autoencoder.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "13CFC4xHEPqDc6jOiH7VXFDdGy4tgHedE",
      "authorship_tag": "ABX9TyPtO9ikNxtIEhk6RwaPBzRx",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/akshat7gupta/deep-learning/blob/main/autoencoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VJ3Cr_pTTdHE"
      },
      "outputs": [],
      "source": [
        "\n",
        "from matplotlib.pyplot import imshow\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import cv2\n",
        "from keras.preprocessing.image import img_to_array\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "SIZE=256\n",
        "img_data=[]\n",
        "\n",
        "img=cv2.imread('/content/drive/MyDrive/monalisa/monalisa.jpg', 0)   #Change 1 to 0 for grey images\n",
        "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #Changing BGR to RGB to show images in true colors\n",
        "img=cv2.resize(img,(SIZE, SIZE))\n",
        "img_data.append(img_to_array(img))\n",
        "\n",
        "img_array = np.reshape(img_data, (len(img_data), SIZE, SIZE, 3))\n",
        "img_array = img_array.astype('float32') / 255.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(SIZE, SIZE, 3)))\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",
        "\n",
        "\n",
        "model.add(MaxPooling2D((2, 2), padding='same'))\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(32, (3, 3), activation='relu', padding='same'))\n",
        "model.add(UpSampling2D((2, 2)))\n",
        "model.add(Conv2D(3, (3, 3), activation='relu', padding='same'))\n",
        "\n",
        "model.compile(optimizer='RMSprop', loss='mean_squared_error', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "3tZxVUHZVBdR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1696487c-32c4-4409-df5c-d7ac812bada4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 256, 256, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 128, 128, 32)      0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 128, 128, 8)       2312      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 64, 64, 8)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 64, 64, 8)         584       \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 32, 32, 8)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 32, 32, 8)         584       \n",
            "                                                                 \n",
            " up_sampling2d (UpSampling2  (None, 64, 64, 8)         0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 64, 64, 8)         584       \n",
            "                                                                 \n",
            " up_sampling2d_1 (UpSamplin  (None, 128, 128, 8)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 128, 128, 32)      2336      \n",
            "                                                                 \n",
            " up_sampling2d_2 (UpSamplin  (None, 256, 256, 32)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 256, 256, 3)       867       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8163 (31.89 KB)\n",
            "Trainable params: 8163 (31.89 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(img_array, img_array,\n",
        "        epochs=200,\n",
        "        shuffle=True)\n",
        "\n",
        "\n",
        "print(\"Neural network output\")\n",
        "pred = model.predict(img_array)\n",
        "\n",
        "\n",
        "\n",
        "imshow(pred[0].reshape(SIZE,SIZE,3), cmap=\"gray\")"
      ],
      "metadata": {
        "id": "YEUdz47nVNRN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6fa99475-236c-4c63-c78b-f1b5beb8f8ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.1856 - accuracy: 0.0184\n",
            "Epoch 2/200\n",
            "1/1 [==============================] - 0s 232ms/step - loss: 0.1498 - accuracy: 0.0045\n",
            "Epoch 3/200\n",
            "1/1 [==============================] - 0s 235ms/step - loss: 0.0812 - accuracy: 0.0000e+00\n",
            "Epoch 4/200\n",
            "1/1 [==============================] - 0s 210ms/step - loss: 0.1069 - accuracy: 0.0000e+00\n",
            "Epoch 5/200\n",
            "1/1 [==============================] - 0s 227ms/step - loss: 0.0961 - accuracy: 0.0092\n",
            "Epoch 6/200\n",
            "1/1 [==============================] - 0s 208ms/step - loss: 0.0744 - accuracy: 0.0109\n",
            "Epoch 7/200\n",
