Denoising_autoencoder.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Denoising_autoencoder.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SaashaJoshi/Pancreas-Cancer/blob/master/Denoising_autoencoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxfVJD9bRXdB",
        "colab_type": "code",
        "outputId": "d3cdf1b3-934f-4254-e62b-3065a63cd928",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "pip install tensorflow-gpu"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0a/93/c7bca39b23aae45cd2e85ad3871c81eccc63b9c5276e926511e2e5b0879d/tensorflow_gpu-2.1.0-cp36-cp36m-manylinux2010_x86_64.whl (421.8MB)\n",
            "\u001b[K     |████████████████████████████████| 421.8MB 40kB/s \n",
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            "Collecting tensorboard<2.2.0,>=2.1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d9/41/bbf49b61370e4f4d245d4c6051dfb6db80cec672605c91b1652ac8cc3d38/tensorboard-2.1.1-py3-none-any.whl (3.8MB)\n",
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            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.12.0)\n",
            "Collecting tensorflow-estimator<2.2.0,>=2.1.0rc0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/18/90/b77c328a1304437ab1310b463e533fa7689f4bfc41549593056d812fab8e/tensorflow_estimator-2.1.0-py2.py3-none-any.whl (448kB)\n",
            "\u001b[K     |████████████████████████████████| 450kB 54.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.4.1)\n",
            "Collecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.34.2)\n",
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            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (2.21.0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (1.7.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (3.2.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (1.0.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (0.4.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (2020.4.5.1)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (1.24.3)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (3.0.4)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (2.8)\n",
            "Requirement already satisfied: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (4.0)\n",
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            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (1.3.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<4.1,>=3.1.4->google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow-gpu) (3.1.0)\n",
            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=7ea293d085ae18c1a8ec9427ba8cee1887291d5e7502d0b7aac138108bf26aca\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "Successfully built gast\n",
            "\u001b[31mERROR: tensorflow 2.2.0rc2 has requirement gast==0.3.3, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.2.0rc2 has requirement tensorboard<2.3.0,>=2.2.0, but you'll have tensorboard 2.1.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 2.2.0rc2 has requirement tensorflow-estimator<2.3.0,>=2.2.0rc0, but you'll have tensorflow-estimator 2.1.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tensorboard, tensorflow-estimator, gast, tensorflow-gpu\n",
            "  Found existing installation: tensorboard 2.2.0\n",
            "    Uninstalling tensorboard-2.2.0:\n",
            "      Successfully uninstalled tensorboard-2.2.0\n",
            "  Found existing installation: tensorflow-estimator 2.2.0rc0\n",
            "    Uninstalling tensorflow-estimator-2.2.0rc0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.2.0rc0\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "Successfully installed gast-0.2.2 tensorboard-2.1.1 tensorflow-estimator-2.1.0 tensorflow-gpu-2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ogcD5A3JviWG",
        "colab_type": "code",
        "outputId": "f0c97807-d67e-4cbf-cf58-cbaf0568b1b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "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&response_type=code&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\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ab_rFu04vrRR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import sys\n",
        "import cv2\n",
        "import random\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tqdm import tqdm\n",
        "from skimage.io import imread, imshow\n",
        "from skimage.transform import resize\n",
        "import matplotlib.pyplot as plt\n",
        "from glob import glob"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBwMcaZqv26G",
        "colab_type": "code",
        "outputId": "1d3bdcb8-a594-43ae-893c-b2b466e91c1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "data_path = 'drive/My Drive/pancreas_data_original/train_images'\n",
        "output_path = working_path = 'drive/My Drive/'\n",
        "g = glob(data_path + '/*.png')\n",
        "\n",
        "# Print out the first 5 file names to verify we're in the right folder.\n",
        "print (\"Total of %d images.\\nFirst 5 filenames:\" % len(g))\n",
        "print ('\\n'.join(g[:5]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total of 39 images.\n",
