{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Autoencoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cdn-images-1.medium.com/max/800/1*LSYNW5m3TN7xRX61BZhoZA.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we will demonstrate how you can create a convolutional autoencoder in Gluon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import mxnet as mx\n",
    "from mxnet import autograd, gluon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "We will use the FashionMNIST dataset, which is of a similar format to MNIST but is richer and has more variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 512\n",
    "ctx = mx.gpu() if mx.context.num_gpus() else mx.cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = lambda x,y: (x.transpose((2,0,1)).astype('float32')/255., y)\n",
    "\n",
    "train_dataset = gluon.data.vision.FashionMNIST(train=True)\n",
    "test_dataset = gluon.data.vision.FashionMNIST(train=False)\n",
    "\n",
    "train_dataset_t = train_dataset.transform(transform)\n",
    "test_dataset_t = test_dataset.transform(transform)\n",
    "\n",
    "train_data = gluon.data.DataLoader(train_dataset_t, batch_size=batch_size, last_batch='rollover', shuffle=True, num_workers=5)\n",
    "test_data = gluon.data.DataLoader(test_dataset_t, batch_size=batch_size, last_batch='rollover', shuffle=True, num_workers=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "for i in range(10):\n",
    "    ax = plt.subplot(1, 10, i+1)\n",
    "    ax.imshow(train_dataset[i][0].squeeze().asnumpy(), cmap='gray')\n",
    "    ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = gluon.nn.HybridSequential()\n",
    "encoder = gluon.nn.HybridSequential()\n",
    "encoder.add(\n",
    "    gluon.nn.Conv2D(channels=4, kernel_size=3, padding=1, strides=(2,2), activation='relu'),\n",
    "    gluon.nn.BatchNorm(),\n",
    "    gluon.nn.Conv2D(channels=8, kernel_size=3, padding=1, strides=(2,2), activation='relu'),\n",
    "    gluon.nn.BatchNorm(),\n",
    "    gluon.nn.Conv2D(channels=16, kernel_size=3, padding=1, strides=(2,2), activation='relu'),\n",
    "    gluon.nn.BatchNorm(),\n",
    "    gluon.nn.Conv2D(channels=32, kernel_size=3, padding=0, strides=(2,2),activation='relu'),\n",
    "    gluon.nn.BatchNorm()\n",
    ")\n",
    "decoder = gluon.nn.HybridSequential()\n",
    "decoder.add(\n",
    "    gluon.nn.Conv2D(channels=32, kernel_size=3, padding=2, activation='relu'),\n",
    "    gluon.nn.HybridLambda(lambda F, x: F.UpSampling(x, scale=2, sample_type='nearest')),\n",
    "    gluon.nn.BatchNorm(),\n",
    "    gluon.nn.Conv2D(channels=16, kernel_size=3, padding=1, activation='relu'),\n",
    "    gluon.nn.HybridLambda(lambda F, x: F.UpSampling(x, scale=2, sample_type='nearest')),\n",
    "    gluon.nn.BatchNorm(),\n",
    "    gluon.nn.Conv2D(channels=8, kernel_size=3, padding=2, activation='relu'),\n",
    "    gluon.nn.HybridLambda(lambda F, x: F.UpSampling(x, scale=2, sample_type='nearest')),\n",
    "    gluon.nn.BatchNorm(),\n",
    "    gluon.nn.Conv2D(channels=4, kernel_size=3, padding=1, activation='relu'),\n",
    "    gluon.nn.Conv2D(channels=1, kernel_size=3, padding=1, activation='sigmoid')\n",
    ")\n",
    "net.add(\n",
    "        encoder,\n",
    "        decoder\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.initialize(ctx=ctx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "        Layer (type)                                Output Shape         Param #\n",
      "================================================================================\n",
      "               Input                              (1, 1, 28, 28)               0\n",
      "        Activation-1  <Symbol autoencoder_encoder_conv0_relu_fwd>               0\n",
      "        Activation-2                              (1, 4, 14, 14)               0\n",
      "            Conv2D-3                              (1, 4, 14, 14)              40\n",
      "         BatchNorm-4                              (1, 4, 14, 14)              16\n",
      "        Activation-5  <Symbol autoencoder_encoder_conv1_relu_fwd>               0\n",
      "        Activation-6                                (1, 8, 7, 7)               0\n",
      "            Conv2D-7                                (1, 8, 7, 7)             296\n",
      "         BatchNorm-8                                (1, 8, 7, 7)              32\n",