            "1/1 [==============================] - 0s 210ms/step - loss: 0.0524 - accuracy: 0.0113\n",
            "Epoch 8/200\n",
            "1/1 [==============================] - 0s 215ms/step - loss: 0.0203 - accuracy: 0.0203\n",
            "Epoch 9/200\n",
            "1/1 [==============================] - 0s 223ms/step - loss: 0.0153 - accuracy: 0.0847\n",
            "Epoch 10/200\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.0147 - accuracy: 0.2286\n",
            "Epoch 11/200\n",
            "1/1 [==============================] - 0s 221ms/step - loss: 0.0144 - accuracy: 0.2772\n",
            "Epoch 12/200\n",
            "1/1 [==============================] - 0s 229ms/step - loss: 0.0144 - accuracy: 0.3228\n",
            "Epoch 13/200\n",
            "1/1 [==============================] - 0s 215ms/step - loss: 0.0145 - accuracy: 0.2919\n",
            "Epoch 14/200\n",
            "1/1 [==============================] - 0s 204ms/step - loss: 0.0155 - accuracy: 0.2395\n",
            "Epoch 15/200\n",
            "1/1 [==============================] - 0s 204ms/step - loss: 0.0174 - accuracy: 0.5462\n",
            "Epoch 16/200\n",
            "1/1 [==============================] - 0s 225ms/step - loss: 0.0232 - accuracy: 0.2214\n",
            "Epoch 17/200\n",
            "1/1 [==============================] - 0s 242ms/step - loss: 0.0203 - accuracy: 0.5793\n",
            "Epoch 18/200\n",
            "1/1 [==============================] - 0s 210ms/step - loss: 0.0249 - accuracy: 0.2795\n",
            "Epoch 19/200\n",
            "1/1 [==============================] - 0s 204ms/step - loss: 0.0158 - accuracy: 0.5410\n",
            "Epoch 20/200\n",
            "1/1 [==============================] - 0s 208ms/step - loss: 0.0169 - accuracy: 0.3022\n",
            "Epoch 21/200\n",
            "1/1 [==============================] - 0s 212ms/step - loss: 0.0151 - accuracy: 0.4752\n",
            "Epoch 22/200\n",
            "1/1 [==============================] - 0s 225ms/step - loss: 0.0173 - accuracy: 0.3121\n",
            "Epoch 23/200\n",
            "1/1 [==============================] - 0s 216ms/step - loss: 0.0169 - accuracy: 0.5295\n",
            "Epoch 24/200\n",
            "1/1 [==============================] - 0s 212ms/step - loss: 0.0192 - accuracy: 0.2479\n",
            "Epoch 25/200\n",
            "1/1 [==============================] - 0s 213ms/step - loss: 0.0156 - accuracy: 0.5876\n",
            "Epoch 26/200\n",
            "1/1 [==============================] - 0s 220ms/step - loss: 0.0169 - accuracy: 0.2637\n",
            "Epoch 27/200\n",
            "1/1 [==============================] - 0s 216ms/step - loss: 0.0148 - accuracy: 0.5601\n",
            "Epoch 28/200\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.0165 - accuracy: 0.2222\n",
            "Epoch 29/200\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.0152 - accuracy: 0.6404\n",
            "Epoch 30/200\n",
            "1/1 [==============================] - 0s 196ms/step - loss: 0.0174 - accuracy: 0.2236\n",
            "Epoch 31/200\n",
            "1/1 [==============================] - 0s 230ms/step - loss: 0.0150 - accuracy: 0.5491\n",
            "Epoch 32/200\n",
            "1/1 [==============================] - 0s 203ms/step - loss: 0.0172 - accuracy: 0.2324\n",
            "Epoch 33/200\n",
            "1/1 [==============================] - 0s 230ms/step - loss: 0.0154 - accuracy: 0.6628\n",
            "Epoch 34/200\n",
            "1/1 [==============================] - 0s 212ms/step - loss: 0.0172 - accuracy: 0.2150\n",
            "Epoch 35/200\n",
            "1/1 [==============================] - 0s 200ms/step - loss: 0.0141 - accuracy: 0.5848\n",
            "Epoch 36/200\n",
            "1/1 [==============================] - 0s 228ms/step - loss: 0.0155 - accuracy: 0.2653\n",
            "Epoch 37/200\n",
            "1/1 [==============================] - 0s 212ms/step - loss: 0.0138 - accuracy: 0.5642\n",
            "Epoch 38/200\n",
            "1/1 [==============================] - 0s 210ms/step - loss: 0.0154 - accuracy: 0.2092\n",
            "Epoch 39/200\n",
            "1/1 [==============================] - 0s 206ms/step - loss: 0.0140 - accuracy: 0.6602\n",
            "Epoch 40/200\n",
            "1/1 [==============================] - 0s 210ms/step - loss: 0.0160 - accuracy: 0.2090\n",
