DenoisingAutoencoder/Denoising-Autoencoder-using-Tensorflow.ipynb · cubone/learnopencv

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Denoising-Autoencoder-using-Tensorflow.ipynb 122.54 KB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow.keras.layers import (\n",
    "    Conv2D,\n",
    "    MaxPooling2D,\n",
    "    Conv2DTranspose,\n",
    "    Flatten,\n",
    "    Dense\n",
    ")\n",
    "from tensorflow.keras import Sequential, Model\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "import os\n",
    "import random\n",
    "import tensorflow as tf\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's configure all random numbers generators to support determinism and obtain reproducible results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# settings for reproducibility\n",
    "seed = 42\n",
    "random.seed(seed)\n",
    "tf.random.set_seed(seed)\n",
    "np.random.seed(seed)\n",
    "\n",
    "os.environ['TF_DETERMINISTIC_OPS'] = '1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of train images: (28, 28), Number of train images: 60000\n",
      "Size of test images: (28, 28), Number of test images: 10000\n"
     ]
    },
    {
     "data": {
      "image/png": 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7yu5N0kb1v6w7rv5zGwskXSJpq6QPJL0haWwb9fafknZJek/9wZpQUm/T1P8S/T1JO7PLjWU/d4m+WvK88XFZIAhO0AFBEHYgCMIOBEHYgSAIOxAEYQeCIOxAEP8PJdJc1jCDmVwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load mnist dataset\n",
    "(train_imgs, train_labels), (test_imgs, test_labels) = tf.keras.datasets.mnist.load_data()\n",
    "\n",
    "# fit image pixel values from 0 to 1\n",
    "train_imgs, test_imgs = train_imgs / 255.0, test_imgs / 255.0 \n",
    "\n",
    "# check data array shapes:\n",
    "print(\"Size of train images: {}, Number of train images: {}\".format(train_imgs.shape[-2:], train_imgs.shape[0]))\n",
    "print(\"Size of test images: {}, Number of test images: {}\".format(test_imgs.shape[-2:], test_imgs.shape[0]))\n",
    "\n",
    "# plot image example from training images\n",
    "plt.imshow(train_imgs[1], cmap='Greys')\n",
    "plt.show()\n",
    "\n",
    "# plot image example from test images\n",
    "plt.imshow(test_imgs[0], cmap='Greys')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define leaky ReLU function\n",
    "def lrelu(x, alpha=0.1):\n",
    "    return tf.math.maximum(alpha*x, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# describe encoder pipeline\n",
    "encoder = Sequential([\n",
    "    # convolution\n",
    "    Conv2D(\n",
    "        filters=32,\n",
    "        kernel_size=(3,3),\n",
    "        strides=(1,1),\n",
    "        padding='SAME',\n",
    "        use_bias=True,\n",
    "        activation=lrelu,\n",
    "        name='conv1'\n",
    "    ),\n",
    "    # the input size is 28x28x32\n",
    "    MaxPooling2D(\n",
    "        pool_size=(2,2),\n",
    "        strides=(2,2),\n",
    "        name='pool1'\n",
    "    ),\n",
    "    # the input size is 14x14x32\n",
    "    Conv2D(\n",
    "        filters=32,\n",
    "        kernel_size=(3,3),\n",
    "        strides=(1,1),\n",
    "        padding='SAME',\n",
    "        use_bias=True,\n",
    "        activation=lrelu,\n",
    "        name='conv2'\n",
    "    ),\n",
    "    # the input size is 14x14x32\n",
    "    MaxPooling2D(\n",
    "        pool_size=(2,2),\n",
    "        strides=(2,2),\n",
    "        name='encoding'\n",
    "    )\n",
    "    # the output size is 7x7x32\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# describe decoder pipeline\n",
    "decoder = Sequential([\n",
    "    Conv2D(\n",
    "        filters=32,\n",
    "        kernel_size=(3,3),\n",
    "        strides=(1,1),\n",
    "        name='conv3',\n",
    "        padding='SAME',\n",
    "        use_bias=True,\n",
    "        activation=lrelu\n",
    "    ),\n",
    "    # updampling, the input size is 7x7x32\n",
    "    Conv2DTranspose(\n",
    "        filters=32,\n",
    "        kernel_size=3,\n",
    "        padding='same',\n",
    "        strides=2,\n",
    "        name='upsample1'\n",
    "    ),\n",
    "    # upsampling, the input size is 14x14x32\n",
    "    Conv2DTranspose(\n",
    "        filters=32,\n",
    "        kernel_size=3,\n",
    "        padding='same',\n",
    "        strides=2,\n",
    "        name='upsample2'\n",
    "    ),\n",
    "    # the input size is 28x28x32\n",
    "    Conv2D(\n",
    "        filters=1,\n",
    "        kernel_size=(3,3),\n",
    "        strides=(1,1),\n",
    "        name='logits',\n",
