autoencoder/Autoencoder_w_gluon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoencoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[참조]\n",
    "  - https://hackernoon.com/autoencoders-deep-learning-bits-1-11731e200694\n",
    "  - https://blog.keras.io/building-autoencoders-in-keras.html\n",
    "  - http://solarisailab.com/archives/113\n",
    "\n",
    "<br/>\n",
    "\n",
    "### Autoencoder(AE)란\n",
    "\n",
    "<img src=\"./image/Autoencoders__Introduction.png \" width=\"400\">\n",
    "\n",
    "<br/>\n",
    "- data compression algorithm으로 compression과 decompression으로 이루어진 함수(특히 Neural Network에서)를 의미 함\n",
    "- 보통 Input :Ouput 구조가 data : probabiliy distributionof the clasees인 일반적인 모형(Classifiers)와는 다르게 AE는 Input : output 구조가 동일 ()\n",
    "   [Noise 제거 목적으로 Input에 Noise 값을 추가하는 경우도 있음]\n",
    "   <br/>\n",
    "   <img src=\"./image/input_w_noise.png \" width=\"400\">\n",
    "   \n",
    " <br/>\n",
    " \n",
    "- AE는 다음과 같은 3가지의 특징을 가짐\n",
    "   - data-specific : AE 모델이 학습 시 사용한 Data와 유사한 Data에 대해서만 차원축소(compression)이 가능\n",
    "   - lossy : original Input 데이터 차원축소 시 데이터 손실이 발생\n",
    "   - leared automatically : 분석가가 Output(target)을 정의할 필요없이 데이터만으로 학습 가능\n",
    "   <br/>\n",
    "   \n",
    "- AE 활용처\n",
    "   - 2012년 초기에는 Deep Convolutional Network의 pre-train에 사용하였음. 그러나 random weight initialization, batch normalization, residual learing 방법이 더 효과적인 것으로 판명되며 pre-train에는 거의 사용되지 않음\n",
    "   - 현재는 주로 2가지 영역에 많이 사용되고 있음\n",
    "      - 1) Data Denoising\n",
    "      - 2) Dimensionality Reduction for data visualization\n",
    "        - t-SNE의 경우 low-dimension일 때 적합하기에, AE를 이용하여 차원 축소 후 t-SNE를 적용할 수 있음"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gluon을 이용한 Autoencoder 예제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Gluon module load\n",
    "import mxnet as mx\n",
    "import numpy as np\n",
    "from mxnet import nd, autograd, gluon\n",
    "ctx = mx.cpu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load MNIST Data Set\n",
    " - Gluon Data API를 이용하지 않고 MNIST ubyte data set을 직접 load\n",
    "   - 동일한 Data Set을 기준으로 Keras 기반 모델과의 Code 및 Performance 비교 목적"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ubytes load function\n",
    "\n",
    "import os, struct\n",
    "import numpy as np\n",
    "\n",
    "def load_mnist(train = True, fashion = False, path = './'):\n",
    "    if train == True:\n",
    "        if fashion == True:\n",
    "            img_fname = str(path + 'fashion/train-images-idx3-ubyte')\n",
    "            lbl_fname = str(path + 'fashion/train-labels-idx1-ubyte')\n",
    "        else:\n",
    "            img_fname = str(path + 'mnist/train-images-idx3-ubyte')\n",
    "            lbl_fname = str(path + 'mnist/train-labels-idx1-ubyte')\n",
    "    else:        \n",
    "        if fashion == True:\n",
    "            img_fname = str(path + 'fashion/t10k-images-idx3-ubyte')\n",
    "            lbl_fname = str(path + 'fashion/t10k-labels-idx1-ubyte')        \n",
    "        else:\n",
    "            img_fname = str(path + 'mnist/t10k-images-idx3-ubyte')\n",
    "            lbl_fname = str(path + 'mnist/t10k-labels-idx1-ubyte')   \n",
    "    \n",
    "    \n",
    "    with open(lbl_fname, 'rb') as flbl:\n",
    "        magic, num = struct.unpack(\">II\", flbl.read(8))\n",
    "        lbl = np.fromfile(flbl, dtype=np.int8)\n",
    "\n",
    "    with open(img_fname, 'rb') as fimg:\n",
    "        magic, num, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n",
    "        img = np.fromfile(fimg, dtype=np.uint8).reshape(len(lbl), rows, cols)\n",
    "\n",
    "    img_f = []\n",
    "    lbl_f = []\n",
    "    \n",
    "    for i in range(len(lbl)):\n",
    "        img_f.append(img[i])\n",
    "        lbl_f.append(lbl[i])\n",
    "    \n",
    "    return img, lbl\n",
    "\n",
    "##############\n",
    "# Keras API를 이용할 경우\n",
    "\n",
    "# Keras API를 이용한 MNIST Dataset Load\n",
    "# from keras.datasets import mnist\n",
    "# import numpy as np\n",
    "# (x_train, _), (x_test, _) = mnist.load_data()\n",
    "\n",
    "# x_train = x_train.astype('float32') / 255.\n",
    "# x_test = x_test.astype('float32') / 255.\n",
    "# x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n",
    "# x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\n",
    "# print(x_train.shape)\n",
    "# print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Auto encoder 결과값 확인을 위한 function (원 데이터 <-> 예측데이터 plotting)\n",