            "Epoch 41/200\n",
            "1/1 [==============================] - 0s 216ms/step - loss: 0.0139 - accuracy: 0.5936\n",
            "Epoch 42/200\n",
            "1/1 [==============================] - 0s 267ms/step - loss: 0.0159 - accuracy: 0.2244\n",
            "Epoch 43/200\n",
            "1/1 [==============================] - 0s 318ms/step - loss: 0.0137 - accuracy: 0.6610\n",
            "Epoch 44/200\n",
            "1/1 [==============================] - 0s 334ms/step - loss: 0.0157 - accuracy: 0.2302\n",
            "Epoch 45/200\n",
            "1/1 [==============================] - 0s 319ms/step - loss: 0.0135 - accuracy: 0.5692\n",
            "Epoch 46/200\n",
            "1/1 [==============================] - 0s 373ms/step - loss: 0.0152 - accuracy: 0.2569\n",
            "Epoch 47/200\n",
            "1/1 [==============================] - 0s 371ms/step - loss: 0.0130 - accuracy: 0.6484\n",
            "Epoch 48/200\n",
            "1/1 [==============================] - 0s 359ms/step - loss: 0.0145 - accuracy: 0.2275\n",
            "Epoch 49/200\n",
            "1/1 [==============================] - 0s 343ms/step - loss: 0.0125 - accuracy: 0.6239\n",
            "Epoch 50/200\n",
            "1/1 [==============================] - 0s 355ms/step - loss: 0.0145 - accuracy: 0.2897\n",
            "Epoch 51/200\n",
            "1/1 [==============================] - 0s 369ms/step - loss: 0.0134 - accuracy: 0.6143\n",
            "Epoch 52/200\n",
            "1/1 [==============================] - 0s 350ms/step - loss: 0.0156 - accuracy: 0.2704\n",
            "Epoch 53/200\n",
            "1/1 [==============================] - 0s 362ms/step - loss: 0.0128 - accuracy: 0.6489\n",
            "Epoch 54/200\n",
            "1/1 [==============================] - 0s 352ms/step - loss: 0.0142 - accuracy: 0.2702\n",
            "Epoch 55/200\n",
            "1/1 [==============================] - 0s 356ms/step - loss: 0.0119 - accuracy: 0.6141\n",
            "Epoch 56/200\n",
            "1/1 [==============================] - 0s 399ms/step - loss: 0.0131 - accuracy: 0.3089\n",
            "Epoch 57/200\n",
            "1/1 [==============================] - 0s 334ms/step - loss: 0.0117 - accuracy: 0.6224\n",
            "Epoch 58/200\n",
            "1/1 [==============================] - 0s 325ms/step - loss: 0.0132 - accuracy: 0.2792\n",
            "Epoch 59/200\n",
            "1/1 [==============================] - 0s 330ms/step - loss: 0.0120 - accuracy: 0.6680\n",
            "Epoch 60/200\n",
            "1/1 [==============================] - 0s 208ms/step - loss: 0.0139 - accuracy: 0.2564\n",
            "Epoch 61/200\n",
            "1/1 [==============================] - 0s 204ms/step - loss: 0.0119 - accuracy: 0.6144\n",
            "Epoch 62/200\n",
            "1/1 [==============================] - 0s 208ms/step - loss: 0.0152 - accuracy: 0.4651\n",
            "Epoch 63/200\n",
            "1/1 [==============================] - 0s 208ms/step - loss: 0.0134 - accuracy: 0.4628\n",
            "Epoch 64/200\n",
            "1/1 [==============================] - 0s 224ms/step - loss: 0.0152 - accuracy: 0.5497\n",
            "Epoch 65/200\n",
            "1/1 [==============================] - 0s 234ms/step - loss: 0.0109 - accuracy: 0.6175\n",
            "Epoch 66/200\n",
            "1/1 [==============================] - 0s 222ms/step - loss: 0.0114 - accuracy: 0.3777\n",
            "Epoch 67/200\n",
            "1/1 [==============================] - 0s 204ms/step - loss: 0.0100 - accuracy: 0.6254\n",
            "Epoch 68/200\n",
            "1/1 [==============================] - 0s 208ms/step - loss: 0.0108 - accuracy: 0.3013\n",
            "Epoch 69/200\n",
            "1/1 [==============================] - 0s 216ms/step - loss: 0.0103 - accuracy: 0.6974\n",
            "Epoch 70/200\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0122 - accuracy: 0.1837\n",
            "Epoch 71/200\n",
            "1/1 [==============================] - 0s 204ms/step - loss: 0.0116 - accuracy: 0.7179\n",
            "Epoch 72/200\n",
            "1/1 [==============================] - 0s 215ms/step - loss: 0.0140 - accuracy: 0.1807\n",
            "Epoch 73/200\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.0111 - accuracy: 0.6626\n",
            "Epoch 74/200\n",
