DenoisingMNIST(CNN+AE).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c45b477-1126-4864-bfa2-9167901f90dd",
   "metadata": {},
   "source": [
    "# Denoising Handwritten Digits\n",
    "\n",
    "This project primarily aims to showcase the practical use of Autoencoders for denoising purposes. Our approach involves introducing Gaussian noise into the MNIST (Modified National Institute of Standards and Technology) dataset and subsequently attempting to remove this noise through the application of various filters such as Gaussian Filter, Bilateral Filter, and Median Filter. However, our main emphasis will be on exploring the effectiveness of **Autoencoders and CNN** in the context of denoising tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b25d6e1b-cad3-4f40-ae9c-869569788644",
   "metadata": {},
   "source": [
    "### Importing Essential Tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3898ecd-d008-40e5-9716-cf07fb4caa53",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "234917e0-0907-4134-a483-5cf7e6dcdbf5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee4a6c68-5a4c-4bec-bf99-338d501d0314",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Model\n",
    "from keras.layers import Input\n",
    "from keras.layers import Conv2D, MaxPool2D\n",
    "from keras.layers import Conv2DTranspose, UpSampling2D\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e2382c4-02bd-4c53-9d78-5b5824cac1a2",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "33fe0d6f-14d7-4b43-84bc-507565da9bc4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def normalise(img_arr):\n",
    "    \"\"\"\n",
    "    Rescale pixels values to [0,1]\n",
    "    \"\"\"\n",
    "    img_arr = img_arr.astype(\"float32\")/255\n",
    "    return img_arr\n",
    "\n",
    "def add_gnoise(img_arr,factor=0.3):\n",
    "    \"\"\"\n",
    "    Add Gaussian Noise to the array.\n",
    "    \"\"\"\n",
    "    noise = factor * np.random.normal(loc=0.0, scale=1.0, size=img_arr.shape)\n",
    "    img_arr = img_arr + noise\n",
    "    img_arr = np.clip(img_arr,0,1)\n",
    "    return img_arr\n",
    "\n",
    "def display(orig_arr,noisy_arr,enc_arr,dec_arr):\n",
    "    \"\"\"\n",
    "    Display 5 images (28 x 28) from each of the provided arrays. \n",
    "    \"\"\"\n",
    "    rows = 4\n",
    "    cols = 5\n",
    "    fig, axis = plt.subplots(nrows= rows,ncols= cols, figsize=(5,5))\n",
    "    \n",
    "    # Original Representations\n",
    "    for col in range(cols):\n",
    "        plt.gray()\n",
    "        axis[0,col].set_yticks([])\n",
    "        axis[0,col].set_xticks([])\n",
    "        axis[0,col].imshow(orig_arr[col].reshape(28,28))\n",
    "    \n",
    "    # Noisy Representations\n",
    "    for col in range(cols):\n",
    "        plt.gray()\n",
    "        axis[1,col].set_yticks([])\n",
    "        axis[1,col].set_xticks([])\n",
    "        axis[1,col].imshow(noisy_arr[col].reshape(28,28))\n",
    "    \n",
    "    # Encoded Representations\n",
    "    for col in range(cols):\n",
    "        plt.gray()\n",
    "        axis[2,col].set_yticks([])\n",
    "        axis[2,col].set_xticks([])\n",
    "        axis[2,col].imshow(enc_arr[col].reshape(14,28))   # 392 dimensional encoded representation\n",
    "        \n",
    "    # Decoded Representations\n",
    "    for col in range(cols):\n",
    "        plt.gray()\n",
    "        axis[3,col].set_yticks([])\n",
    "        axis[3,col].set_xticks([])\n",
    "        axis[3,col].imshow(dec_arr[col].reshape(28,28))\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b52304f4-1a8b-4b05-89bf-a05b19a3bbfa",
   "metadata": {},
   "source": [
    "### Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed761f51-6090-4566-a0c4-0e8a009f71e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "((X_train,y_train),(X_test,y_test)) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1dfbe17a-1a07-4800-87a2-9625569bf0c1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (60000,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c37475f6-e71d-4f1f-a82b-3eb3b7da15a1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000, 28, 28), (10000,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80a4d4ef-f46b-4b4e-921e-3e3fa9a0788b",
   "metadata": {},
   "source": [
    "There are 70,000 images of handwritten digits in the MNIST dataset. Of these 70,000, 60,000 are being used for training and rest 10,000 for testing. Each image is a black and white image with pixel values ranging [0,255]. Each image is 28 x 28 pixels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcfd23c-1c5d-4718-a08b-0fe561240ecf",
