autoencoder_denoise_tutorial.ipynb

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   "source": [
    "<h1 style=\"text-align: center;\" markdown=\"1\"> Autoencoder with Convolution & Deconvolution Example </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Intro slides for the presentation\n",
    "#<img src=\"images/images.001.jpg\" alt=\"Drawing\" style=\"width: 700px;\"/>\n",
    "#<img src=\"images/images.002.jpg\" alt=\"Drawing\" style=\"width: 700px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task of the tutorial\n",
    "\n",
    "Build a 2 layers autoencoder with TensorFlow to remove noise from an image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoencoder Overview\n",
    "\n",
    "Autoencoders are unsupervised neural networks that aim to copy their inputs to their outputs. They work by compressing the input into a **latent-space representation**, and then reconstructing the output from this representation. \n",
    "\n",
    "\n",
    "<img src=\"images/image_a.png\" alt=\"Drawing\" style=\"width: 800px;\"/>\n",
    "\n",
    "- The **latent vector** is the essence of the autoencoder;\n",
    "\n",
    "- The encoder and the decoder could be composed by any kind of layers. We will se a CNN-autoencoder;\n",
    "\n",
    "**Encoder:** This is the part of the network that compresses the input into a latent-space representation. It can be represented by an encoding function \n",
    "\n",
    "### $$h=f(x).$$\n",
    "\n",
    "**Decoder**: This part aims to reconstruct the input from the latent space representation. It can be represented by a decoding function \n",
    "\n",
    "### $$r=g(h).$$\n",
    "\n",
    "\n",
    "The autoencoder as a whole can thus be described by the function \n",
    "\n",
    "### $$g(f(x)) = r.$$ \n",
    "\n",
    "where $r$ is called *reconstruction*. By training the autoencoder to copy the input to the output ($r$ as close as the original input $x$), the latent representation $h$ will take on useful properties. For instance, if the dimension of $h$ is smaller than $x$ we can construct a representation of the input which is compressed. This forces the autoencoder to learn the most salient features of the training data (a sort of PCA).\n",
    "\n",
    "References:\n",
    "- [Gradient-based learning applied to document recognition](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf). Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Proceedings of the IEEE, 86(11):2278-2324, November 1998.\n",
    "\n",
    "## Applications of Autoencoders\n",
    "\n",
    "- **Dimensional Reduction**: throw away the output and extract the compressed layer. We can now build a new dataset with lower dimensionality;\n",
    "- **Seq2Seq**: Sequence to Sequence models (language translation, for instance) have an encoder-decoder structure;\n",
    "- **Denoising**: the input is an image with some noise added. The loss function is setted to minimize the difference between the reconstructed image and the original noise-free image.\n",
    "- **...**\n",
    "\n",
    "## MNIST Dataset Overview\n",
    "\n",
    "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n",
    "\n",
    "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n",
    "\n",
    "More info: http://yann.lecun.com/exdb/mnist/\n",
    "\n",
    "## Deconvolutional Layer (transposed conv-layer)\n",
    "\n",
    "A deconvolutional layer is just the transposed of its corresponding convolutional layer (E.g. if conv layer's shape is [height, width, numColors, numMaps], than the deconv layer will have the shape [height, width, numMaps, numColors])\n",
    "\n",
    "one-to-many (deconv; feature to pixels) rather than many-to-one (conv; pixels to feature) association.\n",
    "\n",
    "### Math of Convolution\n",
    "\n",
    "<img src=\"images/image_b.png\" alt=\"Drawing\" style=\"width: 600px;\"/>\n",
    "\n",
    "<img src=\"images/image_c.png\" alt=\"Drawing\" style=\"width: 600px;\"/>\n",
    "\n",
    "\n",
    "### Math of Transposed Convolution\n",
    "\n",
    "<img src=\"images/image_d.png\" alt=\"Drawing\" style=\"width: 600px;\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <h1 style=\"text-align: center;\" markdown=\"1\">  Tensorflow Convolutional Autoencoder for Noise Reduction </h1>\n",
    "\n",
    "#### Versions: Python 3.6, Tensorflow 1.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division, print_function, absolute_import\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.contrib.slim as slim\n",
    "\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load MNIST Dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: The dataset folder must be put in the main directory where this tutorial is placed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /Users/Yak52/Github/machine-learning-lab/./MNIST/train-images-idx3-ubyte.gz\n",
      "Extracting /Users/Yak52/Github/machine-learning-lab/./MNIST/train-labels-idx1-ubyte.gz\n",