            "1/1 [==============================] - 0s 226ms/step - loss: 0.0134 - accuracy: 0.5202\n",
            "Epoch 75/200\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.0112 - accuracy: 0.3015\n",
            "Epoch 76/200\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0128 - accuracy: 0.7050\n",
            "Epoch 77/200\n",
            "1/1 [==============================] - 0s 214ms/step - loss: 0.0102 - accuracy: 0.2927\n",
            "Epoch 78/200\n",
            "1/1 [==============================] - 0s 224ms/step - loss: 0.0113 - accuracy: 0.6551\n",
            "Epoch 79/200\n",
            "1/1 [==============================] - 0s 219ms/step - loss: 0.0100 - accuracy: 0.5192\n",
            "Epoch 80/200\n",
            "1/1 [==============================] - 0s 216ms/step - loss: 0.0112 - accuracy: 0.4716\n",
            "Epoch 81/200\n",
            "1/1 [==============================] - 0s 216ms/step - loss: 0.0100 - accuracy: 0.7050\n",
            "Epoch 82/200\n",
            "1/1 [==============================] - 0s 198ms/step - loss: 0.0117 - accuracy: 0.2179\n",
            "Epoch 83/200\n",
            "1/1 [==============================] - 0s 220ms/step - loss: 0.0105 - accuracy: 0.6435\n",
            "Epoch 84/200\n",
            "1/1 [==============================] - 0s 196ms/step - loss: 0.0126 - accuracy: 0.2830\n",
            "Epoch 85/200\n",
            "1/1 [==============================] - 0s 210ms/step - loss: 0.0106 - accuracy: 0.6509\n",
            "Epoch 86/200\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.0121 - accuracy: 0.3767\n",
            "Epoch 87/200\n",
            "1/1 [==============================] - 0s 216ms/step - loss: 0.0097 - accuracy: 0.5930\n",
            "Epoch 88/200\n",
            "1/1 [==============================] - 0s 224ms/step - loss: 0.0109 - accuracy: 0.4877\n",
            "Epoch 89/200\n",
            "1/1 [==============================] - 0s 198ms/step - loss: 0.0096 - accuracy: 0.5692\n",
            "Epoch 90/200\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.0108 - accuracy: 0.4929\n",
            "Epoch 91/200\n",
            "1/1 [==============================] - 0s 212ms/step - loss: 0.0094 - accuracy: 0.5835\n",
            "Epoch 92/200\n",
            "1/1 [==============================] - 0s 198ms/step - loss: 0.0110 - accuracy: 0.4384\n",
            "Epoch 93/200\n",
            "1/1 [==============================] - 0s 229ms/step - loss: 0.0100 - accuracy: 0.5777\n",
            "Epoch 94/200\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.0117 - accuracy: 0.3948\n",
            "Epoch 95/200\n",
            "1/1 [==============================] - 0s 206ms/step - loss: 0.0095 - accuracy: 0.6415\n",
            "Epoch 96/200\n",
            "1/1 [==============================] - 0s 219ms/step - loss: 0.0108 - accuracy: 0.3294\n",
            "Epoch 97/200\n",
            "1/1 [==============================] - 0s 195ms/step - loss: 0.0093 - accuracy: 0.5980\n",
            "Epoch 98/200\n",
            "1/1 [==============================] - 0s 222ms/step - loss: 0.0110 - accuracy: 0.4376\n",
            "Epoch 99/200\n",
            "1/1 [==============================] - 0s 216ms/step - loss: 0.0096 - accuracy: 0.5672\n",
            "Epoch 100/200\n",
            "1/1 [==============================] - 0s 204ms/step - loss: 0.0112 - accuracy: 0.4346\n",
            "Epoch 101/200\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0094 - accuracy: 0.6410\n",
            "Epoch 102/200\n",
            "1/1 [==============================] - 0s 218ms/step - loss: 0.0108 - accuracy: 0.3543\n",
            "Epoch 103/200\n",
            "1/1 [==============================] - 0s 207ms/step - loss: 0.0091 - accuracy: 0.5981\n",
            "Epoch 104/200\n",
            "1/1 [==============================] - 0s 202ms/step - loss: 0.0107 - accuracy: 0.4394\n",
            "Epoch 105/200\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0093 - accuracy: 0.5868\n",
            "Epoch 106/200\n",
            "1/1 [==============================] - 0s 306ms/step - loss: 0.0107 - accuracy: 0.4033\n",
            "Epoch 107/200\n",
            "1/1 [==============================] - 0s 317ms/step - loss: 0.0090 - accuracy: 0.6457\n",
            "Epoch 108/200\n",
            "1/1 [==============================] - 0s 350ms/step - loss: 0.0105 - accuracy: 0.3427\n",
