba41f098b6cb091b9e8c7a80e1b4d679a581a0ccd17b10373/tensorboard-1.13.1-py3-none-any.whl (3.2MB)\n",
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            "\u001b[?25hRequirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.13.1->ISR) (0.8.1)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->imageio->ISR) (0.46)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow==1.13.1->ISR) (41.2.0)\n",
            "Collecting mock>=2.0.0 (from tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow==1.13.1->ISR)\n",
            "  Downloading https://files.pythonhosted.org/packages/05/d2/f94e68be6b17f46d2c353564da56e6fb89ef09faeeff3313a046cb810ca9/mock-3.0.5-py2.py3-none-any.whl\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.1->ISR) (3.1.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.1->ISR) (0.16.0)\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: numpy, Keras, mock, tensorflow-estimator, tensorboard, tensorflow, ISR\n",
            "  Found existing installation: numpy 1.16.5\n",
            "    Uninstalling numpy-1.16.5:\n",
            "      Successfully uninstalled numpy-1.16.5\n",
            "  Found existing installation: Keras 2.2.5\n",
            "    Uninstalling Keras-2.2.5:\n",
            "      Successfully uninstalled Keras-2.2.5\n",
            "  Found existing installation: tensorflow-estimator 1.15.1\n",
            "    Uninstalling tensorflow-estimator-1.15.1:\n",
            "      Successfully uninstalled tensorflow-estimator-1.15.1\n",
            "  Found existing installation: tensorboard 1.15.0\n",
            "    Uninstalling tensorboard-1.15.0:\n",
            "      Successfully uninstalled tensorboard-1.15.0\n",
            "  Found existing installation: tensorflow 1.15.0rc3\n",
            "    Uninstalling tensorflow-1.15.0rc3:\n",
            "      Successfully uninstalled tensorflow-1.15.0rc3\n",
            "Successfully installed ISR-2.1 Keras-2.2.4 mock-3.0.5 numpy-1.16.2 tensorboard-1.13.1 tensorflow-1.13.1 tensorflow-estimator-1.13.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "R_BXVVxnN1sx"
      },
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "aJCKjYk-MA-p"
      },
      "source": [
        "\n",
        "## Get the training data\n",
        "Get your data to train the model. The div2k dataset linked here is for a scaling factor of 2. Beware of this later when training the model.\n",
        "\n",
        "(for more options on how to get you data on Colab notebooks visit https://colab.research.google.com/notebooks/io.ipynb)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ytGnfdDo77l-",
        "outputId": "3ff87698-c152-4b48-d6ba-367755842a32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!wget http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_LR_bicubic_X2.zip\n",
        "!wget http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_bicubic_X2.zip\n",
        "!wget http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip\n",
        "!wget http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_HR.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-21 01:15:59--  http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_LR_bicubic_X2.zip\n",
            "Resolving data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)... 129.132.52.162\n",
            "Connecting to data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)|129.132.52.162|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_LR_bicubic_X2.zip [following]\n",
            "--2019-10-21 01:16:00--  https://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_LR_bicubic_X2.zip\n",
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            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 925390592 (883M) [application/zip]\n",
            "Saving to: ‘DIV2K_train_LR_bicubic_X2.zip’\n",
            "\n",
            "DIV2K_train_LR_bicu 100%[===================>] 882.52M  23.6MB/s    in 39s     \n",
            "\n",
            "2019-10-21 01:16:39 (22.9 MB/s) - ‘DIV2K_train_LR_bicubic_X2.zip’ saved [925390592/925390592]\n",
            "\n",
            "--2019-10-21 01:16:39--  http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_bicubic_X2.zip\n",
            "Resolving data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)... 129.132.52.162\n",
            "Connecting to data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)|129.132.52.162|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
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            "--2019-10-21 01:16:40--  https://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_LR_bicubic_X2.zip\n",
            "Connecting to data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)|129.132.52.162|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 117763600 (112M) [application/zip]\n",
            "Saving to: ‘DIV2K_valid_LR_bicubic_X2.zip’\n",
            "\n",