    "def plot_result(img_set_1, img_set_2, num = 10, fig_size = (20,4)):\n",
    "    import matplotlib.pyplot as plt\n",
    "    import math\n",
    "\n",
    "    img_1_dim = int(math.sqrt(img_set_1.shape[1]))\n",
    "    img_2_dim = int(math.sqrt(img_set_2.shape[1]))\n",
    "    \n",
    "    # num = how many digits we will display\n",
    "    plt.figure(figsize = fig_size)\n",
    "    for i in range(num):\n",
    "        # display original\n",
    "        ax = plt.subplot(2, num, i + 1)\n",
    "        plt.imshow(img_set_1[i].reshape(img_1_dim, img_1_dim))\n",
    "        plt.gray()\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "\n",
    "        # display reconstruction\n",
    "        ax = plt.subplot(2, num, i + 1 + num)\n",
    "        plt.imshow(img_set_2[i].reshape(img_2_dim, img_2_dim))\n",
    "        plt.gray()\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# 10 digits mnist data set\n",
    "train_i, train_l = load_mnist(train = True, fashion = False, path = './')\n",
    "test_i, test_l = load_mnist(train = False, fashion = False, path = './')\n",
    "print(train_i.shape)\n",
    "print(test_i.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AUIQfCIrwA0H9H4BpmwJXvvG+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16ffbe4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(train_i[0].reshape(28, 28, 1).reshape(28,28))\n",
    "plt.gray()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# 10 category fashion mnist data set\n",
    "f_train_i, f_train_l = load_mnist(train = True, fashion = True, path = './')\n",
    "f_test_i, f_test_l = load_mnist(train = False, fashion = True, path = './')\n",
    "\n",
    "print(f_train_i.shape)\n",
    "print(f_test_i.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1459ab940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(f_train_i[0].reshape(28, 28, 1).reshape(28,28))\n",
    "plt.gray()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 1, 28, 28)\n",
      "(60000, 784)\n"
     ]
    }
   ],
   "source": [
    "# data normalization [0,1] CNN용\n",
    "train_cnn = train_i.reshape(train_i.shape[0], train_i.shape[1], train_i.shape[1], 1).astype('float32') / 255.\n",
    "train_cnn = train_cnn.transpose((0,3,1,2))\n",
    "test_cnn = test_i.reshape(test_i.shape[0], test_i.shape[1], test_i.shape[1], 1).astype('float32') / 255.\n",
    "test_cnn = test_cnn.transpose((0,3,1,2))\n",
    "print(train_cnn.shape)\n",
    "\n",
    "# data normalization [0,1] & flattening\n",
    "train_i = train_i.reshape(train_i.shape[0], train_i.shape[1]*train_i.shape[1]).astype('float32') / 255.\n",
    "test_i = test_i.reshape(test_i.shape[0], test_i.shape[1]*test_i.shape[1]).astype('float32') / 255.\n",
    "print(train_i.shape)\n",
    "\n",
    "# noise factor 추가\n",
    "noise_factor = 0.3\n",
    "train_i_n = train_i + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=train_i.shape) \n",
    "test_i_n = test_i + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=test_i.shape) \n",
    "train_i_n = np.clip(train_i_n, 0., 1.) # [0,1 사이로 clipping]\n",
    "test_i_n = np.clip(test_i_n, 0., 1.)\n",
    "\n",
    "train_cnn_n = train_cnn + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=train_cnn.shape) \n",
    "test_cnn_n = test_cnn + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=test_cnn.shape) \n",
    "train_cnn_n = np.clip(train_cnn_n, 0., 1.) # [0,1 사이로 clipping]\n",
    "test_cnn_n = np.clip(test_cnn_n, 0., 1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HEEIIIYQQQgghJAPgSxxCCCGEEEIIIYSQDIAvcQghhBBCCCGEEEIyAL7EIYQQ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\nY4wxxpgu4L8B84VePfXSxFYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x170864e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 10\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i+1)\n",
    "    plt.imshow(train_i[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + n +1)\n",
    "    plt.imshow(train_i_n[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert numpy array to nd array (gluon에서 사용하기 위해)\n",
    "x_train_nd_cnn = nd.array(train_cnn)\n",
    "x_test_nd_cnn = nd.array(test_cnn)\n",
    "\n",
    "x_train_nd_cnn_n = nd.array(train_cnn_n)\n",
    "x_test_nd_cnn_n = nd.array(test_cnn_n)\n",
    "\n",
    "x_train_nd = nd.array(train_i)\n",
    "x_test_nd = nd.array(test_i)\n",
    "\n",
    "x_train_nd_n = nd.array(train_i_n)\n",
    "x_test_nd_n = nd.array(test_i_n)\n",
    "\n",
    "# make data_loader (mini-batch를 쉽게 만들기 위해 gluon DataLoader API 활용)\n",
    "batch_size = 256\n",