            "Epoch 109/200\n",
            "1/1 [==============================] - 0s 358ms/step - loss: 0.0093 - accuracy: 0.6127\n",
            "Epoch 110/200\n",
            "1/1 [==============================] - 0s 350ms/step - loss: 0.0107 - accuracy: 0.3890\n",
            "Epoch 111/200\n",
            "1/1 [==============================] - 0s 353ms/step - loss: 0.0086 - accuracy: 0.6318\n",
            "Epoch 112/200\n",
            "1/1 [==============================] - 0s 334ms/step - loss: 0.0103 - accuracy: 0.3847\n",
            "Epoch 113/200\n",
            "1/1 [==============================] - 0s 341ms/step - loss: 0.0094 - accuracy: 0.5862\n",
            "Epoch 114/200\n",
            "1/1 [==============================] - 0s 338ms/step - loss: 0.0111 - accuracy: 0.4010\n",
            "Epoch 115/200\n",
            "1/1 [==============================] - 0s 352ms/step - loss: 0.0090 - accuracy: 0.6456\n",
            "Epoch 116/200\n",
            "1/1 [==============================] - 0s 369ms/step - loss: 0.0103 - accuracy: 0.3366\n",
            "Epoch 117/200\n",
            "1/1 [==============================] - 0s 329ms/step - loss: 0.0087 - accuracy: 0.6149\n",
            "Epoch 118/200\n",
            "1/1 [==============================] - 0s 363ms/step - loss: 0.0099 - accuracy: 0.3911\n",
            "Epoch 119/200\n",
            "1/1 [==============================] - 0s 356ms/step - loss: 0.0086 - accuracy: 0.6255\n",
            "Epoch 120/200\n",
            "1/1 [==============================] - 0s 372ms/step - loss: 0.0099 - accuracy: 0.3400\n",
            "Epoch 121/200\n",
            "1/1 [==============================] - 0s 322ms/step - loss: 0.0085 - accuracy: 0.6346\n",
            "Epoch 122/200\n",
            "1/1 [==============================] - 0s 336ms/step - loss: 0.0101 - accuracy: 0.3587\n",
            "Epoch 123/200\n",
            "1/1 [==============================] - 0s 286ms/step - loss: 0.0092 - accuracy: 0.6086\n",
            "Epoch 124/200\n",
            "1/1 [==============================] - 0s 206ms/step - loss: 0.0107 - accuracy: 0.3620\n",
            "Epoch 125/200\n",
            "1/1 [==============================] - 0s 199ms/step - loss: 0.0086 - accuracy: 0.6395\n",
            "Epoch 126/200\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.0098 - accuracy: 0.3238\n",
            "Epoch 127/200\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.0082 - accuracy: 0.6252\n",
            "Epoch 128/200\n",
            "1/1 [==============================] - 0s 200ms/step - loss: 0.0096 - accuracy: 0.3651\n",
            "Epoch 129/200\n",
            "1/1 [==============================] - 0s 217ms/step - loss: 0.0087 - accuracy: 0.6106\n",
            "Epoch 130/200\n",
            "1/1 [==============================] - 0s 222ms/step - loss: 0.0101 - accuracy: 0.3562\n",
            "Epoch 131/200\n",
            "1/1 [==============================] - 0s 228ms/step - loss: 0.0086 - accuracy: 0.6473\n",
            "Epoch 132/200\n",
            "1/1 [==============================] - 0s 208ms/step - loss: 0.0101 - accuracy: 0.3204\n",
            "Epoch 133/200\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0087 - accuracy: 0.6237\n",
            "Epoch 134/200\n",
            "1/1 [==============================] - 0s 213ms/step - loss: 0.0099 - accuracy: 0.3557\n",
            "Epoch 135/200\n",
            "1/1 [==============================] - 0s 206ms/step - loss: 0.0081 - accuracy: 0.6268\n",
            "Epoch 136/200\n",
            "1/1 [==============================] - 0s 219ms/step - loss: 0.0096 - accuracy: 0.3661\n",
            "Epoch 137/200\n",
            "1/1 [==============================] - 0s 222ms/step - loss: 0.0088 - accuracy: 0.5974\n",
            "Epoch 138/200\n",
            "1/1 [==============================] - 0s 221ms/step - loss: 0.0103 - accuracy: 0.3768\n",
            "Epoch 139/200\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0084 - accuracy: 0.6749\n",
            "Epoch 140/200\n",
            "1/1 [==============================] - 0s 237ms/step - loss: 0.0096 - accuracy: 0.2795\n",
            "Epoch 141/200\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0081 - accuracy: 0.6416\n",
            "Epoch 142/200\n",