   "metadata": {},
   "source": [
    "### Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22e5e27a-6e7a-4529-9cca-866426d27812",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows = 3\n",
    "cols = 3\n",
    "\n",
    "plt.gray()\n",
    "fig, axis = plt.subplots(nrows=rows,ncols=cols,figsize=(5,5))\n",
    "count = 99\n",
    "for row in range(rows):\n",
    "    for col in range(cols):\n",
    "        axis[row][col].set_title(y_train[count])\n",
    "        axis[row][col].set_xticks([])\n",
    "        axis[row][col].set_yticks([])\n",
    "        axis[row][col].imshow(X_train[count])\n",
    "        count = count + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53e46b44-7a08-47cc-97ff-79cd954c1221",
   "metadata": {},
   "source": [
    "### Modelling Convolutional Auto Encoder , Encoder and Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c770211e-69c8-4dec-bec3-69ce54532af4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "INPUT = Input(shape=(28,28,1))  # 28,28,1 = 784 dim\n",
    "\n",
    "layer1 = Conv2D(filters=32,kernel_size=(3,3),strides=(1, 1),padding='same',activation=\"relu\")(INPUT)    # 28,28,32\n",
    "layer2 = MaxPool2D(pool_size=(2, 2),padding='same')(layer1)                               # 14,14,32\n",
    "layer3 = Conv2D(filters=16,kernel_size=(3,3),strides=(1, 1),padding='same',activation=\"relu\")(layer2)   # 14,14,16\n",
    "layer4 = MaxPool2D(pool_size=(2, 2),padding='same')(layer3)                               #  7, 7,16\n",
    "\n",
    "layer5 = Conv2D(filters=8,kernel_size=(3,3),strides=(1, 1),padding='same',activation=\"relu\")(layer4)    #  7, 7, 8  (bottleneck) = 392 dim\n",
    "\n",
    "layer6 = Conv2DTranspose(filters=16,kernel_size=(3,3),strides=(1, 1),padding='same',activation=\"relu\")(layer5)   #  7, 7, 16\n",
    "layer7 = UpSampling2D(size=(2, 2))(layer6)                                                                       #  14,14,16\n",
    "layer8 = Conv2DTranspose(filters=32,kernel_size=(3,3),strides=(1, 1),padding='same',activation=\"relu\")(layer7)   #  14,14,32\n",
    "layer9 = UpSampling2D(size=(2, 2))(layer8)                                                                       #  28,28,32\n",
    "\n",
    "layer10 = Conv2D(filters=1,kernel_size=(3,3),strides=(1, 1),padding='same',activation=\"sigmoid\")(layer9)  #  28,28,1 = 784 dim\n",
    "# Sigmoid, because I will scale the pixel values to [0,1]\n",
    "\n",
    "# Stacked - Auto Encoder + CNN\n",
    "autoencoder = Model(INPUT,layer10)\n",
    "\n",
    "# Encoder\n",
    "encoder = Model(INPUT,layer5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b69766a-bce5-458b-ba08-7873a6ba963c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 28, 28, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 14, 14, 32)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 14, 14, 16)        4624      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 7, 7, 16)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 7, 7, 8)           1160      \n",
      "                                                                 \n",
      " conv2d_transpose (Conv2DTr  (None, 7, 7, 16)          1168      \n",
      " anspose)                                                        \n",
      "                                                                 \n",
      " up_sampling2d (UpSampling2  (None, 14, 14, 16)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_transpose_1 (Conv2D  (None, 14, 14, 32)        4640      \n",
      " Transpose)                                                      \n",
      "                                                                 \n",
      " up_sampling2d_1 (UpSamplin  (None, 28, 28, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 28, 28, 1)         289       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 12201 (47.66 KB)\n",
      "Trainable params: 12201 (47.66 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c5b1e9-e199-4a87-b803-03d00e6de25c",
   "metadata": {},
   "source": [
    "##### Input Output Dimensions Must Match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ecc1438-77a1-4d1d-8c91-16642e6e3a80",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 28, 28, 32)        320       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 14, 14, 32)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 14, 14, 16)        4624      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 7, 7, 16)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 7, 7, 8)           1160      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 6104 (23.84 KB)\n",