            "First 5 filenames:\n",
            "drive/My Drive/pancreas_data_original/train_images/000149.png\n",
            "drive/My Drive/pancreas_data_original/train_images/000150.png\n",
            "drive/My Drive/pancreas_data_original/train_images/000148.png\n",
            "drive/My Drive/pancreas_data_original/train_images/000145.png\n",
            "drive/My Drive/pancreas_data_original/train_images/000143.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9bN7_eiv-1s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from PIL import Image\n",
        "import os\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from matplotlib.pyplot import imshow\n",
        "\n",
        "# name = \"image\"\n",
        "# path = os.curdir + \"/datasetfinal/\"+ name\n",
        "\n",
        "test_dir = 'drive/My Drive/pancreas_data_original/test_images'\n",
        "train_dir = 'drive/My Drive/pancreas_data_original/train_images'\n",
        "\n",
        "new_path = 'drive/My Drive/pancreas_data_original/converted_train_images'\n",
        "if not os.path.exists(new_path):\n",
        "    os.makedirs(d)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sbgk4XUiwIUg",
        "colab_type": "code",
        "outputId": "ad0c1f84-a9e3-453a-d52f-75b24ed5e09c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "dirs = os.listdir(train_dir)\n",
        "print(dirs)\n",
        "\n",
        "for f in dirs:\n",
        "  full_new_path = os.path.join(new_path, f)\n",
        "  fullpath = os.path.join(train_dir,f) \n",
        "  if os.path.isfile(fullpath):    # os.path.isfile returns boolean value whether file exists or not\n",
        "    img=Image.open(fullpath)    # opens and identifies given image file\n",
        "    pixels = img.load()         # image data collected in Image.open is read here and loads pixel data\n",
        "    img_convert = img.convert('L')      # returns converted copy of image\n",
        "    imshow(img_convert)\n",
        "    img_convert.save(full_new_path)        # saves image under given filename"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['000149.png', '000150.png', '000148.png', '000145.png', '000143.png', '000144.png', '000141.png', '000140.png', '000134.png', '000137.png', '000135.png', '000132.png', '000131.png', '000130.png', '000129.png', '000127.png', '000124.png', '000125.png', '000123.png', '000117.png', '000126.png', '000118.png', '000116.png', '000119.png', '000115.png', '000113.png', '000114.png', '000112.png', '000110.png', '000111.png', '000109.png', '000105.png', '000108.png', '000103.png', '000101.png', '000104.png', '000102.png', '000100.png', '000146.png']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMiAnNaywdWn",
        "colab_type": "code",
        "outputId": "dd43e31d-98b6-4582-ddaf-39aedb7462fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "# Verify images in new_path\n",
        "for f in os.listdir(new_path):\n",
        "  full_new_path = os.path.join(new_path, f)\n",
        "  img = Image.open(full_new_path)\n",
        "  imshow(img)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wh0u-85ewuPs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "noise_factor = 10\n",
        "\n",
        "def gauss_noise_gen(path, noise_factor = 10):\n",
        "  for f in os.listdir(path):\n",
        "    full_new_path = os.path.join(new_path, f)\n",
        "    img = Image.open(full_new_path)\n",
        "    # for batch_x, batch_y in batches:\n",
        "    # Generate Gaussian noise\n",
        "    sigma = np.random.exponential(0.15)\n",
        "    gauss = np.random.normal(loc = 0, scale = sigma, size = img.size)     # loc = mean, scale = standard deviation\n",
        "    noise = noise_factor * gauss\n",
        "    img_gauss = img + noise\n",
        "    # img_gauss.save(full_new_path)\n",
        "    return img_gauss\n",
        "\n",
        "# X_train = new_path\n",
        "X_noisy = gauss_noise_gen(new_path, 10)\n",
        "# print(X_train_noisy)\n",
        "# print(X_train.shape)\n",
        "# print(X_train_noisy.shape)\n",
        "    \n",
        "    # batch_noisy = batch_x + noise\n",
        "    # gauss = gauss.reshape(img.shape[0],img.shape[1],img.shape[2]).astype('uint8')\n",
        "    # Add Gaussian noise to the image\n",
        "    # imshow(img_gauss)\n",
        "    # yield (batch_noisy, batch_y)\n",
        "\n",
        "# def poisson_noise_gen(noise_factor):\n",
        "#   for f in os.listdir(new_path):\n",
        "  #   full_new_path = os.path.join(new_path, f)\n",
        "  #   img = Image.open(full_new_path)\n",
        "  #   # Generate Gaussian noise\n",
        "  #   sigma = np.random.exponential(0.15)\n",
        "  #   poi = np.random.poisson(loc = 0, scale = sigma, size = img.size)     # loc = mean, scale = standard deviation\n",
        "  #   noise = noise_factor * poi\n",
        "  #   # gauss = gauss.reshape(img.shape[0],img.shape[1],img.shape[2]).astype('uint8')\n",
        "  #   # Add Gaussian noise to the image\n",
        "  #   img_poi = img + noise"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JeU5P5TXKdFv",