            "1/1 [==============================] - 0s 214ms/step - loss: 0.0093 - accuracy: 0.3422\n",
            "Epoch 143/200\n",
            "1/1 [==============================] - 0s 197ms/step - loss: 0.0083 - accuracy: 0.5847\n",
            "Epoch 144/200\n",
            "1/1 [==============================] - 0s 206ms/step - loss: 0.0095 - accuracy: 0.4907\n",
            "Epoch 145/200\n",
            "1/1 [==============================] - 0s 228ms/step - loss: 0.0081 - accuracy: 0.5698\n",
            "Epoch 146/200\n",
            "1/1 [==============================] - 0s 192ms/step - loss: 0.0094 - accuracy: 0.4480\n",
            "Epoch 147/200\n",
            "1/1 [==============================] - 0s 206ms/step - loss: 0.0083 - accuracy: 0.6193\n",
            "Epoch 148/200\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0096 - accuracy: 0.3243\n",
            "Epoch 149/200\n",
            "1/1 [==============================] - 0s 217ms/step - loss: 0.0081 - accuracy: 0.6673\n",
            "Epoch 150/200\n",
            "1/1 [==============================] - 0s 217ms/step - loss: 0.0094 - accuracy: 0.2542\n",
            "Epoch 151/200\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.0082 - accuracy: 0.6960\n",
            "Epoch 152/200\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0095 - accuracy: 0.2887\n",
            "Epoch 153/200\n",
            "1/1 [==============================] - 0s 194ms/step - loss: 0.0081 - accuracy: 0.6012\n",
            "Epoch 154/200\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0098 - accuracy: 0.5560\n",
            "Epoch 155/200\n",
            "1/1 [==============================] - 0s 234ms/step - loss: 0.0085 - accuracy: 0.3878\n",
            "Epoch 156/200\n",
            "1/1 [==============================] - 0s 219ms/step - loss: 0.0100 - accuracy: 0.5752\n",
            "Epoch 157/200\n",
            "1/1 [==============================] - 0s 203ms/step - loss: 0.0081 - accuracy: 0.6462\n",
            "Epoch 158/200\n",
            "1/1 [==============================] - 0s 206ms/step - loss: 0.0091 - accuracy: 0.2834\n",
            "Epoch 159/200\n",
            "1/1 [==============================] - 0s 226ms/step - loss: 0.0077 - accuracy: 0.6856\n",
            "Epoch 160/200\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.0088 - accuracy: 0.2732\n",
            "Epoch 161/200\n",
            "1/1 [==============================] - 0s 208ms/step - loss: 0.0079 - accuracy: 0.6544\n",
            "Epoch 162/200\n",
            "1/1 [==============================] - 0s 222ms/step - loss: 0.0091 - accuracy: 0.3603\n",
            "Epoch 163/200\n",
            "1/1 [==============================] - 0s 223ms/step - loss: 0.0080 - accuracy: 0.5697\n",
            "Epoch 164/200\n",
            "1/1 [==============================] - 0s 220ms/step - loss: 0.0094 - accuracy: 0.5546\n",
            "Epoch 165/200\n",
            "1/1 [==============================] - 0s 210ms/step - loss: 0.0082 - accuracy: 0.4930\n",
            "Epoch 166/200\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.0095 - accuracy: 0.5262\n",
            "Epoch 167/200\n",
            "1/1 [==============================] - 0s 226ms/step - loss: 0.0079 - accuracy: 0.6545\n",
            "Epoch 168/200\n",
            "1/1 [==============================] - 0s 245ms/step - loss: 0.0092 - accuracy: 0.3040\n",
            "Epoch 169/200\n",
            "1/1 [==============================] - 0s 478ms/step - loss: 0.0080 - accuracy: 0.6815\n",
            "Epoch 170/200\n",
            "1/1 [==============================] - 0s 338ms/step - loss: 0.0091 - accuracy: 0.2882\n",
            "Epoch 171/200\n",
            "1/1 [==============================] - 1s 632ms/step - loss: 0.0076 - accuracy: 0.6542\n",
            "Epoch 172/200\n",
            "1/1 [==============================] - 0s 329ms/step - loss: 0.0087 - accuracy: 0.3869\n",
            "Epoch 173/200\n",
            "1/1 [==============================] - 0s 363ms/step - loss: 0.0079 - accuracy: 0.5355\n",
            "Epoch 174/200\n",
            "1/1 [==============================] - 0s 372ms/step - loss: 0.0091 - accuracy: 0.5712\n",
            "Epoch 175/200\n",
            "1/1 [==============================] - 0s 315ms/step - loss: 0.0078 - accuracy: 0.5307\n",
            "Epoch 176/200\n",
            "1/1 [==============================] - 0s 337ms/step - loss: 0.0090 - accuracy: 0.4327\n",