      "Trainable params: 6104 (23.84 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0fd9f7-33a3-403a-8bd2-4a7a3c68cf3c",
   "metadata": {},
   "source": [
    "### Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "03bfe7bf-9c23-4191-841e-e6c415f14da9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "NOISE_FACTOR = 0.5\n",
    "\n",
    "pX_train = normalise(X_train)  # normalise pixels\n",
    "pX_test  = normalise(X_test)   # normalise pixels\n",
    "\n",
    "noisy_pX_train = add_gnoise(pX_train)  # noisy processed X_train\n",
    "noisy_pX_test  = add_gnoise(pX_test)   # noisy processed X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "301f101a-5c4e-4d4d-917a-d1c468111850",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (10000, 28, 28))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bc42d5b2-e0e5-4860-9f34-a55e8e1f2d8c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (10000, 28, 28))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pX_train.shape,pX_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ffa6c53e-f20c-4221-952e-e6170a05c329",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (10000, 28, 28))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisy_pX_train.shape,noisy_pX_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e92ad41-e448-49c8-bf75-7bc9d12c9b39",
   "metadata": {},
   "source": [
    "### Compiling and Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "51690f87-3b85-4845-867d-2709bc904925",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "704/704 [==============================] - 16s 22ms/step - loss: 0.1422 - mse: 0.0261 - val_loss: 0.0953 - val_mse: 0.0107\n",
      "Epoch 2/300\n",
      "704/704 [==============================] - 18s 26ms/step - loss: 0.0907 - mse: 0.0094 - val_loss: 0.0887 - val_mse: 0.0086\n",
      "Epoch 3/300\n",
      "704/704 [==============================] - 19s 26ms/step - loss: 0.0863 - mse: 0.0080 - val_loss: 0.0860 - val_mse: 0.0078\n",
      "Epoch 4/300\n",
      "704/704 [==============================] - 18s 26ms/step - loss: 0.0843 - mse: 0.0074 - val_loss: 0.0845 - val_mse: 0.0073\n",
      "Epoch 5/300\n",
      "704/704 [==============================] - 18s 26ms/step - loss: 0.0833 - mse: 0.0070 - val_loss: 0.0837 - val_mse: 0.0071\n",
      "Epoch 6/300\n",
      "704/704 [==============================] - 18s 26ms/step - loss: 0.0825 - mse: 0.0068 - val_loss: 0.0830 - val_mse: 0.0068\n",
      "Epoch 7/300\n",
      "704/704 [==============================] - 19s 27ms/step - loss: 0.0820 - mse: 0.0066 - val_loss: 0.0825 - val_mse: 0.0066\n",
      "Epoch 8/300\n",
      "704/704 [==============================] - 19s 26ms/step - loss: 0.0815 - mse: 0.0065 - val_loss: 0.0822 - val_mse: 0.0065\n",
      "Epoch 9/300\n",
      "704/704 [==============================] - 19s 27ms/step - loss: 0.0812 - mse: 0.0064 - val_loss: 0.0822 - val_mse: 0.0066\n",
      "Epoch 10/300\n",
      "704/704 [==============================] - 21s 29ms/step - loss: 0.0809 - mse: 0.0063 - val_loss: 0.0821 - val_mse: 0.0066\n",
      "Epoch 11/300\n",
      "704/704 [==============================] - 22s 32ms/step - loss: 0.0807 - mse: 0.0062 - val_loss: 0.0814 - val_mse: 0.0063\n",
      "Epoch 12/300\n",
      "704/704 [==============================] - 23s 33ms/step - loss: 0.0805 - mse: 0.0061 - val_loss: 0.0811 - val_mse: 0.0062\n",
      "Epoch 13/300\n",
      "704/704 [==============================] - 23s 32ms/step - loss: 0.0802 - mse: 0.0061 - val_loss: 0.0819 - val_mse: 0.0064\n",
      "Epoch 14/300\n",
      "704/704 [==============================] - 22s 32ms/step - loss: 0.0801 - mse: 0.0060 - val_loss: 0.0811 - val_mse: 0.0062\n",
      "Epoch 15/300\n",
      "704/704 [==============================] - 22s 31ms/step - loss: 0.0799 - mse: 0.0060 - val_loss: 0.0807 - val_mse: 0.0061\n",
      "Epoch 16/300\n",
      "704/704 [==============================] - 22s 31ms/step - loss: 0.0798 - mse: 0.0059 - val_loss: 0.0813 - val_mse: 0.0063\n",
      "Epoch 17/300\n",
      "704/704 [==============================] - 22s 31ms/step - loss: 0.0797 - mse: 0.0059 - val_loss: 0.0805 - val_mse: 0.0060\n",
      "Epoch 18/300\n",
      "704/704 [==============================] - 22s 31ms/step - loss: 0.0795 - mse: 0.0059 - val_loss: 0.0805 - val_mse: 0.0060\n",
      "Epoch 19/300\n",
      "704/704 [==============================] - 22s 31ms/step - loss: 0.0794 - mse: 0.0058 - val_loss: 0.0802 - val_mse: 0.0059\n",