        "colab_type": "code",
        "outputId": "cc9ca5c5-6e61-4d38-f932-043df1639a2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "from keras.preprocessing.image import load_img, array_to_img, img_to_array\n",
        "X = []\n",
        "Y = []\n",
        "\n",
        "# Noisy Images\n",
        "for img in os.listdir(new_path):\n",
        "  full_new_path = os.path.join(new_path, img)\n",
        "  img = load_img(full_new_path, grayscale=True,target_size=(512, 512))\n",
        "  img = img_to_array(img).astype('float32')/255.\n",
        "  X.append(img)\n",
        "\n",
        "# Clean Images\n",
        "for img in os.listdir(train_dir):\n",
        "  full_path = os.path.join(train_dir, img)\n",
        "  img = load_img(full_path, grayscale=True,target_size=(512, 512))\n",
        "  img = img_to_array(img).astype('float32')/255.\n",
        "  Y.append(img)\n",
        "\n",
        "X = np.array(X)\n",
        "Y = np.array(Y)\n",
        "\n",
        "print(\"Size of X : \", X.shape)\n",
        "print(\"Size of Y : \", Y.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py:104: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
            "  warnings.warn('grayscale is deprecated. Please use '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Size of X :  (39, 512, 512, 1)\n",
            "Size of Y :  (39, 512, 512, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjHYqca3wzJb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, _, Y_train, _ = train_test_split(X, Y, test_size=0.1, random_state=111)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XjYTN1HJsKm0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# data_gen_args = dict()\n",
        "# #     featurewise_center=True,\n",
        "# #     featurewise_std_normalization=True,\n",
        "#     # rotation_range=20,\n",
        "#     # width_shift_range=0.2,\n",
        "#     # height_shift_range=0.2,\n",
        "#     # brightness_range=[0.5, 1.2],\n",
        "#     # shear_range=0.01,\n",
        "#     # horizontal_flip=True,\n",
        "#     # rescale=1/255,\n",
        "#     # fill_mode='reflect',\n",
        "#     # data_format='channels_last')\n",
        "\n",
        "# data_flow_args = dict(\n",
        "#     target_size = (512, 512),\n",
        "#     batch_size = 10,\n",
        "#     class_mode = 'input')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xomvtj3cPeXg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow import keras\n",
        "import keras.layers as layers\n",
        "import keras.models as models\n",
        "from keras.initializers import orthogonal\n",
        "\n",
        "def Conv2DLayer(x, filters, kernal, strides, padding, block_id, kernel_init = orthogonal()):\n",
        "  prefix = f'block_{block_id}_'\n",
        "  x = layers.Conv2D(filters, kernel_size = kernal, strides = strides, padding = padding, kernel_initializer = kernel_init, name = prefix + 'conv')(x)\n",
        "  x = layers.LeakyReLU(name = prefix + 'lrelu')(x)\n",
        "  x = layers.Dropout(0.2, name = prefix + 'drop')(x)\n",
        "  x = layers.BatchNormalization(name = prefix + 'conv_bn')\n",
        "\n",
        "def Transpose_Conv2DLayer(x, filters, kernal, strides, padding, block_id, kernel_init = orthogonal()):\n",
        "  prefix = f'block: {block_id}'\n",
        "  x = layers.Conv2DTranspose(filters, kernel_size = kernal, strides = strides, padding = padding, kernel_initializer = kernel_init, name = prefix + 'de-conv')(x)\n",
        "  x = layers.LeakyReLU(name = prefix + 'lrelu')(x)\n",
        "  x = layers.Dropout(0.2, name = prefix + 'drop')((x))\n",
        "  x = layers.BatchNormalization(name = prefix + 'conv_bn')(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6KRrSANNkywT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Conv2DLayer(x, filters, kernel, strides, padding, block_id, kernel_init=orthogonal()):\n",
        "  prefix = f'block_{block_id}_'\n",
        "  x = layers.Conv2D(filters, kernel_size=kernel, strides=strides, padding=padding,\n",
        "                    kernel_initializer=kernel_init, name=prefix+'conv')(x)\n",
        "  x = layers.LeakyReLU(name=prefix+'lrelu')(x)\n",
        "  x = layers.Dropout(0.2, name=prefix+'drop')((x))\n",
        "  x = layers.BatchNormalization(name=prefix+'conv_bn')(x)\n",
        "  return x\n",
        "\n",
        "def Transpose_Conv2D(x, filters, kernel, strides, padding, block_id, kernel_init=orthogonal()):\n",
        "  prefix = f'block_{block_id}_'\n",
        "  x = layers.Conv2DTranspose(filters, kernel_size=kernel, strides=strides, padding=padding,\n",
        "                              kernel_initializer=kernel_init, name=prefix+'de-conv')(x)\n",
        "  x = layers.LeakyReLU(name=prefix+'lrelu')(x)\n",
        "  x = layers.Dropout(0.2, name=prefix+'drop')((x))\n",
        "  x = layers.BatchNormalization(name=prefix+'conv_bn')(x)\n",
        "  return x\n",
        "\n",
        "from keras.optimizers import SGD, Adam\n",
        "\n",
        "def AutoEncoder():\n",
        "  inputs = layers.Input(shape=(512, 512, 1), name='image_input')\n",
        "                          \n",