            "DIV2K_valid_LR_bicu 100%[===================>] 112.31M  23.2MB/s    in 5.6s    \n",
            "\n",
            "2019-10-21 01:16:46 (20.1 MB/s) - ‘DIV2K_valid_LR_bicubic_X2.zip’ saved [117763600/117763600]\n",
            "\n",
            "--2019-10-21 01:16:46--  http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip\n",
            "Resolving data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)... 129.132.52.162\n",
            "Connecting to data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)|129.132.52.162|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip [following]\n",
            "--2019-10-21 01:16:46--  https://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip\n",
            "Connecting to data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)|129.132.52.162|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3530603713 (3.3G) [application/zip]\n",
            "Saving to: ‘DIV2K_train_HR.zip’\n",
            "\n",
            "DIV2K_train_HR.zip  100%[===================>]   3.29G  23.4MB/s    in 2m 30s  \n",
            "\n",
            "2019-10-21 01:19:17 (22.5 MB/s) - ‘DIV2K_train_HR.zip’ saved [3530603713/3530603713]\n",
            "\n",
            "--2019-10-21 01:19:17--  http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_HR.zip\n",
            "Resolving data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)... 129.132.52.162\n",
            "Connecting to data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)|129.132.52.162|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
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            "--2019-10-21 01:19:17--  https://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_valid_HR.zip\n",
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            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 448993893 (428M) [application/zip]\n",
            "Saving to: ‘DIV2K_valid_HR.zip’\n",
            "\n",
            "DIV2K_valid_HR.zip  100%[===================>] 428.19M  23.2MB/s    in 19s     \n",
            "\n",
            "2019-10-21 01:19:37 (22.7 MB/s) - ‘DIV2K_valid_HR.zip’ saved [448993893/448993893]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CMUgC2k21lC9",
        "colab": {}
      },
      "source": [
        "!mkdir div2k\n",
        "!unzip -q DIV2K_valid_LR_bicubic_X2.zip -d div2k\n",
        "!unzip -q DIV2K_train_LR_bicubic_X2.zip -d div2k\n",
        "!unzip -q DIV2K_train_HR.zip -d div2k\n",
        "!unzip -q DIV2K_valid_HR.zip -d div2k"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LeNFEtJeNKTj"
      },
      "source": [
        "## Create the models\n",
        "Import the models from the ISR package and create\n",
        "\n",
        "- a RRDN super scaling network\n",
        "- a discriminator network for GANs training\n",
        "- a VGG19 feature extractor to train with a perceptual loss function\n",
        "\n",
        "Carefully select\n",
        "- 'x': this is the upscaling factor (2 by default)\n",
        "- 'layers_to_extract': these are the layers from the VGG19 that will be used in the perceptual loss (leave the default if you're not familiar with it)\n",
        "- 'lr_patch_size': this is the size of the patches that will be extracted from the LR images and fed to the ISR network during training time\n",
        "\n",
        "Play around with the other architecture parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8e2RSZq4BY_T",
        "outputId": "141629fd-abbe-4a12-af2f-e41cb78ec18c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from ISR.models import RRDN\n",
        "from ISR.models import Discriminator\n",
        "from ISR.models import Cut_VGG19"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "celHq8FjB5vA",
        "outputId": "a0582445-dff2-4b56-c342-964c13a3ffd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "lr_train_patch_size = 40\n",
        "layers_to_extract = [5, 9]\n",
        "scale = 2\n",
        "hr_train_patch_size = lr_train_patch_size * scale\n",
        "\n",
        "rrdn  = RRDN(arch_params={'C':4, 'D':3, 'G':64, 'G0':64, 'T':10, 'x':scale}, patch_size=lr_train_patch_size)\n",
        "f_ext = Cut_VGG19(patch_size=hr_train_patch_size, layers_to_extract=layers_to_extract)\n",
        "discr = Discriminator(patch_size=hr_train_patch_size, kernel_size=3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80142336/80134624 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "aaXfv12EPzal"
      },
      "source": [
        "## Give the models to the Trainer\n",
        "The Trainer object will combine the networks, manage your training data and keep you up-to-date with the training progress through Tensorboard and the command line.\n",
        "\n",
        "Here we do not use  the pixel-wise MSE but only the perceptual loss by specifying the respective weights in `loss_weights`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6AV0m-s8OaqI",