      "Epoch 20/300\n",
      "704/704 [==============================] - 22s 31ms/step - loss: 0.0794 - mse: 0.0058 - val_loss: 0.0806 - val_mse: 0.0060\n",
      "Epoch 21/300\n",
      "704/704 [==============================] - 22s 31ms/step - loss: 0.0793 - mse: 0.0058 - val_loss: 0.0803 - val_mse: 0.0059\n",
      "Epoch 22/300\n",
      "704/704 [==============================] - 23s 32ms/step - loss: 0.0792 - mse: 0.0057 - val_loss: 0.0803 - val_mse: 0.0059\n",
      "Epoch 23/300\n",
      "704/704 [==============================] - 23s 32ms/step - loss: 0.0791 - mse: 0.0057 - val_loss: 0.0800 - val_mse: 0.0058\n",
      "Epoch 24/300\n",
      "704/704 [==============================] - 22s 32ms/step - loss: 0.0790 - mse: 0.0057 - val_loss: 0.0799 - val_mse: 0.0058\n",
      "Epoch 25/300\n",
      "704/704 [==============================] - 23s 32ms/step - loss: 0.0790 - mse: 0.0057 - val_loss: 0.0799 - val_mse: 0.0058\n",
      "Epoch 26/300\n",
      "704/704 [==============================] - 23s 33ms/step - loss: 0.0790 - mse: 0.0057 - val_loss: 0.0798 - val_mse: 0.0058\n",
      "Epoch 27/300\n",
      "704/704 [==============================] - 24s 34ms/step - loss: 0.0788 - mse: 0.0056 - val_loss: 0.0802 - val_mse: 0.0059\n",
      "Epoch 28/300\n",
      "704/704 [==============================] - 23s 33ms/step - loss: 0.0788 - mse: 0.0056 - val_loss: 0.0797 - val_mse: 0.0057\n",
      "Epoch 29/300\n",
      "704/704 [==============================] - 24s 34ms/step - loss: 0.0788 - mse: 0.0056 - val_loss: 0.0803 - val_mse: 0.0059\n",
      "Epoch 30/300\n",
      "704/704 [==============================] - 24s 34ms/step - loss: 0.0787 - mse: 0.0056 - val_loss: 0.0797 - val_mse: 0.0057\n",
      "Epoch 31/300\n",
      "704/704 [==============================] - 24s 34ms/step - loss: 0.0787 - mse: 0.0056 - val_loss: 0.0803 - val_mse: 0.0059\n",
      "Epoch 32/300\n",
      "704/704 [==============================] - 24s 33ms/step - loss: 0.0786 - mse: 0.0056 - val_loss: 0.0795 - val_mse: 0.0057\n",
      "Epoch 33/300\n",
      "704/704 [==============================] - 24s 34ms/step - loss: 0.0786 - mse: 0.0056 - val_loss: 0.0799 - val_mse: 0.0058\n",
      "Epoch 34/300\n",
      "704/704 [==============================] - 23s 33ms/step - loss: 0.0786 - mse: 0.0056 - val_loss: 0.0796 - val_mse: 0.0057\n",
      "Epoch 35/300\n",
      "704/704 [==============================] - 24s 34ms/step - loss: 0.0785 - mse: 0.0055 - val_loss: 0.0794 - val_mse: 0.0056\n",
      "Epoch 36/300\n",
      "704/704 [==============================] - 24s 34ms/step - loss: 0.0785 - mse: 0.0055 - val_loss: 0.0793 - val_mse: 0.0056\n",
      "Epoch 37/300\n",
      "704/704 [==============================] - 24s 34ms/step - loss: 0.0785 - mse: 0.0055 - val_loss: 0.0796 - val_mse: 0.0057\n",
      "Epoch 38/300\n",
      "704/704 [==============================] - 305s 433ms/step - loss: 0.0784 - mse: 0.0055 - val_loss: 0.0794 - val_mse: 0.0056\n",
      "Epoch 39/300\n",
      "704/704 [==============================] - 18s 26ms/step - loss: 0.0784 - mse: 0.0055 - val_loss: 0.0796 - val_mse: 0.0057\n",
      "Epoch 40/300\n",
      "704/704 [==============================] - 123s 175ms/step - loss: 0.0784 - mse: 0.0055 - val_loss: 0.0794 - val_mse: 0.0057\n",
      "Epoch 41/300\n",
      "704/704 [==============================] - 19s 27ms/step - loss: 0.0783 - mse: 0.0055 - val_loss: 0.0792 - val_mse: 0.0056\n",
      "Epoch 42/300\n",
      "704/704 [==============================] - 19s 27ms/step - loss: 0.0783 - mse: 0.0055 - val_loss: 0.0799 - val_mse: 0.0058\n",
      "Epoch 43/300\n",
      "704/704 [==============================] - 19s 27ms/step - loss: 0.0783 - mse: 0.0055 - val_loss: 0.0792 - val_mse: 0.0056\n",
      "Epoch 44/300\n",
      "704/704 [==============================] - 19s 26ms/step - loss: 0.0782 - mse: 0.0054 - val_loss: 0.0792 - val_mse: 0.0056\n",
      "Epoch 45/300\n",
      "704/704 [==============================] - 19s 28ms/step - loss: 0.0782 - mse: 0.0054 - val_loss: 0.0792 - val_mse: 0.0056\n",
      "Epoch 46/300\n",
      "704/704 [==============================] - 19s 27ms/step - loss: 0.0782 - mse: 0.0054 - val_loss: 0.0790 - val_mse: 0.0055\n",
      "Epoch 47/300\n",
      "704/704 [==============================] - 19s 26ms/step - loss: 0.0781 - mse: 0.0054 - val_loss: 0.0793 - val_mse: 0.0056\n",
      "Epoch 48/300\n",
      "704/704 [==============================] - 19s 27ms/step - loss: 0.0781 - mse: 0.0054 - val_loss: 0.0790 - val_mse: 0.0055\n",