        "  # 256 x 256\n",
        "  conv1 = Conv2DLayer(inputs, 64, 3, strides=1, padding='same', block_id=1)\n",
        "  conv2 = Conv2DLayer(conv1, 64, 3, strides=2, padding='same', block_id=2)\n",
        "  \n",
        "  # 128 x 128\n",
        "  conv3 = Conv2DLayer(conv2, 128, 5, strides=2, padding='same', block_id=3)\n",
        "  \n",
        "  # 64 x 64\n",
        "  conv4 = Conv2DLayer(conv3, 128, 3, strides=1, padding='same', block_id=4)\n",
        "  conv5 = Conv2DLayer(conv4, 256, 5, strides=2, padding='same', block_id=5)\n",
        "  \n",
        "  # 32 x 32\n",
        "  conv6 = Conv2DLayer(conv5, 512, 3, strides=2, padding='same', block_id=6)\n",
        "  \n",
        "  # 16 x 16\n",
        "  deconv1 = Transpose_Conv2D(conv6, 512, 3, strides=2, padding='same', block_id=7)\n",
        "  \n",
        "  # 32 x 32\n",
        "  skip1 = layers.concatenate([deconv1, conv5], name='skip1')\n",
        "  conv7 = Conv2DLayer(skip1, 256, 3, strides=1, padding='same', block_id=8)\n",
        "  deconv2 = Transpose_Conv2D(conv7, 128, 3, strides=2, padding='same', block_id=9)\n",
        "  \n",
        "  # 64 x 64\n",
        "  skip2 = layers.concatenate([deconv2, conv3], name='skip2')\n",
        "  conv8 = Conv2DLayer(skip2, 128, 5, strides=1, padding='same', block_id=10)\n",
        "  deconv3 = Transpose_Conv2D(conv8, 64, 3, strides=2, padding='same', block_id=11)\n",
        "  \n",
        "  # 128 x 128\n",
        "  skip3 = layers.concatenate([deconv3, conv2], name='skip3')\n",
        "  conv9 = Conv2DLayer(skip3, 64, 5, strides=1, padding='same', block_id=12)\n",
        "  deconv4 = Transpose_Conv2D(conv9, 64, 3, strides=2, padding='same', block_id=13)\n",
        "  \n",
        "  # 256 x 256\n",
        "  skip3 = layers.concatenate([deconv4, conv1])\n",
        "  conv10 = layers.Conv2D(1, 3, strides=1, padding='same', activation='sigmoid', kernel_initializer=orthogonal(), name='final_conv')(skip3)\n",
        "\n",
        "  model = models.Model(inputs=inputs, outputs=conv10)\n",
        "  model.compile(optimizer = Adam(lr = 0.002), loss = 'mse', metrics = ['accuracy'])\n",
        "  \n",
        "  return model\n",
        "\n",
        "\n",
        "  # inputs = layers.Input(shape = (512, 512, 3), name='image_input')\n",
        "\n",
        "  # # 256*256\n",
        "  # conv1 = Conv2DLayer(inputs, 64, 3, strides = 1, padding = 'same', block_id = 1)\n",
        "  # conv2 = Conv2DLayer(conv1, 64, 3, strides = 2, padding = 'same', block_id = 2)\n",
        "\n",
        "  # # 128*128\n",
        "  # conv3 = Conv2DLayer(conv2, 128, 5, strides = 2, padding = 'same', block_id = 1)\n",
        "\n",
        "  # # 64*64\n",
        "  # conv4 = Conv2DLayer(conv3, 128, 3, strides = 1, padding = 'same', block_id = 4)\n",
        "  # conv5 = Conv2DLayer(conv4, 256, 5, strides = 2, padding = 'same', block_id = 5)\n",
        "\n",
        "  # # 32*32\n",
        "  # conv6 = Conv2DLayer(conv5, 512, 3, strides = 2, padding = 'same', block_id = 6)\n",
        "\n",
        "  # # 16*16\n",
        "  # deconv1 = Transpose_Conv2D(conv6, 512, 3, strides = 2, padding = 'same', block_id = 7)\n",
        "\n",
        "  # # 32*322\n",
        "  # skip1 = layers.concatenate([deconv1, conv5], name = 'skip1')\n",
        "  # conv7 = Conv2DLayer(skip1, 256, 3, strides = 1, padding = 'same', block_id = 8)\n",
        "  # deconv2 = Transpose_Conv2D(conv7, 128, 3, strides = 2, padding = 'same', block_id = 9)\n",
        "\n",
        "  # # 64*64\n",
        "  # skip2 = layers.concatenate([deconv2, conv3], name = 'skip2')\n",
        "  # conv8 = Conv2DLayer(skip2, 128, 5, strides = 1, padding = 'same', block_id = 10)\n",
        "  # deconv3 = Transpose_Conv2D(conv8, 64, 3, strides = 2, padding = 'same', block_id = 11)\n",
        "\n",
        "  # # 128*128\n",
        "  # skip3 = layers.concatenate([deconv3, conv2], name = 'skip3')\n",
        "  # conv9 = Conv2DLayer(skip3, 64, 5, strides = 1, padding = 'same', block_id = 12)\n",
        "  # deconv4 = Transpose_Conv2D(conv9, 64, 3, strides = 2, padding = 'same', block_id = 13)\n",
        "\n",
        "  # # 256*256\n",
        "  # skip4 = layers.concatenate([deconv4, conv1])\n",
        "  # conv10 = layers.Conv2D(3, 3, strides = 1, padding = 'same', activation = 'sigmoid', kernel_initializer = orthogonal(), name ='final_conv')(skip3)\n",
        "\n",
        "  # outputs = conv10\n",
        "\n",
        "  # return models.Model(inputs = inputs, outputs = outputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhH2ZL2APba2",
        "colab_type": "code",