            "Epoch 177/200\n",
            "1/1 [==============================] - 0s 357ms/step - loss: 0.0080 - accuracy: 0.6916\n",
            "Epoch 178/200\n",
            "1/1 [==============================] - 0s 376ms/step - loss: 0.0092 - accuracy: 0.3186\n",
            "Epoch 179/200\n",
            "1/1 [==============================] - 0s 362ms/step - loss: 0.0077 - accuracy: 0.6952\n",
            "Epoch 180/200\n",
            "1/1 [==============================] - 0s 389ms/step - loss: 0.0089 - accuracy: 0.3363\n",
            "Epoch 181/200\n",
            "1/1 [==============================] - 0s 360ms/step - loss: 0.0079 - accuracy: 0.6080\n",
            "Epoch 182/200\n",
            "1/1 [==============================] - 0s 388ms/step - loss: 0.0090 - accuracy: 0.4458\n",
            "Epoch 183/200\n",
            "1/1 [==============================] - 0s 332ms/step - loss: 0.0075 - accuracy: 0.5158\n",
            "Epoch 184/200\n",
            "1/1 [==============================] - 0s 324ms/step - loss: 0.0087 - accuracy: 0.4535\n",
            "Epoch 185/200\n",
            "1/1 [==============================] - 0s 244ms/step - loss: 0.0078 - accuracy: 0.5415\n",
            "Epoch 186/200\n",
            "1/1 [==============================] - 0s 215ms/step - loss: 0.0092 - accuracy: 0.4006\n",
            "Epoch 187/200\n",
            "1/1 [==============================] - 0s 218ms/step - loss: 0.0079 - accuracy: 0.6834\n",
            "Epoch 188/200\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0092 - accuracy: 0.2955\n",
            "Epoch 189/200\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.0078 - accuracy: 0.7393\n",
            "Epoch 190/200\n",
            "1/1 [==============================] - 0s 218ms/step - loss: 0.0090 - accuracy: 0.3526\n",
            "Epoch 191/200\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.0078 - accuracy: 0.4850\n",
            "Epoch 192/200\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.0088 - accuracy: 0.5440\n",
            "Epoch 193/200\n",
            "1/1 [==============================] - 0s 210ms/step - loss: 0.0073 - accuracy: 0.4243\n",
            "Epoch 194/200\n",
            "1/1 [==============================] - 0s 207ms/step - loss: 0.0083 - accuracy: 0.5088\n",
            "Epoch 195/200\n",
            "1/1 [==============================] - 0s 219ms/step - loss: 0.0076 - accuracy: 0.5595\n",
            "Epoch 196/200\n",
            "1/1 [==============================] - 0s 210ms/step - loss: 0.0088 - accuracy: 0.3457\n",
            "Epoch 197/200\n",
            "1/1 [==============================] - 0s 204ms/step - loss: 0.0076 - accuracy: 0.7278\n",
            "Epoch 198/200\n",
            "1/1 [==============================] - 0s 206ms/step - loss: 0.0089 - accuracy: 0.3353\n",
            "Epoch 199/200\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.0078 - accuracy: 0.5808\n",
            "Epoch 200/200\n",
            "1/1 [==============================] - 0s 218ms/step - loss: 0.0089 - accuracy: 0.4433\n",
            "Neural network output\n",
            "1/1 [==============================] - 0s 211ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7b30e821dd80>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "(x_train, _), (x_test, _) = mnist.load_data()\n",
        "\n",
        "x_train = x_train.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))\n",
        "x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))\n",
        "\n",
        "#adding some noise\n",
        "noise_factor = 0.5\n",
        "x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)\n",
        "x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)\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",
        "#Displaying images with noise\n",
        "plt.figure(figsize=(20, 2))\n",
        "for i in range(1,10):\n",
        "    ax = plt.subplot(1, 10, i)\n",
        "    plt.imshow(x_test_noisy[i].reshape(28, 28), cmap=\"binary\")\n",
        "plt.show()\n",
        "\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(28, 28, 1)))\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",
        "\n",
        "\n",
        "model.add(MaxPooling2D((2, 2), padding='same'))\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(32, (3, 3), activation='relu'))\n",