      "Epoch 49/300\n",
      "704/704 [==============================] - 19s 26ms/step - loss: 0.0781 - mse: 0.0054 - val_loss: 0.0793 - val_mse: 0.0056\n",
      "Epoch 50/300\n",
      "704/704 [==============================] - 18s 26ms/step - loss: 0.0780 - mse: 0.0054 - val_loss: 0.0796 - val_mse: 0.0057\n",
      "Epoch 51/300\n",
      "704/704 [==============================] - 16s 22ms/step - loss: 0.0780 - mse: 0.0054 - val_loss: 0.0790 - val_mse: 0.0055\n",
      "Epoch 52/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0780 - mse: 0.0054 - val_loss: 0.0793 - val_mse: 0.0056\n",
      "Epoch 53/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0780 - mse: 0.0054 - val_loss: 0.0789 - val_mse: 0.0055\n",
      "Epoch 54/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0780 - mse: 0.0054 - val_loss: 0.0790 - val_mse: 0.0055\n",
      "Epoch 55/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0779 - mse: 0.0054 - val_loss: 0.0788 - val_mse: 0.0055\n",
      "Epoch 56/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0779 - mse: 0.0053 - val_loss: 0.0791 - val_mse: 0.0055\n",
      "Epoch 57/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0779 - mse: 0.0053 - val_loss: 0.0789 - val_mse: 0.0055\n",
      "Epoch 58/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0779 - mse: 0.0053 - val_loss: 0.0788 - val_mse: 0.0055\n",
      "Epoch 59/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0779 - mse: 0.0053 - val_loss: 0.0788 - val_mse: 0.0055\n",
      "Epoch 60/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0778 - mse: 0.0053 - val_loss: 0.0788 - val_mse: 0.0055\n",
      "Epoch 61/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0778 - mse: 0.0053 - val_loss: 0.0791 - val_mse: 0.0056\n",
      "Epoch 62/300\n",
      "704/704 [==============================] - 17s 23ms/step - loss: 0.0778 - mse: 0.0053 - val_loss: 0.0789 - val_mse: 0.0055\n",
      "Epoch 63/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0778 - mse: 0.0053 - val_loss: 0.0790 - val_mse: 0.0056\n",
      "Epoch 64/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0777 - mse: 0.0053 - val_loss: 0.0787 - val_mse: 0.0054\n",
      "Epoch 65/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0778 - mse: 0.0053 - val_loss: 0.0790 - val_mse: 0.0055\n",
      "Epoch 66/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0777 - mse: 0.0053 - val_loss: 0.0786 - val_mse: 0.0054\n",
      "Epoch 67/300\n",
      "704/704 [==============================] - 17s 25ms/step - loss: 0.0777 - mse: 0.0053 - val_loss: 0.0790 - val_mse: 0.0055\n",
      "Epoch 68/300\n",
      "704/704 [==============================] - 17s 25ms/step - loss: 0.0777 - mse: 0.0053 - val_loss: 0.0790 - val_mse: 0.0055\n",
      "Epoch 69/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0777 - mse: 0.0053 - val_loss: 0.0793 - val_mse: 0.0057\n",
      "Epoch 70/300\n",
      "704/704 [==============================] - 17s 25ms/step - loss: 0.0777 - mse: 0.0053 - val_loss: 0.0786 - val_mse: 0.0054\n",
      "Epoch 71/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0777 - mse: 0.0053 - val_loss: 0.0788 - val_mse: 0.0055\n",
      "Epoch 72/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0777 - mse: 0.0053 - val_loss: 0.0788 - val_mse: 0.0054\n",
      "Epoch 73/300\n",
      "704/704 [==============================] - 18s 26ms/step - loss: 0.0777 - mse: 0.0053 - val_loss: 0.0800 - val_mse: 0.0059\n",
      "Epoch 74/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0777 - mse: 0.0053 - val_loss: 0.0787 - val_mse: 0.0054\n",
      "Epoch 75/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0776 - mse: 0.0053 - val_loss: 0.0786 - val_mse: 0.0054\n",
      "Epoch 76/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0776 - mse: 0.0053 - val_loss: 0.0786 - val_mse: 0.0054\n",
      "Epoch 77/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0776 - mse: 0.0053 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 78/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0776 - mse: 0.0053 - val_loss: 0.0786 - val_mse: 0.0054\n",
      "Epoch 79/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0776 - mse: 0.0053 - val_loss: 0.0800 - val_mse: 0.0059\n",
      "Epoch 80/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0776 - mse: 0.0053 - val_loss: 0.0788 - val_mse: 0.0055\n",
      "Epoch 81/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0776 - mse: 0.0052 - val_loss: 0.0786 - val_mse: 0.0054\n",
