Autoencoder_denoising.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "389d5d26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nTraining a denoising autoencoder.\\nAgain on MNIST dataset.\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Training a denoising autoencoder.\n",
    "Again on MNIST dataset.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "708dd39c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense, Input\n",
    "from tensorflow.keras.layers import Conv2D, Flatten\n",
    "from tensorflow.keras.layers import Reshape, Conv2DTranspose\n",
    "from tensorflow.keras.models import Model \n",
    "from tensorflow.keras import backend as K \n",
    "from tensorflow.keras.datasets import mnist "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "725d3b72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2392cabf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading MNIST dataset\n",
    "(x_train,_),(x_test,_) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c1dc3f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reshape to (28,28,1) and normalize input images \n",
    "\n",
    "image_size = x_train.shape[1]\n",
    "x_train = np.reshape(x_train,[-1,image_size, image_size,1])\n",
    "x_test = np.reshape(x_test,[-1,image_size, image_size,1])\n",
    "x_train = x_train.astype('float32') / 255\n",
    "x_test = x_test.astype('float32') / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "238a5332",
   "metadata": {},
   "outputs": [],
   "source": [
    "#generate corrupted MNIST images by adding noise with normal dist centered at 0.5 and std = 0.5 \n",
    "\n",
    "noise = np.random.normal(loc=0.5,scale=0.5,size=x_train.shape)\n",
    "x_train_noisy = x_train + noise\n",
    "\n",
    "noise = np.random.normal(loc=0.5, scale=0.5, size=x_test.shape)\n",
    "x_test_noisy = x_test + noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9c7f8ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding noise may exceed normalized pixel values>1.0 or <0.0\n",
    "#clip pixel values >1.0 and <0.0 to 0.0\n",
    "\n",
    "x_train_noisy = np.clip(x_train_noisy,0.,1.)\n",
    "x_test_noisy = np.clip(x_test_noisy,0.,1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09e03854",
   "metadata": {},
   "outputs": [],
   "source": [
    "#network parameters \n",
    "\n",
    "input_shape = (image_size, image_size,1)\n",
    "batch_size = 32\n",
    "kernel_size = 3\n",
    "latent_dim = 16\n",
    "\n",
    "#encoder/decoder number of CNN layers and filters per layer\n",
    "layer_filters = [32,64]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c1cbf0ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#build the autoencoder model\n",
    "#first the encoder part \n",
    "\n",
    "inputs = Input(shape=input_shape, name='encoder_input')\n",
    "x = inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32b91783",
   "metadata": {},
   "outputs": [],
   "source": [
    "#stack of Conv2D(32)-Conv2D(64)\n",
    "\n",
    "for filters in layer_filters:\n",
    "    \n",
    "    x = Conv2D(filters=filters,\n",
    "              kernel_size=kernel_size,\n",
    "              strides=2,\n",
    "              activation='relu',\n",
    "              padding='same')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "84107c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "#shape info need to build decoder model so we do not need to do hand computation \n",
    "#the input to the decoder first Conv2DTranspose will have this shape \n",
    "#shape is (7,7,64) which can be processed by the decoder back to (28,28,1)\n",
    "\n",
    "shape = K.int_shape(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c6c26238",
   "metadata": {},
   "outputs": [],
   "source": [
    "#generate the latent vector\n",
    "x = Flatten()(x)\n",
    "latent = Dense(latent_dim, name='latent_vector')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6855c06b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"encoder\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " encoder_input (InputLayer)  [(None, 28, 28, 1)]       0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 14, 14, 32)        320       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 7, 7, 64)          18496     \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 3136)              0         \n",
      "                                                                 \n",
      " latent_vector (Dense)       (None, 16)                50192     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 69,008\n",
      "Trainable params: 69,008\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#insantiate encoder mode\n",
    "encoder = Model(inputs, latent, name='encoder')\n",
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d2dfe272",
   "metadata": {},
   "outputs": [],
   "source": [
    "#build the decoder model\n",