        "outputId": "a3b7c43c-5d99-499c-fc37-3800a83589a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = AutoEncoder()\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "image_input (InputLayer)        (None, 512, 512, 1)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "block_1_conv (Conv2D)           (None, 512, 512, 64) 640         image_input[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_lrelu (LeakyReLU)       (None, 512, 512, 64) 0           block_1_conv[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_1_drop (Dropout)          (None, 512, 512, 64) 0           block_1_lrelu[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_1_conv_bn (BatchNormaliza (None, 512, 512, 64) 256         block_1_drop[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_2_conv (Conv2D)           (None, 256, 256, 64) 36928       block_1_conv_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_lrelu (LeakyReLU)       (None, 256, 256, 64) 0           block_2_conv[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_2_drop (Dropout)          (None, 256, 256, 64) 0           block_2_lrelu[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_2_conv_bn (BatchNormaliza (None, 256, 256, 64) 256         block_2_drop[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_3_conv (Conv2D)           (None, 128, 128, 128 204928      block_2_conv_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_3_lrelu (LeakyReLU)       (None, 128, 128, 128 0           block_3_conv[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_3_drop (Dropout)          (None, 128, 128, 128 0           block_3_lrelu[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_3_conv_bn (BatchNormaliza (None, 128, 128, 128 512         block_3_drop[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_4_conv (Conv2D)           (None, 128, 128, 128 147584      block_3_conv_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_lrelu (LeakyReLU)       (None, 128, 128, 128 0           block_4_conv[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_4_drop (Dropout)          (None, 128, 128, 128 0           block_4_lrelu[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_4_conv_bn (BatchNormaliza (None, 128, 128, 128 512         block_4_drop[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_5_conv (Conv2D)           (None, 64, 64, 256)  819456      block_4_conv_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_lrelu (LeakyReLU)       (None, 64, 64, 256)  0           block_5_conv[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_5_drop (Dropout)          (None, 64, 64, 256)  0           block_5_lrelu[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_5_conv_bn (BatchNormaliza (None, 64, 64, 256)  1024        block_5_drop[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_6_conv (Conv2D)           (None, 32, 32, 512)  1180160     block_5_conv_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_6_lrelu (LeakyReLU)       (None, 32, 32, 512)  0           block_6_conv[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_6_drop (Dropout)          (None, 32, 32, 512)  0           block_6_lrelu[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_6_conv_bn (BatchNormaliza (None, 32, 32, 512)  2048        block_6_drop[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_7_de-conv (Conv2DTranspos (None, 64, 64, 512)  2359808     block_6_conv_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_lrelu (LeakyReLU)       (None, 64, 64, 512)  0           block_7_de-conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_drop (Dropout)          (None, 64, 64, 512)  0           block_7_lrelu[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_7_conv_bn (BatchNormaliza (None, 64, 64, 512)  2048        block_7_drop[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "skip1 (Concatenate)             (None, 64, 64, 768)  0           block_7_conv_bn[0][0]            \n",
            "                                                                 block_5_conv_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_conv (Conv2D)           (None, 64, 64, 256)  1769728     skip1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block_8_lrelu (LeakyReLU)       (None, 64, 64, 256)  0           block_8_conv[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_8_drop (Dropout)          (None, 64, 64, 256)  0           block_8_lrelu[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_8_conv_bn (BatchNormaliza (None, 64, 64, 256)  1024        block_8_drop[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_9_de-conv (Conv2DTranspos (None, 128, 128, 128 295040      block_8_conv_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_lrelu (LeakyReLU)       (None, 128, 128, 128 0           block_9_de-conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_drop (Dropout)          (None, 128, 128, 128 0           block_9_lrelu[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_9_conv_bn (BatchNormaliza (None, 128, 128, 128 512         block_9_drop[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "skip2 (Concatenate)             (None, 128, 128, 256 0           block_9_conv_bn[0][0]            \n",