      "Epoch 82/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0776 - mse: 0.0053 - val_loss: 0.0794 - val_mse: 0.0056\n",
      "Epoch 83/300\n",
      "704/704 [==============================] - 18s 26ms/step - loss: 0.0776 - mse: 0.0053 - val_loss: 0.0784 - val_mse: 0.0053\n",
      "Epoch 84/300\n",
      "704/704 [==============================] - 301s 428ms/step - loss: 0.0776 - mse: 0.0053 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 85/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0789 - val_mse: 0.0055\n",
      "Epoch 86/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0788 - val_mse: 0.0054\n",
      "Epoch 87/300\n",
      "704/704 [==============================] - 23s 33ms/step - loss: 0.0776 - mse: 0.0053 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 88/300\n",
      "704/704 [==============================] - 15s 22ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0784 - val_mse: 0.0054\n",
      "Epoch 89/300\n",
      "704/704 [==============================] - 19s 28ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 90/300\n",
      "704/704 [==============================] - 309s 439ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 91/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0796 - val_mse: 0.0057\n",
      "Epoch 92/300\n",
      "704/704 [==============================] - 14s 20ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0792 - val_mse: 0.0056\n",
      "Epoch 93/300\n",
      "704/704 [==============================] - 154s 219ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0784 - val_mse: 0.0053\n",
      "Epoch 94/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0786 - val_mse: 0.0054\n",
      "Epoch 95/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0784 - val_mse: 0.0053\n",
      "Epoch 96/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0786 - val_mse: 0.0054\n",
      "Epoch 97/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 98/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0789 - val_mse: 0.0055\n",
      "Epoch 99/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0786 - val_mse: 0.0054\n",
      "Epoch 100/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0788 - val_mse: 0.0055\n",
      "Epoch 101/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0775 - mse: 0.0052 - val_loss: 0.0784 - val_mse: 0.0053\n",
      "Epoch 102/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 103/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0784 - val_mse: 0.0053\n",
      "Epoch 104/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0784 - val_mse: 0.0053\n",
      "Epoch 105/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 106/300\n",
      "704/704 [==============================] - 15s 22ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0786 - val_mse: 0.0054\n",
      "Epoch 107/300\n",
      "704/704 [==============================] - 15s 22ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0793 - val_mse: 0.0057\n",
      "Epoch 108/300\n",
      "704/704 [==============================] - 15s 22ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 109/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0784 - val_mse: 0.0054\n",
      "Epoch 110/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0783 - val_mse: 0.0053\n",
      "Epoch 111/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0784 - val_mse: 0.0053\n",
      "Epoch 112/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0786 - val_mse: 0.0054\n",
      "Epoch 113/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 114/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 115/300\n",
      "704/704 [==============================] - 17s 24ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0791 - val_mse: 0.0056\n",
      "Epoch 116/300\n",
      "704/704 [==============================] - 18s 25ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0783 - val_mse: 0.0053\n",
      "Epoch 117/300\n",
      "704/704 [==============================] - 18s 26ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0784 - val_mse: 0.0053\n",
      "Epoch 118/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0784 - val_mse: 0.0053\n",
      "Epoch 119/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 120/300\n",
      "704/704 [==============================] - 16s 22ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0784 - val_mse: 0.0054\n",
      "Epoch 121/300\n",
      "704/704 [==============================] - 15s 21ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0794 - val_mse: 0.0057\n",