        "colab": {}
      },
      "source": [
        "from ISR.train import Trainer\n",
        "loss_weights = {\n",
        "  'generator': 0.0,\n",
        "  'feature_extractor': 0.0833,\n",
        "  'discriminator': 0.01\n",
        "}\n",
        "losses = {\n",
        "  'generator': 'mae',\n",
        "  'feature_extractor': 'mse',\n",
        "  'discriminator': 'binary_crossentropy'\n",
        "} \n",
        "\n",
        "log_dirs = {'logs': './logs', 'weights': './weights'}\n",
        "\n",
        "learning_rate = {'initial_value': 0.0004, 'decay_factor': 0.5, 'decay_frequency': 30}\n",
        "\n",
        "flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}\n",
        "\n",
        "trainer = Trainer(\n",
        "    generator=rrdn,\n",
        "    discriminator=discr,\n",
        "    feature_extractor=f_ext,\n",
        "    lr_train_dir='div2k/DIV2K_train_LR_bicubic/X2/',\n",
        "    hr_train_dir='div2k/DIV2K_train_HR/',\n",
        "    lr_valid_dir='div2k/DIV2K_train_LR_bicubic/X2/',\n",
        "    hr_valid_dir='div2k/DIV2K_train_HR/',\n",
        "    loss_weights=loss_weights,\n",
        "    learning_rate=learning_rate,\n",
        "    flatness=flatness,\n",
        "    dataname='div2k',\n",
        "    log_dirs=log_dirs,\n",
        "    weights_generator=None,\n",
        "    weights_discriminator=None,\n",
        "    n_validation=40,\n",
        ")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5UpepsY77r5M"
      },
      "source": [
        "Choose epoch number, steps and batch size and start training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YnvSnZUa7rA6",
        "outputId": "db75803b-734b-4279-ed8f-319afd960faa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        }
      },
      "source": [
        "trainer.train(\n",
        "    epochs=1,\n",
        "    steps_per_epoch=20,\n",
        "    batch_size=4,\n",
        "    monitored_metrics={'val_PSNR_Y': 'max'}\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Training details:\n",
            "  training_parameters: \n",
            "    metrics: {'generator': <function PSNR_Y at 0x7f040778da60>}\n",
            "    losses: {'generator': 'mae', 'discriminator': 'binary_crossentropy', 'feature_extractor': 'mse'}\n",
            "    adam_optimizer: {'beta1': 0.9, 'beta2': 0.999, 'epsilon': None}\n",
            "    learning_rate: {'initial_value': 0.0004, 'decay_factor': 0.5, 'decay_frequency': 30}\n",
            "    flatness: {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5}\n",
            "    n_validation: 40\n",
            "    dataname: div2k\n",
            "    fallback_save_every_n_epochs: 2\n",
            "    log_dirs: {'logs': './logs', 'weights': './weights'}\n",
            "    loss_weights: {'generator': 0.0, 'feature_extractor': 0.0833, 'discriminator': 0.01}\n",
            "    hr_valid_dir: div2k/DIV2K_train_HR/\n",
            "    lr_valid_dir: div2k/DIV2K_train_LR_bicubic/X2/\n",
            "    hr_train_dir: div2k/DIV2K_train_HR/\n",
            "    lr_train_dir: div2k/DIV2K_train_LR_bicubic/X2/\n",
            "    lr_patch_size: 40\n",
            "    steps_per_epoch: 20\n",
            "    batch_size: 4\n",
            "    starting_epoch: 0\n",
            "  generator: \n",
            "    name: rrdn\n",
            "    parameters: {'C': 4, 'D': 3, 'G': 64, 'G0': 64, 'T': 10, 'x': 2}\n",
            "    weights_generator: None\n",
            "  discriminator: \n",
            "    name: srgan-large\n",
            "    weights_discriminator: None\n",
            "  feature_extractor: \n",
            "    name: vgg19\n",
            "    layers: [5, 9]\n",
            "Epoch 0/1\n",
            "Current learning rate: 0.00039999998989515007\n",
            "  0%|          | 0/20 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:490: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
            "  'Discrepancy between trainable weights and collected trainable'\n",
            "100%|██████████| 20/20 [13:49<00:00, 39.44s/it]\n",
            "Epoch 0 took      829.9s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "160/160 [==============================] - 316s 2s/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "val_PSNR_Y is NOT among the model metrics, removing it.\n",
            "{'val_loss': 0.9829644300043583, 'val_generator_loss': 0.40370433069765566, 'val_discriminator_loss': 0.6998646706342697, 'val_feature_extractor_loss': 19.83274558186531, 'val_generator_PSNR_Y': 8.570307517051697, 'train_d_real_loss': 0.09677358, 'train_d_real_acc': 1.0, 'train_d_fake_loss': 0.30542096, 'train_d_fake_acc': 0.815625, 'train_loss': 0.3081001, 'train_generator_loss': 0.83961016, 'train_discriminator_loss': 4.408768, 'train_feature_extractor_loss': 5.180871, 'train_generator_PSNR_Y': 2.7880409}\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}