            "                                                                 block_3_conv_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_conv (Conv2D)          (None, 128, 128, 128 819328      skip2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_lrelu (LeakyReLU)      (None, 128, 128, 128 0           block_10_conv[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_10_drop (Dropout)         (None, 128, 128, 128 0           block_10_lrelu[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_10_conv_bn (BatchNormaliz (None, 128, 128, 128 512         block_10_drop[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_11_de-conv (Conv2DTranspo (None, 256, 256, 64) 73792       block_10_conv_bn[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_lrelu (LeakyReLU)      (None, 256, 256, 64) 0           block_11_de-conv[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_drop (Dropout)         (None, 256, 256, 64) 0           block_11_lrelu[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_11_conv_bn (BatchNormaliz (None, 256, 256, 64) 256         block_11_drop[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "skip3 (Concatenate)             (None, 256, 256, 128 0           block_11_conv_bn[0][0]           \n",
            "                                                                 block_2_conv_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_conv (Conv2D)          (None, 256, 256, 64) 204864      skip3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_lrelu (LeakyReLU)      (None, 256, 256, 64) 0           block_12_conv[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_12_drop (Dropout)         (None, 256, 256, 64) 0           block_12_lrelu[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_12_conv_bn (BatchNormaliz (None, 256, 256, 64) 256         block_12_drop[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block_13_de-conv (Conv2DTranspo (None, 512, 512, 64) 36928       block_12_conv_bn[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_13_lrelu (LeakyReLU)      (None, 512, 512, 64) 0           block_13_de-conv[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_13_drop (Dropout)         (None, 512, 512, 64) 0           block_13_lrelu[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_13_conv_bn (BatchNormaliz (None, 512, 512, 64) 256         block_13_drop[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_4 (Concatenate)     (None, 512, 512, 128 0           block_13_conv_bn[0][0]           \n",
            "                                                                 block_1_conv_bn[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "final_conv (Conv2D)             (None, 512, 512, 1)  1153        concatenate_4[0][0]              \n",
            "==================================================================================================\n",
            "Total params: 7,959,809\n",
            "Trainable params: 7,955,073\n",
            "Non-trainable params: 4,736\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQUnGi-Nx79R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train_datagen = ImageDataGenerator()\n",
        "\n",
        "# train_batches = train_datagen.flow_from_directory(new_path , target_size = (512, 512), batch_size = 10, class_mode = 'input')\n",
        "# train_noisy_batches = gauss_noise_gen(train_batches, 10)\n",
        "# noisy_images_train = gauss_noise_gen() "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4FB7yDwxq2E",
        "colab_type": "code",
        "outputId": "311de28d-3056-4f3c-83a8-543132dcbac0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "# model.fit(train_noisy, \n",
        "#           steps_per_epoch = train_batches.samples // batch_size,\n",
        "#           epochs = 10,\n",
        "#           verbose=1, \n",
        "#           validation_data = val_noisy_batches,\n",
        "#           validation_steps = train_batches.samples // batch_size,)\n",
        "\n",
        "model.fit(X_train, Y_train, epochs = 10, batch_size = 8)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "35/35 [==============================] - 5s 148ms/step - loss: 0.1537 - accuracy: 0.6760\n",
            "Epoch 2/10\n",
            "35/35 [==============================] - 3s 84ms/step - loss: 0.0837 - accuracy: 0.7587\n",
            "Epoch 3/10\n",
            "35/35 [==============================] - 3s 84ms/step - loss: 0.0666 - accuracy: 0.7639\n",
            "Epoch 4/10\n",
            "35/35 [==============================] - 3s 84ms/step - loss: 0.0544 - accuracy: 0.7771\n",
            "Epoch 5/10\n",
            "35/35 [==============================] - 3s 84ms/step - loss: 0.0473 - accuracy: 0.7805\n",
            "Epoch 6/10\n",
            "35/35 [==============================] - 3s 84ms/step - loss: 0.0405 - accuracy: 0.7841\n",
            "Epoch 7/10\n",
            "35/35 [==============================] - 3s 84ms/step - loss: 0.0358 - accuracy: 0.7860\n",
            "Epoch 8/10\n",
            "35/35 [==============================] - 3s 84ms/step - loss: 0.0327 - accuracy: 0.7877\n",
            "Epoch 9/10\n",
            "35/35 [==============================] - 3s 84ms/step - loss: 0.0304 - accuracy: 0.7882\n",
            "Epoch 10/10\n",
            "35/35 [==============================] - 3s 84ms/step - loss: 0.0310 - accuracy: 0.7891\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7f68e45ec518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSYj-KKGT9Hx",
        "colab_type": "code",
        "outputId": "868c7d50-5984-46f7-d9c5-d73f4feb63a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "test_noisy = gauss_noise_gen(train_dir, 10)\n",
        "noisy = []\n",
        "# Noisy Images\n",