      "        Activation-9  <Symbol autoencoder_encoder_conv2_relu_fwd>               0\n",
      "       Activation-10                               (1, 16, 4, 4)               0\n",
      "           Conv2D-11                               (1, 16, 4, 4)            1168\n",
      "        BatchNorm-12                               (1, 16, 4, 4)              64\n",
      "       Activation-13  <Symbol autoencoder_encoder_conv3_relu_fwd>               0\n",
      "       Activation-14                               (1, 32, 1, 1)               0\n",
      "           Conv2D-15                               (1, 32, 1, 1)            4640\n",
      "        BatchNorm-16                               (1, 32, 1, 1)             128\n",
      "       Activation-17  <Symbol autoencoder_decoder_conv0_relu_fwd>               0\n",
      "       Activation-18                               (1, 32, 3, 3)               0\n",
      "           Conv2D-19                               (1, 32, 3, 3)            9248\n",
      "     HybridLambda-20                               (1, 32, 6, 6)               0\n",
      "        BatchNorm-21                               (1, 32, 6, 6)             128\n",
      "       Activation-22  <Symbol autoencoder_decoder_conv1_relu_fwd>               0\n",
      "       Activation-23                               (1, 16, 6, 6)               0\n",
      "           Conv2D-24                               (1, 16, 6, 6)            4624\n",
      "     HybridLambda-25                             (1, 16, 12, 12)               0\n",
      "        BatchNorm-26                             (1, 16, 12, 12)              64\n",
      "       Activation-27  <Symbol autoencoder_decoder_conv2_relu_fwd>               0\n",
      "       Activation-28                              (1, 8, 14, 14)               0\n",
      "           Conv2D-29                              (1, 8, 14, 14)            1160\n",
      "     HybridLambda-30                              (1, 8, 28, 28)               0\n",
      "        BatchNorm-31                              (1, 8, 28, 28)              32\n",
      "       Activation-32  <Symbol autoencoder_decoder_conv3_relu_fwd>               0\n",
      "       Activation-33                              (1, 4, 28, 28)               0\n",
      "           Conv2D-34                              (1, 4, 28, 28)             292\n",
      "       Activation-35  <Symbol autoencoder_decoder_conv4_sigmoid_fwd>               0\n",
      "       Activation-36                              (1, 1, 28, 28)               0\n",
      "           Conv2D-37                              (1, 1, 28, 28)              37\n",
      "================================================================================\n",
      "Parameters in forward computation graph, duplicate included\n",
      "   Total params: 21969\n",
      "   Trainable params: 21737\n",
      "   Non-trainable params: 232\n",
      "Shared params in forward computation graph: 0\n",
      "Unique parameters in model: 21969\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "net.summary(test_dataset_t[0][0].expand_dims(axis=0).as_in_context(ctx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the original image goes from 28x28 = 784 pixels to a vector of length 32. That is a ~25x information compression rate.\n",
    "Then the decoder brings back this compressed information to the original shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "l2_loss = gluon.loss.L2Loss()\n",
    "l1_loss = gluon.loss.L1Loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': 0.001, 'wd':0.001})\n",
    "net.hybridize(static_shape=True, static_alloc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [0], Loss 0.2246280246310764\n",
      "Epoch [1], Loss 0.14493223337026742\n",
      "Epoch [2], Loss 0.13147933666522688\n",
      "Epoch [3], Loss 0.12138325943906084\n",
      "Epoch [4], Loss 0.11291297684367906\n",
      "Epoch [5], Loss 0.10611823453741559\n",
      "Epoch [6], Loss 0.09942417470817892\n",
      "Epoch [7], Loss 0.09408332955124032\n",
      "Epoch [8], Loss 0.08883619716024807\n",
      "Epoch [9], Loss 0.08491455795418502\n",
      "Epoch [10], Loss 0.0809355994402352\n",
      "Epoch [11], Loss 0.07784551636785524\n",
      "Epoch [12], Loss 0.07570812029716296\n",
      "Epoch [13], Loss 0.07417513366438384\n",