      "Extracting /Users/Yak52/Github/machine-learning-lab/./MNIST/t10k-images-idx3-ubyte.gz\n",
      "Extracting /Users/Yak52/Github/machine-learning-lab/./MNIST/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# Import MNIST data\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "MNIST_PATH = os.path.join(os.getcwd(),'./MNIST')\n",
    "mnist = input_data.read_data_sets(MNIST_PATH, one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters and Activation Function\n",
    "\n",
    "We use a **leaky ReLU** which instead of clipping the negative values to zero, cuts them to a specific amount based on a hyperparameter alpha. This ensures that the network learns something even when the pixel value is below zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "# Training Parameters\n",
    "learning_rate = 0.01\n",
    "num_steps = 1200\n",
    "batch_size = 128\n",
    "imsize = 28\n",
    "\n",
    "# Noise Parameter\n",
    "noise_variance = 0.5\n",
    "\n",
    "display_step = 100\n",
    "\n",
    "# LeakyRelu activation function (gradient is not zero for negative values)\n",
    "def lrelu(x,alpha=0.1):\n",
    "    return tf.maximum(alpha*x,x)\n",
    "\n",
    "# tf Graph input (only pictures)\n",
    "X = tf.placeholder(\"float\", [None, 28, 28, 1])\n",
    "X_noisy = tf.placeholder(\"float\", [None, 28, 28, 1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autoencoder using Convolutional & Deconvolutional layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "encoder/conv1/Maximum:0 (?, 14, 14, 3)\n",
      "encoder/conv2/Maximum:0 (?, 7, 7, 3)\n",
      "decoder/conv1/Maximum:0 (?, 14, 14, 3)\n",
      "decoder/conv2/Maximum:0 (?, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "# Building the encoder\n",
    "# tf.nn.relu\n",
    "def encoder(x):\n",
    "    with tf.variable_scope('encoder'): \n",
    "        layer_1 = slim.conv2d(x, 3, [10,10], stride=2, padding='SAME', activation_fn=lrelu, scope='conv1')\n",
    "        print(layer_1.name, layer_1.get_shape())\n",
    "        layer_2 = slim.conv2d(layer_1, 3, [3,3], stride=2, padding='SAME', activation_fn=lrelu, scope='conv2')\n",
    "        print(layer_2.name, layer_2.get_shape())\n",
    "        return layer_2\n",
    "\n",
    "\n",
    "# Building the decoder\n",
    "def decoder(x):\n",
    "    with tf.variable_scope('decoder'): \n",
    "        layer_1 = slim.layers.conv2d_transpose(x, 3, [3,3], stride=2, padding='SAME', activation_fn=lrelu, scope='conv1')\n",
    "        print(layer_1.name, layer_1.get_shape())\n",
    "        layer_2 = slim.layers.conv2d_transpose(layer_1, 1, [10,10], stride=2, padding='SAME', activation_fn=lrelu, scope='conv2')\n",
    "        print(layer_2.name, layer_2.get_shape())\n",
    "    return layer_2\n",
    "\n",
    "# Construct model\n",
    "encoder_input = X_noisy\n",
    "encoder_output = encoder(encoder_input)\n",
    "decoder_output = decoder(encoder_output)\n",
    "\n",
    "\n",
    "# Targets (Labels) are the input data.\n",
    "y_target = X\n",
    "\n",
    "# Define loss and optimizer, minimize the squared error\n",
    "loss = tf.reduce_mean(tf.pow(y_target - decoder_output, 2))\n",
    "optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss)\n",
    "\n",
    "# Initialize the variables (i.e. assign their default value)\n",
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training (Unsupervised)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 1: Minibatch Loss: 0.114284\n",
      "Step 100: Minibatch Loss: 0.091627\n",
      "Step 200: Minibatch Loss: 0.039045\n",
      "Step 300: Minibatch Loss: 0.032179\n",
      "Step 400: Minibatch Loss: 0.030061\n",
      "Step 500: Minibatch Loss: 0.024722\n",
      "Step 600: Minibatch Loss: 0.023035\n",
      "Step 700: Minibatch Loss: 0.021502\n",
      "Step 800: Minibatch Loss: 0.023333\n",
      "Step 900: Minibatch Loss: 0.022982\n",
      "Step 1000: Minibatch Loss: 0.023276\n",
      "Step 1100: Minibatch Loss: 0.020655\n",
      "Step 1200: Minibatch Loss: 0.023176\n"
     ]
    }
   ],
   "source": [
    "# Start Training\n",
    "# Start a new TF session\n",
    "sess = tf.Session()\n",
    "\n",
    "# Run the initializer\n",
    "sess.run(init)\n",
    "\n",
    "# Training\n",
    "plot_step = []\n",
    "plot_loss = []\n",
    "for i in range(1, num_steps+1):\n",
    "    # Prepare Data\n",
    "    # Get the next batch of MNIST data (only images are needed, not labels)\n",
    "    batch_x, _ = mnist.train.next_batch(batch_size)\n",
    "    # Reshape the batch correctly\n",
    "    batch_x = batch_x.reshape([-1, 28, 28, 1])\n",
    "    # Add some Gaussian noise to the original images\n",
    "    batch_x_noisy = batch_x + np.random.normal(0, noise_variance, (batch_x.shape[0], batch_x.shape[1],batch_x.shape[2],batch_x.shape[3]))\n",
    "    # Run optimization op (backprop) and cost op (to get loss value)\n",
    "    _, l = sess.run([optimizer, loss], feed_dict={X: batch_x, X_noisy: batch_x_noisy})\n",