    "        padding='SAME',\n",
    "        use_bias=True\n",
    "    )    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model class definition\n",
    "class EncoderDecoderModel(Model):\n",
    "    def __init__(self, is_sigmoid=False):\n",
    "        super(EncoderDecoderModel, self).__init__()\n",
    "        # assign encoder sequence\n",
    "        self._encoder = encoder\n",
    "        # assign decoder sequence \n",
    "        self._decoder = decoder\n",
    "        self._is_sigmoid = is_sigmoid\n",
    "        \n",
    "    # forward pass\n",
    "    def call(self, x):\n",
    "        x = self._encoder(x)\n",
    "        decoded = self._decoder(x)\n",
    "        if self._is_sigmoid:\n",
    "            decoded = tf.keras.activations.sigmoid(decoded)\n",
    "        return decoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare input data for the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add noise to the images for train and test\n",
    "def distort_image(input_imgs, noise_factor=0.5):\n",
    "    noisy_imgs = input_imgs + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=input_imgs.shape) \n",
    "    noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n",
    "    return noisy_imgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare training reference images: add new dimension\n",
    "train_imgs_data = train_imgs[..., tf.newaxis]\n",
    "\n",
    "# prepare test reference images: add new dimension\n",
    "test_imgs_data = test_imgs[..., tf.newaxis]\n",
    "\n",
    "# prepare distorted input data for training\n",
    "train_noisy_imgs = distort_image(train_imgs_data)\n",
    "\n",
    "# prepare distorted input data for evaluation\n",
    "test_noisy_imgs = distort_image(test_imgs_data)\n",
    "\n",
    "# plot distorted image example from training images\n",
    "image_id_to_plot = 0\n",
    "plt.imshow(tf.squeeze(train_noisy_imgs[image_id_to_plot]), cmap='Greys')\n",
    "plt.title(\"The number is: {}\".format(train_labels[image_id_to_plot]))\n",
    "plt.show()\n",
    "\n",
    "# plot distorted image example from test images\n",
    "plt.imshow(tf.squeeze(test_noisy_imgs[image_id_to_plot]), cmap='Greys')\n",
    "plt.title(\"The number is: {}\".format(test_labels[image_id_to_plot]))\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define custom target function for further minimization\n",
    "def cost_function(labels=None, logits=None, name=None):\n",
    "    loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits, name=name)\n",
    "    return tf.reduce_mean(loss) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# funstion for train and val losses visualizations\n",
    "def plot_losses(results):\n",
    "    plt.plot(results.history['loss'], 'bo', label='Training loss')\n",
    "    plt.plot(results.history['val_loss'], 'r', label='Validation loss')\n",
    "    plt.title('Training and validation loss',fontsize=14)\n",
    "    plt.xlabel('Epochs ',fontsize=14)\n",
    "    plt.ylabel('Loss',fontsize=14)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block describes basic training pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/25\n",
      "60000/60000 [==============================] - 8s 130us/sample - loss: 0.5725 - val_loss: 0.4929\n",
      "Epoch 2/25\n",
      "60000/60000 [==============================] - 6s 93us/sample - loss: 0.4393 - val_loss: 0.3462\n",
      "Epoch 3/25\n",
      "60000/60000 [==============================] - 6s 93us/sample - loss: 0.2448 - val_loss: 0.2063\n",
      "Epoch 4/25\n",
      "60000/60000 [==============================] - 6s 92us/sample - loss: 0.1895 - val_loss: 0.1741\n",
      "Epoch 5/25\n",
      "60000/60000 [==============================] - 6s 95us/sample - loss: 0.1671 - val_loss: 0.1590\n",
      "Epoch 6/25\n",
      "60000/60000 [==============================] - 6s 93us/sample - loss: 0.1548 - val_loss: 0.1485\n",
      "Epoch 7/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1457 - val_loss: 0.1407\n",
      "Epoch 8/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1389 - val_loss: 0.1349\n",
      "Epoch 9/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1342 - val_loss: 0.1310\n",
      "Epoch 10/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1308 - val_loss: 0.1281\n",
      "Epoch 11/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1284 - val_loss: 0.1259\n",
      "Epoch 12/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1265 - val_loss: 0.1243\n",
      "Epoch 13/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1250 - val_loss: 0.1229\n",