    "latent_inputs = Input(shape=(latent_dim,),name='decoder_input')\n",
    "\n",
    "#use the shape (7,7,64) that was earlier saved\n",
    "x = Dense(shape[1]*shape[2]*shape[3])(latent_inputs)\n",
    "#from vector to suitable shape for transposed conv\n",
    "x = Reshape((shape[1],shape[2],shape[3]))(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "636811d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#stack of Conv2DTranspose(64)-Conv2DTranspose(32)\n",
    "\n",
    "for filters in layer_filters[::-1]:\n",
    "    x = Conv2DTranspose(filters=filters,\n",
    "                       kernel_size=kernel_size,\n",
    "                       strides=2,\n",
    "                       activation='relu',\n",
    "                       padding='same')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8236369b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reconstruct the denoised input\n",
    "\n",
    "outputs = Conv2DTranspose(filters=1,\n",
    "                         kernel_size=kernel_size,\n",
    "                         padding='same',\n",
    "                         activation='sigmoid',\n",
    "                         name='decoder_output')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7695878d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"decoder\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " decoder_input (InputLayer)  [(None, 16)]              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 3136)              53312     \n",
      "                                                                 \n",
      " reshape (Reshape)           (None, 7, 7, 64)          0         \n",
      "                                                                 \n",
      " conv2d_transpose (Conv2DTra  (None, 14, 14, 64)       36928     \n",
      " nspose)                                                         \n",
      "                                                                 \n",
      " conv2d_transpose_1 (Conv2DT  (None, 28, 28, 32)       18464     \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " decoder_output (Conv2DTrans  (None, 28, 28, 1)        289       \n",
      " pose)                                                           \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 108,993\n",
      "Trainable params: 108,993\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#insantiate decoder model\n",
    "decoder = Model(latent_inputs, outputs, name='decoder')\n",
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "398ffa42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"autoencoder\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " encoder_input (InputLayer)  [(None, 28, 28, 1)]       0         \n",
      "                                                                 \n",
      " encoder (Functional)        (None, 16)                69008     \n",
      "                                                                 \n",
      " decoder (Functional)        (None, 28, 28, 1)         108993    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 178,001\n",
      "Trainable params: 178,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#autoencoder = encoder + decoder\n",
    "#instantiate autoencoder model \n",
    "\n",
    "autoencoder = Model(inputs, decoder(encoder(inputs)),name='autoencoder')\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "73df3f71",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mean Square Error (MSE) loss function, Adam Optimizer\n",
    "\n",
    "autoencoder.compile(loss='mse',optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0565aba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "1875/1875 [==============================] - 34s 18ms/step - loss: 0.0349 - val_loss: 0.0201\n",
      "Epoch 2/40\n",
      "1875/1875 [==============================] - 36s 19ms/step - loss: 0.0192 - val_loss: 0.0181\n",
      "Epoch 3/40\n",
      "1875/1875 [==============================] - 39s 21ms/step - loss: 0.0177 - val_loss: 0.0172\n",
      "Epoch 4/40\n",
      "1875/1875 [==============================] - 42s 22ms/step - loss: 0.0170 - val_loss: 0.0166\n",
      "Epoch 5/40\n",
      "1875/1875 [==============================] - 49s 26ms/step - loss: 0.0165 - val_loss: 0.0164\n",
      "Epoch 6/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0161 - val_loss: 0.0163\n",
      "Epoch 7/40\n",
      "1875/1875 [==============================] - 58s 31ms/step - loss: 0.0158 - val_loss: 0.0162\n",
      "Epoch 8/40\n",
      "1875/1875 [==============================] - 57s 31ms/step - loss: 0.0156 - val_loss: 0.0159\n",
      "Epoch 9/40\n",
      "1875/1875 [==============================] - 57s 31ms/step - loss: 0.0154 - val_loss: 0.0157\n",
      "Epoch 10/40\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0152 - val_loss: 0.0156\n",
      "Epoch 11/40\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0150 - val_loss: 0.0156\n",