      "Epoch 122/300\n",
      "704/704 [==============================] - 15s 22ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0784 - val_mse: 0.0053\n",
      "Epoch 123/300\n",
      "704/704 [==============================] - 15s 22ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0783 - val_mse: 0.0053\n",
      "Epoch 124/300\n",
      "704/704 [==============================] - 16s 22ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 125/300\n",
      "704/704 [==============================] - 16s 22ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0788 - val_mse: 0.0055\n",
      "Epoch 126/300\n",
      "704/704 [==============================] - 16s 22ms/step - loss: 0.0774 - mse: 0.0052 - val_loss: 0.0782 - val_mse: 0.0053\n",
      "Epoch 127/300\n",
      "704/704 [==============================] - 15s 22ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0783 - val_mse: 0.0053\n",
      "Epoch 128/300\n",
      "704/704 [==============================] - 16s 22ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0783 - val_mse: 0.0053\n",
      "Epoch 129/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 130/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0797 - val_mse: 0.0058\n",
      "Epoch 131/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0792 - val_mse: 0.0056\n",
      "Epoch 132/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 133/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0785 - val_mse: 0.0054\n",
      "Epoch 134/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0783 - val_mse: 0.0053\n",
      "Epoch 135/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0782 - val_mse: 0.0053\n",
      "Epoch 136/300\n",
      "704/704 [==============================] - 16s 23ms/step - loss: 0.0773 - mse: 0.0052 - val_loss: 0.0786 - val_mse: 0.0055\n"
     ]
    }
   ],
   "source": [
    "# Compile \n",
    "autoencoder.compile(optimizer=\"adam\",loss=\"binary_crossentropy\",metrics=[\"mse\"])\n",
    "\n",
    "# Callbacks\n",
    "callbacks_list = [EarlyStopping(monitor=\"val_loss\",mode=\"min\",patience=10)]\n",
    "\n",
    "# History and Fit\n",
    "# Model expects (#, 28,28,1)\n",
    "history = autoencoder.fit(noisy_pX_train.reshape(len(X_train),28,28,1),pX_train.reshape(len(X_train),28,28,1),\n",
    "                         batch_size=64,epochs=300,\n",
    "                         validation_split=0.25,\n",
    "                         callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "df4ada0a-e3f6-4de9-b522-4848e3c43bec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>mse</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.142237</td>\n",
       "      <td>0.026139</td>\n",
       "      <td>0.095299</td>\n",
       "      <td>0.010744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.090661</td>\n",
       "      <td>0.009416</td>\n",
       "      <td>0.088750</td>\n",
       "      <td>0.008627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.086344</td>\n",
       "      <td>0.008024</td>\n",
       "      <td>0.085976</td>\n",
       "      <td>0.007756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.084304</td>\n",
       "      <td>0.007370</td>\n",
       "      <td>0.084494</td>\n",
       "      <td>0.007258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.083259</td>\n",
       "      <td>0.007036</td>\n",
       "      <td>0.083732</td>\n",
       "      <td>0.007056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>0.077339</td>\n",
       "      <td>0.005185</td>\n",
       "      <td>0.078501</td>\n",
       "      <td>0.005404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>0.077310</td>\n",
       "      <td>0.005176</td>\n",
       "      <td>0.078545</td>\n",
       "      <td>0.005415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>0.077302</td>\n",
       "      <td>0.005174</td>\n",
       "      <td>0.078273</td>\n",
       "      <td>0.005315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>0.077302</td>\n",
       "      <td>0.005173</td>\n",
       "      <td>0.078244</td>\n",
       "      <td>0.005300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0.077323</td>\n",
       "      <td>0.005181</td>\n",
       "      <td>0.078638</td>\n",
       "      <td>0.005457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss       mse  val_loss   val_mse\n",
       "0    0.142237  0.026139  0.095299  0.010744\n",
       "1    0.090661  0.009416  0.088750  0.008627\n",
       "2    0.086344  0.008024  0.085976  0.007756\n",