      "Epoch [14], Loss 0.07218785571236895\n",
      "Epoch [15], Loss 0.07093704352944584\n",
      "Epoch [16], Loss 0.0700181406787318\n",
      "Epoch [17], Loss 0.0689836893326197\n",
      "Epoch [18], Loss 0.06782063459738708\n",
      "Epoch [19], Loss 0.06713279088338216\n"
     ]
    }
   ],
   "source": [
    "epochs = 20\n",
    "for e in range(epochs):\n",
    "    curr_loss = 0.\n",
    "    for i, (data, _) in enumerate(train_data):\n",
    "        data = data.as_in_context(ctx)\n",
    "        with autograd.record():\n",
    "            output = net(data)\n",
    "            # Compute the L2 and L1 losses between the original and the generated image\n",
    "            l2 = l2_loss(output.flatten(), data.flatten())\n",
    "            l1 = l1_loss(output.flatten(), data.flatten())\n",
    "            l =  l2 + l1 \n",
    "        l.backward()\n",
    "        trainer.step(data.shape[0])\n",
    "        \n",
    "        curr_loss += l.mean()\n",
    "\n",
    "    print(\"Epoch [{}], Loss {}\".format(e, curr_loss.asscalar()/(i+1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing reconstruction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot 10 images and their reconstruction by the autoencoder. The results are pretty good for a ~25x compression rate!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x288 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,4))\n",
    "for i in range(10):\n",
    "    idx = random.randint(0, len(test_dataset))\n",
    "    img, _ = test_dataset[idx]\n",
    "    x, _ = test_dataset_t[idx]\n",
    "\n",
    "    data = x.as_in_context(ctx).expand_dims(axis=0)\n",
    "    output = net(data)\n",
    "    \n",
    "    ax = plt.subplot(2, 10, i+1)\n",
    "    ax.imshow(img.squeeze().asnumpy(), cmap='gray')\n",
    "    ax.axis('off')\n",
    "    ax = plt.subplot(2, 10, 10+i+1)\n",
    "    ax.imshow((output[0].asnumpy() * 255.).transpose((1,2,0)).squeeze(), cmap='gray')\n",
    "    _ = ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating latent space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now use separately the **encoder** that takes an image to a latent vector and the **decoder** that transform a latent vector into images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get two images from the testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f04995adc50>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = random.randint(0, len(test_dataset))\n",
    "img1, _ = test_dataset[idx]\n",
    "x, _ = test_dataset_t[idx]\n",
    "data1 = x.as_in_context(ctx).expand_dims(axis=0)\n",
    "\n",
    "idx = random.randint(0, len(test_dataset))\n",
    "img2, _ = test_dataset[idx]\n",
    "x, _ = test_dataset_t[idx]\n",
    "data2 = x.as_in_context(ctx).expand_dims(axis=0)\n",
    "\n",
    "plt.figure(figsize=(2,2))\n",
    "plt.imshow(img1.squeeze().asnumpy(), cmap='gray')\n",
    "plt.show()\n",
    "plt.figure(figsize=(2,2))\n",
    "plt.imshow(img2.squeeze().asnumpy(), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get the latent representations of the images by passing them through the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent1 = encoder(data1)\n",
    "latent2 = encoder(data2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the latent vector is made of 32 components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 32, 1, 1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We interpolate the two latent representations, vectors of 32 values, to get a new intermediate latent representation, pass it through the decoder and plot the resulting decoded image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = 10\n",
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "for i in range(int(num)):\n",
    "    \n",
    "        new_latent = latent2*(i+1)/num + latent1*(num-i)/num\n",
    "        output = decoder(new_latent)\n",
    "        \n",
    "        #plot result\n",
    "        ax = plt.subplot(1, num, i+1)\n",
    "        ax.imshow((output[0].asnumpy() * 255.).transpose((1,2,0)).squeeze(), cmap='gray')\n",
    "        _ = ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the latent space learnt by the autoencoder is fairly smooth, there is no sudden jump from one shape to another"
   ]
  }
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