      "Epoch 12/40\n",
      "1875/1875 [==============================] - 57s 31ms/step - loss: 0.0149 - val_loss: 0.0157\n",
      "Epoch 13/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0148 - val_loss: 0.0155\n",
      "Epoch 14/40\n",
      "1875/1875 [==============================] - 57s 31ms/step - loss: 0.0147 - val_loss: 0.0156\n",
      "Epoch 15/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0146 - val_loss: 0.0156\n",
      "Epoch 16/40\n",
      "1875/1875 [==============================] - 58s 31ms/step - loss: 0.0145 - val_loss: 0.0154\n",
      "Epoch 17/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0145 - val_loss: 0.0155\n",
      "Epoch 18/40\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0144 - val_loss: 0.0155\n",
      "Epoch 19/40\n",
      "1875/1875 [==============================] - 55s 29ms/step - loss: 0.0143 - val_loss: 0.0155\n",
      "Epoch 20/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0142 - val_loss: 0.0154\n",
      "Epoch 21/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0142 - val_loss: 0.0153\n",
      "Epoch 22/40\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0141 - val_loss: 0.0152\n",
      "Epoch 23/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0141 - val_loss: 0.0153\n",
      "Epoch 24/40\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0141 - val_loss: 0.0154\n",
      "Epoch 25/40\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0140 - val_loss: 0.0153\n",
      "Epoch 26/40\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0140 - val_loss: 0.0154\n",
      "Epoch 27/40\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0139 - val_loss: 0.0154\n",
      "Epoch 28/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0139 - val_loss: 0.0153\n",
      "Epoch 29/40\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0139 - val_loss: 0.0153\n",
      "Epoch 30/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0138 - val_loss: 0.0153\n",
      "Epoch 31/40\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0138 - val_loss: 0.0154\n",
      "Epoch 32/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0138 - val_loss: 0.0153\n",
      "Epoch 33/40\n",
      "1875/1875 [==============================] - 58s 31ms/step - loss: 0.0137 - val_loss: 0.0153\n",
      "Epoch 34/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0137 - val_loss: 0.0153\n",
      "Epoch 35/40\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0137 - val_loss: 0.0156\n",
      "Epoch 36/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0137 - val_loss: 0.0153\n",
      "Epoch 37/40\n",
      "1875/1875 [==============================] - 57s 30ms/step - loss: 0.0136 - val_loss: 0.0153\n",
      "Epoch 38/40\n",
      "1875/1875 [==============================] - 55s 30ms/step - loss: 0.0136 - val_loss: 0.0154\n",
      "Epoch 39/40\n",
      "1875/1875 [==============================] - 57s 31ms/step - loss: 0.0136 - val_loss: 0.0154\n",
      "Epoch 40/40\n",
      "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0136 - val_loss: 0.0153\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x22e801c0af0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train the autoencoder\n",
    "\n",
    "autoencoder.fit(x_train_noisy,\n",
    "               x_train,\n",
    "               validation_data=(x_test_noisy,x_test),\n",
    "               epochs=40,\n",
    "               batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2b53ccde",
   "metadata": {},
   "outputs": [],
   "source": [
    "#preduct the autoencoder output from corrupted test images \n",
    "\n",
    "x_decoded = autoencoder.predict(x_test_noisy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ecf1d069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3 sets of image with 9 MNIST digits\n",
    "#1st rows - original image\n",
    "#2nd rows images corrupted by noise\n",
    "#3rd tows - denoised images\n",
    "\n",
    "rows, cols, = 3,9\n",
    "num = rows* cols\n",
    "\n",
    "imgs = np.concatenate([x_test[:num],x_test_noisy[:num],x_decoded[:num]])\n",
    "imgs = imgs.reshape((rows * 3, cols, image_size, image_size))\n",
    "imgs = np.vstack(np.split(imgs, rows, axis=1))\n",
    "imgs = imgs.reshape((rows * 3,-1, image_size, image_size))\n",
    "imgs = np.vstack([np.hstack(i) for i in imgs])\n",
    "imgs = (imgs * 255).astype(np.uint8)\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.axis('off')\n",
    "plt.title('Original Images: top rows,'\n",
    "         'Corrupted Input: middle rows,'\n",
    "         'Denoised Input: thrid rows')\n",
    "plt.imshow(imgs, interpolation='none',cmap='gray')\n",
    "Image.fromarray(imgs).save('corrupted_and_denoised.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e1d1906",
   "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": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}