        "model.add(UpSampling2D((2, 2)))\n",
        "model.add(Conv2D(1, (3, 3), activation='relu', padding='same'))\n",
        "\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.fit(x_train_noisy, x_train, epochs=10, batch_size=256, shuffle=True,\n",
        "          validation_data=(x_test_noisy, x_test))\n",
        "\n",
        "\n",
        "\n",
        "model.evaluate(x_test_noisy, x_test)\n",
        "\n",
        "model.save('denoising_autoencoder.model')\n",
        "\n",
        "no_noise_img = model.predict(x_test_noisy)\n",
        "\n",
        "plt.figure(figsize=(40, 4))\n",
        "for i in range(10):\n",
        "    # display original\n",
        "    ax = plt.subplot(3, 20, i + 1)\n",
        "    plt.imshow(x_test_noisy[i].reshape(28, 28), cmap=\"binary\")\n",
        "\n",
        "    # display reconstructed (after noise removed) image\n",
        "    ax = plt.subplot(3, 20, 40 +i+ 1)\n",
        "    plt.imshow(no_noise_img[i].reshape(28, 28), cmap=\"binary\")"
      ],
      "metadata": {
        "id": "YD_zZXXdVR1F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "19bbbc43-d82f-4e0c-98c5-e1b13185cf36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x200 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_7 (Conv2D)           (None, 28, 28, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 14, 14, 32)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 14, 14, 8)         2312      \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPoolin  (None, 7, 7, 8)           0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 7, 7, 8)           584       \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPoolin  (None, 4, 4, 8)           0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_10 (Conv2D)          (None, 4, 4, 8)           584       \n",
            "                                                                 \n",
            " up_sampling2d_3 (UpSamplin  (None, 8, 8, 8)           0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 8, 8, 8)           584       \n",
            "                                                                 \n",
            " up_sampling2d_4 (UpSamplin  (None, 16, 16, 8)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_12 (Conv2D)          (None, 14, 14, 32)        2336      \n",
            "                                                                 \n",
            " up_sampling2d_5 (UpSamplin  (None, 28, 28, 32)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_13 (Conv2D)          (None, 28, 28, 1)         289       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7009 (27.38 KB)\n",
            "Trainable params: 7009 (27.38 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "Epoch 1/10\n",
            "235/235 [==============================] - 103s 430ms/step - loss: 0.0556 - val_loss: 0.0426\n",
            "Epoch 2/10\n",
            "235/235 [==============================] - 100s 428ms/step - loss: 0.0380 - val_loss: 0.0341\n",
            "Epoch 3/10\n",
            "235/235 [==============================] - 96s 408ms/step - loss: 0.0326 - val_loss: 0.0306\n",
            "Epoch 4/10\n",
            "235/235 [==============================] - 96s 410ms/step - loss: 0.0299 - val_loss: 0.0284\n",
            "Epoch 5/10\n",
            "235/235 [==============================] - 96s 410ms/step - loss: 0.0279 - val_loss: 0.0271\n",
            "Epoch 6/10\n",
            "235/235 [==============================] - 96s 408ms/step - loss: 0.0267 - val_loss: 0.0259\n",
            "Epoch 7/10\n",
            "235/235 [==============================] - 96s 408ms/step - loss: 0.0257 - val_loss: 0.0250\n",
            "Epoch 8/10\n",
            "235/235 [==============================] - 96s 409ms/step - loss: 0.0250 - val_loss: 0.0244\n",
            "Epoch 9/10\n",
            "235/235 [==============================] - 96s 408ms/step - loss: 0.0244 - val_loss: 0.0238\n",
            "Epoch 10/10\n",
            "235/235 [==============================] - 96s 410ms/step - loss: 0.0239 - val_loss: 0.0235\n",
            "313/313 [==============================] - 4s 13ms/step - loss: 0.0235\n",
            "313/313 [==============================] - 4s 12ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 4000x400 with 20 Axes>"
            ],
            "image/png": 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