        "for img in os.listdir(new_path):\n",
        "  full_new_path = os.path.join(new_path, img)\n",
        "  img = load_img(full_new_path, grayscale=True,target_size=(512, 512))\n",
        "  img = img_to_array(img).astype('float32')/255.\n",
        "  noisy.append(img)\n",
        "noisy = np.array(noisy)\n",
        "print(noisy.shape)\n",
        "# X, Y = next(test_noisy)\n",
        "\n",
        "# score = model.evaluate(test_noisy, verbose=1)\n",
        "# print('Test loss:', score[0])\n",
        "# print('Test accuracy:', score[1])\n",
        "\n",
        "model.predict(noisy)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py:104: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
            "  warnings.warn('grayscale is deprecated. Please use '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "(39, 512, 512, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ResourceExhaustedError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-76-887b605b2d9d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# print('Test accuracy:', score[1])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoisy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1460\u001b[0m                                             \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1461\u001b[0m                                             \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1462\u001b[0;31m                                             callbacks=callbacks)\n\u001b[0m\u001b[1;32m   1463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1464\u001b[0m     def train_on_batch(self, x, y,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mpredict_loop\u001b[0;34m(model, f, ins, batch_size, verbose, steps, callbacks)\u001b[0m\n\u001b[1;32m    322\u001b[0m             \u001b[0mbatch_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'batch'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'size'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'predict'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'begin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3725\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3726\u001b[0m       \u001b[0mconverted_inputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3727\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mconverted_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3729\u001b[0m     \u001b[0;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1549\u001b[0m       \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFor\u001b[0m \u001b[0minvalid\u001b[0m \u001b[0mpositional\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mkeyword\u001b[0m \u001b[0margument\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1550\u001b[0m     \"\"\"\n\u001b[0;32m-> 1551\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1552\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1553\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1589\u001b[0m       raise TypeError(\"Keyword arguments {} unknown. Expected {}.\".format(\n\u001b[1;32m   1590\u001b[0m           list(kwargs.keys()), list(self._arg_keywords)))\n\u001b[0;32m-> 1591\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1593\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1690\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1691\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1692\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1693\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1694\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    543\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"executor_type\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"config_proto\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m     \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     keras_symbolic_tensors = [\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
            "\u001b[0;31mResourceExhaustedError\u001b[0m:  OOM when allocating tensor with shape[32,512,512,128] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[node concatenate_4/concat (defined at /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3009) ]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.\n [Op:__inference_keras_scratch_graph_31292]\n\nFunction call stack:\nkeras_scratch_graph\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kl_KYJjsQ8cW",
        "colab_type": "code",
        "outputId": "e8c50195-a067-4ecf-a9ee-608a2e05e81c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "# # test_noisy = gauss_noise_gen(train_dir, 10)\n",
        "# sample_test = load_img(X_train, target_size=(512, 512))\n",
        "# sample_test = img_to_array(sample_test)\n",
        "# sample_test_img = sample_test.astype('float32')/255.\n",
        "# sample_test_img = np.expand_dims(sample_test, axis=0)\n",
        "\n",
        "# # Get the predition\n",
        "# predicted_label = np.squeeze(autoencoder.predict(sample_test_img))\n",
        "\n",
        "# f, ax = plt.subplots(1,2, figsize=(10,8))\n",
        "# ax[0].imshow(np.squeeze(sample_test), cmap='gray')\n",
        "# ax[1].imshow(np.squeeze(predicted_label.astype('int8')), cmap='gray')\n",