    "    # The following sess.run() is for an identity autoencoder \n",
    "    #_, l = sess.run([optimizer, loss], feed_dict={X: batch_x, X_noisy: batch_x})\n",
    "    # Display logs per step\n",
    "    if i % display_step == 0 or i == 1:\n",
    "        plot_step.append(i)\n",
    "        plot_loss.append(l)\n",
    "        print('Step %i: Minibatch Loss: %f' % (i, l))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loss Function Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e93668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e937f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(plot_step, plot_loss, 'bo', label='Training loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Steps ',fontsize=16)\n",
    "plt.ylabel('Loss',fontsize=16)\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12131c940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testing\n",
    "# Encode and decode images from test set and visualize their reconstruction.\n",
    "n = 3\n",
    "m = 3\n",
    "canvas_noisy = np.empty((28 * n, 28 * m))\n",
    "canvas_recon = np.empty((28 * n, 28 * m))\n",
    "canvas_orig = np.empty((28 * n, 28 * m))\n",
    "for i in range(n):\n",
    "    # MNIST test set\n",
    "    batch_x, _ = mnist.test.next_batch(n)\n",
    "    batch_x = batch_x\n",
    "    # Encode and decode the digit image\n",
    "    batch_x = batch_x.reshape([n, 28, 28, 1])\n",
    "    #batch_x_noisy = batch_x + 0.2*tf.random_normal(tf.shape(batch_x))\n",
    "    batch_x_noisy = batch_x + np.random.normal(0, noise_variance, (batch_x.shape[0], batch_x.shape[1],batch_x.shape[2],batch_x.shape[3]))\n",
    "    #g = sess.run(decoder_op, feed_dict={X: batch_x})\n",
    "    results = sess.run(decoder_output, feed_dict={X: batch_x, X_noisy: batch_x_noisy})\n",
    "    # Display input images\n",
    "    for j in range(m):\n",
    "        canvas_noisy[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = batch_x_noisy[j].reshape([28, 28])\n",
    "    # Display reconstructed images\n",
    "    for j in range(m):\n",
    "        canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = results[j].reshape([28, 28])\n",
    "        # Display original images\n",
    "    for j in range(m):\n",
    "        canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = batch_x[j].reshape([28, 28])\n",
    "\n",
    "        \n",
    "#Plotting\n",
    "plt.figure(figsize=(18,18))\n",
    "plt.subplot(131)\n",
    "plt.imshow(canvas_noisy, origin=\"upper\", cmap=\"gray\")\n",
    "plt.title(\"Noisy Images\")\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.imshow(canvas_recon, origin=\"upper\", cmap=\"gray\")\n",
    "plt.title(\"Reconstructed\\Cleaned Images\")\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.imshow(canvas_orig, origin=\"upper\", cmap=\"gray\")\n",
    "plt.title(\"Original Images\")\n",
    "plt.show()\n",
    "\n",
    "# print(\"original Images\")\n",
    "# plt.figure(figsize=(n, m))\n",
    "# plt.imshow(canvas_orig, origin=\"upper\", cmap=\"gray\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align: center;\" markdown=\"1\"> Looking inside a Deep Neural Network </h1> \n",
    "\n",
    "- Visualiziong weights from a layer \n",
    "\n",
    "- Visualiziong activations from a layer "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualiziong weights from a layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "yellow weights --> weights close to 1.0\n",
    "\n",
    "darkblue weights --> weights close to -1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 10, 1, 3)\n",
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125a73438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_weights(scope):\n",
    "    with tf.variable_scope(scope, reuse=True):\n",
    "        ww = sess.run(tf.get_variable('weights'))\n",
    "        plotNNWeights(ww)\n",
    "        #print(ww)\n",
    "        #print(np.squeeze(ww[:,:,:,0]))\n",
    "        \n",
    "def plotNNWeights(units):\n",
    "    print(units.shape)\n",
    "    filters = units.shape[3]\n",
    "    plt.figure(1, figsize=(20,20))\n",
    "    n_columns = 6\n",
    "    n_rows = math.ceil(filters / n_columns) + 1\n",
    "    for i in range(filters):\n",
    "        plt.subplot(n_rows, n_columns, i+1)\n",
    "        plt.title('Weights' + str(i))\n",
    "        ww = np.squeeze(units[:,:,:,i])\n",
    "        plt.imshow(ww, interpolation=\"nearest\")\n",
    "        #plt.imshow(ww, interpolation=\"nearest\", cmap=\"gray\")\n",
    "\n",
    "print(get_weights('encoder/conv1'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 3, 3, 3)\n",
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126d670b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(get_weights('encoder/conv2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 3, 3, 3)\n",