      "Epoch 14/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1238 - val_loss: 0.1218\n",
      "Epoch 15/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1227 - val_loss: 0.1208\n",
      "Epoch 16/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1217 - val_loss: 0.1199\n",
      "Epoch 17/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1209 - val_loss: 0.1192\n",
      "Epoch 18/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1202 - val_loss: 0.1185\n",
      "Epoch 19/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1195 - val_loss: 0.1179\n",
      "Epoch 20/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1190 - val_loss: 0.1173\n",
      "Epoch 21/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1184 - val_loss: 0.1168\n",
      "Epoch 22/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1179 - val_loss: 0.1163\n",
      "Epoch 23/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1175 - val_loss: 0.1159\n",
      "Epoch 24/25\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1170 - val_loss: 0.1155\n",
      "Epoch 25/25\n",
      "60000/60000 [==============================] - 6s 93us/sample - loss: 0.1166 - val_loss: 0.1152\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# instantiate the model\n",
    "encoder_decoder_model = EncoderDecoderModel()\n",
    "\n",
    "# training loop params\n",
    "num_epochs = 25\n",
    "batch_size_to_set = 64\n",
    "\n",
    "# training process params\n",
    "learning_rate = 1e-5\n",
    "# default number of workers for training process\n",
    "num_workers = 2\n",
    "\n",
    "# initialize the training configurations such as optimizer, loss function and accuracy metrics\n",
    "encoder_decoder_model.compile(\n",
    "    optimizer=tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate),\n",
    "    loss=cost_function,\n",
    "    metrics=None\n",
    ")\n",
    "\n",
    "results = encoder_decoder_model.fit(\n",
    "    train_noisy_imgs,\n",
    "    train_imgs_data,\n",
    "    epochs=num_epochs,\n",
    "    batch_size=batch_size_to_set,\n",
    "    validation_data=(test_noisy_imgs, test_imgs_data),\n",
    "    workers=num_workers,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "# visualize train and val losses\n",
    "plot_losses(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view the decoding process on the same data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate the model with final sigmoid activation\n",
    "encoder_decoder_model2 = EncoderDecoderModel(is_sigmoid=True)\n",
    "\n",
    "# define the number of images to decode\n",
    "img_num_to_decode = 10\n",
    "\n",
    "# get 10 test reference images\n",
    "test_imgs_data_decode = test_imgs_data[:img_num_to_decode]\n",
    "\n",
    "# get 10 distorted input test data and cast to float32 as tf layer's dtype is float32\n",
    "test_noisy_imgs_decode = tf.cast(test_noisy_imgs[:img_num_to_decode], tf.float32)\n",
    "\n",
    "decoded_images = encoder_decoder_model2(test_noisy_imgs_decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Images\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Noisy Images\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reconstruction of Noisy Images\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# view initial, noisy and decoded images\n",
    "plt.figure(figsize=(20, 4))\n",
    "plt.title('Reconstructed Images')\n",
    "\n",
    "print(\"Original Images\")\n",
    "for i in range(img_num_to_decode):\n",
    "    plt.subplot(2, img_num_to_decode, i+1)\n",
    "    plt.imshow(test_imgs_data_decode[i, ..., 0], cmap='gray')\n",
    "plt.show()    \n",
    "plt.figure(figsize=(20, 4))\n",
    "\n",
    "# plot the range of input noisy images\n",
    "print(\"Noisy Images\")\n",
    "for i in range(img_num_to_decode):\n",
    "    plt.subplot(2, img_num_to_decode, i+1)\n",
    "    plt.imshow(test_noisy_imgs_decode[i, ..., 0], cmap='gray')\n",
    "plt.show()    \n",
    "plt.figure(figsize=(20, 4))\n",
    "\n",
    "# plot the range of decoded noisy images\n",
    "print(\"Reconstruction of Noisy Images\")\n",
    "for i in range(img_num_to_decode):\n",
    "    plt.subplot(2, img_num_to_decode, i+1)\n",
    "    plt.imshow(decoded_images[i, ..., 0], cmap='gray')    \n",
    "plt.show()    \n",
    "plt.close()  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

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提交于 2020-06-15 17:26 +08:00 . Minor modifications

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