        "# plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2812\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2813\u001b[0;31m         \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2814\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUnsupportedOperation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'seek'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-67-648b59d8d7c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msample_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m512\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m512\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0msample_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_to_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msample_test_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float32'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m255.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0msample_test_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py\u001b[0m in \u001b[0;36mload_img\u001b[0;34m(path, grayscale, color_mode, target_size, interpolation)\u001b[0m\n\u001b[1;32m    108\u001b[0m         raise ImportError('Could not import PIL.Image. '\n\u001b[1;32m    109\u001b[0m                           'The use of `load_img` requires PIL.')\n\u001b[0;32m--> 110\u001b[0;31m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpil_image\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcolor_mode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'grayscale'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'L'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2813\u001b[0m         \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2814\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUnsupportedOperation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2815\u001b[0;31m         \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2816\u001b[0m         \u001b[0mexclusive_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'read'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KmOAHtqoRis1",
        "colab_type": "code",
        "outputId": "8bdfbda8-bbb4-4c4b-a9b0-2983e56c6b9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 929
        }
      },
      "source": [
        "f, ax = plt.subplots(1,2, figsize=(10,8))\n",
        "ax[0].imshow(np.squeeze(X_train[10]), cmap='gray')\n",
        "sample_test = load_img(X_train, grayscale=True, target_size=(512, 512))\n",
        "sample_test = img_to_array(X_train)\n",
        "sample_test_img = sample_test.astype('float32')/255.\n",
        "sample_test_img = np.expand_dims(sample_test, axis=0)\n",
        "predicted_label = np.squeeze(AutoEncoder.predict(sample_test_image))\n",
        "ax[1].imshow(np.squeeze(predicted_label.astype('int8')), cmap='gray')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py:104: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
            "  warnings.warn('grayscale is deprecated. Please use '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2812\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2813\u001b[0;31m         \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2814\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUnsupportedOperation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'seek'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-51-471962e9d31b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msample_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_img\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrayscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m512\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m512\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0msample_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg_to_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msample_test_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float32'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m255.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py\u001b[0m in \u001b[0;36mload_img\u001b[0;34m(path, grayscale, color_mode, target_size, interpolation)\u001b[0m\n\u001b[1;32m    108\u001b[0m         raise ImportError('Could not import PIL.Image. '\n\u001b[1;32m    109\u001b[0m                           'The use of `load_img` requires PIL.')\n\u001b[0;32m--> 110\u001b[0;31m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpil_image\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcolor_mode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'grayscale'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'L'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2813\u001b[0m         \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2814\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUnsupportedOperation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2815\u001b[0;31m         \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2816\u001b[0m         \u001b[0mexclusive_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'read'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWctRiNuR-fK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}