       "3    0.084304  0.007370  0.084494  0.007258\n",
       "4    0.083259  0.007036  0.083732  0.007056\n",
       "..        ...       ...       ...       ...\n",
       "131  0.077339  0.005185  0.078501  0.005404\n",
       "132  0.077310  0.005176  0.078545  0.005415\n",
       "133  0.077302  0.005174  0.078273  0.005315\n",
       "134  0.077302  0.005173  0.078244  0.005300\n",
       "135  0.077323  0.005181  0.078638  0.005457\n",
       "\n",
       "[136 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2ff13db0-c63e-4158-9742-1f331862bfb6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12720f990>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots(nrows=1,ncols=2,figsize=(10,5))\n",
    "axis[0].plot(hist[\"loss\"],label=\"Training Loss\")\n",
    "axis[0].plot(hist[\"val_loss\"],label=\"Validation Loss\")\n",
    "axis[0].legend()\n",
    "\n",
    "axis[1].plot(hist[\"mse\"],label=\"Training MSE\")\n",
    "axis[1].plot(hist[\"val_mse\"],label=\"Validation MSE\")\n",
    "axis[1].legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "781362e5-5fe1-47e0-acff-5768a5736822",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Predictions on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eb70c2a2-9f68-4d0a-931f-ebffd7ff305b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = autoencoder.predict(noisy_pX_test.reshape(len(X_test),28,28,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f0db9b-be7e-4291-84b7-22c61889aeec",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Displaying Original, Encoded and Decoded Representations for Training Set\n",
    "\n",
    "\n",
    "* Original Image from MNIST Dataset\n",
    "* Noisy Image\n",
    "* Encoded Representation (32 dimensions)\n",
    "* Decoded Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e18ff017-5efc-4bb9-9e79-7b4fb65c3b03",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 6s 3ms/step\n",
      "1875/1875 [==============================] - 2s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "dec_X_train = autoencoder.predict(noisy_pX_train.reshape(len(X_train),28,28,1)) # decoded representations from autoencoder\n",
    "enc_X_train = encoder.predict(noisy_pX_train.reshape(len(X_train),28,28,1))     # encoded representations from encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "174e4fcf-b711-46f4-b92d-6da0d8422dbf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pX_train,noisy_pX_train,enc_X_train,dec_X_train);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad58bce4-e0af-48eb-b7ef-541a445f2b15",
   "metadata": {},
   "source": [
    "### Displaying Original, Encoded and Decoded Representations for Test Set\n",
    "\n",
    "* Original Image from MNIST Dataset\n",
    "* Noisy Image\n",
    "* Encoded Representation (32 dimensions)\n",
    "* Decoded Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c303c4bd-5a44-444b-917f-16ef49bd6d4b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step\n",
      "313/313 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "dec_X_test = autoencoder.predict(noisy_pX_test.reshape(len(X_test),28,28,1))  # decoded representations from autoencoder\n",
    "enc_X_test = encoder.predict(noisy_pX_test.reshape(len(X_test),28,28,1))      # encoded representations from encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f09b230d-bfc3-467d-95f9-add879647f82",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pX_test,noisy_pX_test,enc_X_test,dec_X_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "58cae02d-844c-4923-891e-2d20f224e2fd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from keras.models import save_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c57315e0-525f-4643-be3f-9c2212efebfc",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cj/hzdf62qj71sdyzsg2kpk9sdw0000gn/T/ipykernel_7611/3054268637.py:1: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  save_model(autoencoder,\"autoencoder_aecnn.h5\")\n",
      "/var/folders/cj/hzdf62qj71sdyzsg2kpk9sdw0000gn/T/ipykernel_7611/3054268637.py:2: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  save_model(encoder,\"encoder_aecnn.h5\")\n"
     ]
    }
   ],
   "source": [
    "save_model(autoencoder,\"autoencoder_aecnn.h5\")\n",
    "save_model(encoder,\"encoder_aecnn.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "62ed5f73-63b6-4a52-acb2-1e597233ee79",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hist.to_csv(\"aecnn_hist.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f05121b-be9a-4671-834f-91ff70847743",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}