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d43db38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(get_weights('decoder/conv1'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 10, 1, 3)\n",
      "None\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e09c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(get_weights('decoder/conv2'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### A real example: AlexNet Conv1 weights on ImageNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The weights are useful to visualize because well-trained networks usually display nice and smooth filters without any noisy patterns. Noisy patterns can be an indicator of a network that hasn’t been trained for long enough, or possibly a very low regularization strength that may have led to overfitting.\n",
    "\n",
    "<img src=\"images/image_f.jpg\" alt=\"Drawing\" style=\"width: 400px;\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualiziong activations from a layer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getActivations(scope, stimuli):\n",
    "    #layer = tf.get_default_graph().get_tensor_by_name(scope + \"/Relu:0\")\n",
    "    layer = tf.get_default_graph().get_tensor_by_name(scope + \"/Maximum:0\")\n",
    "    units = sess.run(layer, feed_dict={X: stimuli, X_noisy: stimuli})\n",
    "    plotNNFilter(units)\n",
    "            \n",
    "    \n",
    "def plotNNFilter(units):\n",
    "    print(units.shape)\n",
    "    filters = units.shape[3]\n",
    "    plt.figure(1, figsize=(20,20))\n",
    "    n_columns = 6\n",
    "    n_rows = math.ceil(filters / n_columns) + 1\n",
    "    for i in range(filters):\n",
    "        plt.subplot(n_rows, n_columns, i+1)\n",
    "        plt.title('Activation ' + str(i))\n",
    "        plt.imshow(units[0,:,:,i], interpolation=\"nearest\", cmap=\"gray\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 28, 28, 1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_x, _ = mnist.test.next_batch(2)\n",
    "\n",
    "image = batch_x[1]\n",
    "noise = np.random.normal(0, noise_variance, image.shape[0])\n",
    "image_input_noise = image + noise\n",
    "image = image.reshape([-1, 28, 28, 1])\n",
    "image_input_noise = image_input_noise.reshape([-1, 28, 28, 1])\n",
    "np.shape(image_input_noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11e4180f0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e2094e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image_input_noise.reshape([28, 28]), interpolation=\"nearest\", cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 14, 14, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126ca7320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_input = image_input_noise\n",
    "getActivations('encoder/conv1',image_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 7, 7, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e3b6518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getActivations('encoder/conv2',image_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 14, 14, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e4348d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getActivations('decoder/conv1',image_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126e27320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "getActivations('decoder/conv2',image_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Original Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x110db8b70>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e4804a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image.reshape([28, 28]), interpolation=\"nearest\", cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<h1 style=\"text-align: center;\" markdown=\"1\"> Deep Learning and Renormalization Group (Physics) </h1>\n",
    "\n",
    "**Deep learning** is a broad set of techniques that uses multiple layers of representation to automatically learn relevant features directly from structured data\n",
    "\n",
    "**Renormalization Group** (RG) is an iterative coarse-graining scheme that allows for the extraction of relevant features (i.e. operators) as a physical system is examined at different length scales. The central goal of RG is to extract relevant features of a physical system for describing phenomena at large length scales by integrating out (i.e. marginalizing over) short distance degrees of freedom. Under this procedure, certain features, called **relevant operators**, become increasingly important while other features, dubbed **irrelevant operators**, have a diminishing effect on the physical properties of the system at large scales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ref about DL and RG\n",
    "\n",
    "*\"An exact mapping between the Variational Renormalization Group and Deep Learning\"*\n",
    "https://arxiv.org/abs/1410.3831\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<img src=\"images/images.003.jpg\" alt=\"Drawing\" style=\"width: 700px;\"/>"
   ]
  }
 ],
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