    "x_train_lodaer_cnn = gluon.data.DataLoader(x_train_nd_cnn, batch_size = batch_size, shuffle=True)\n",
    "x_test_lodaer_cnn = gluon.data.DataLoader(x_train_nd_cnn, batch_size = batch_size, shuffle=True)\n",
    "\n",
    "x_train_lodaer_cnn_n = gluon.data.DataLoader(x_train_nd_cnn_n, batch_size = batch_size, shuffle=True)\n",
    "x_test_lodaer_cnn_n = gluon.data.DataLoader(x_train_nd_cnn_n, batch_size = batch_size, shuffle=True)\n",
    "\n",
    "x_train_lodaer = gluon.data.DataLoader(x_train_nd, batch_size = batch_size, shuffle=True)\n",
    "x_test_lodaer = gluon.data.DataLoader(x_train_nd, batch_size = batch_size, shuffle=True)\n",
    "\n",
    "x_train_lodaer_n = gluon.data.DataLoader(x_train_nd_n, batch_size = batch_size, shuffle=True)\n",
    "x_test_lodaer_n = gluon.data.DataLoader(x_train_nd_n, batch_size = batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autoencoder Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Simple AE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class autoencoder_simple(gluon.Block):\n",
    "    def __init__(self):\n",
    "        super(autoencoder_simple, self).__init__()\n",
    "        with self.name_scope():\n",
    "            self.encoder = gluon.nn.Sequential('encoder_')\n",
    "            with self.encoder.name_scope():\n",
    "                self.encoder.add(gluon.nn.Dense(16, activation='relu')) # encoding & latent layer\n",
    "                \n",
    "            self.decoder = gluon.nn.Sequential('decoder_')\n",
    "            with self.decoder.name_scope():\n",
    "                self.decoder.add(gluon.nn.Dense(28 * 28, activation='sigmoid'))\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.encoder(x)\n",
    "        x = self.decoder(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learning_rate = 1e-3\n",
    "\n",
    "model_1 = autoencoder_simple()\n",
    "model_1.collect_params().initialize(mx.init.Xavier(), ctx=ctx)\n",
    "criterion = gluon.loss.L2Loss()\n",
    "optimizer = gluon.Trainer(model_1.collect_params(), 'adam',\n",
    "                          {'learning_rate': learning_rate,\n",
    "                           'wd': 1e-5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Block.collect_params of autoencoder_simple(\n",
       "  (decoder): Sequential(\n",
       "    (0): Dense(None -> 784, Activation(sigmoid))\n",
       "  )\n",
       "  (encoder): Sequential(\n",
       "    (0): Dense(None -> 16, Activation(relu))\n",
       "  )\n",
       ")>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.collect_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch [1/20], loss:0.0403\n",
      "epoch [2/20], loss:0.0222\n",
      "epoch [3/20], loss:0.0180\n",
      "epoch [4/20], loss:0.0159\n",
      "epoch [5/20], loss:0.0147\n",
      "epoch [6/20], loss:0.0139\n",
      "epoch [7/20], loss:0.0135\n",
      "epoch [8/20], loss:0.0132\n",
      "epoch [9/20], loss:0.0130\n",
      "epoch [10/20], loss:0.0128\n",
      "epoch [11/20], loss:0.0126\n",
      "epoch [12/20], loss:0.0123\n",
      "epoch [13/20], loss:0.0121\n",
      "epoch [14/20], loss:0.0119\n",
      "epoch [15/20], loss:0.0118\n",
      "epoch [16/20], loss:0.0117\n",
      "epoch [17/20], loss:0.0116\n",
      "epoch [18/20], loss:0.0115\n",
      "epoch [19/20], loss:0.0115\n",
      "epoch [20/20], loss:0.0114\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 20\n",
    "data_loader = x_train_lodaer\n",
    "model = model_1\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    running_loss = 0.0\n",
    "    n_total = 0.0\n",
    "    \n",
    "    for data in data_loader:\n",
    "        img = data\n",
    "        img = img.as_in_context(ctx)\n",
    "#         img = img.reshape((img.shape[0], -1)).as_in_context(ctx)\n",
    "\n",
    "        with mx.autograd.record():\n",
    "            output = model(img)\n",
    "            loss = criterion(output, img) # autoencoder의 target 값\n",
    "        loss.backward()\n",
    "        optimizer.step(img.shape[0])\n",
    "        running_loss += mx.nd.sum(loss).asscalar()\n",
    "        n_total += img.shape[0]\n",
    "    # ===================log========================\n",
    "    print('epoch [{}/{}], loss:{:.4f}'\n",
    "          .format(epoch + 1, num_epochs, running_loss / n_total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 16)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "# encoding된 값\n",
    "encoded_imgs = model.encoder(x_test_nd).asnumpy()\n",
    "print(encoded_imgs.shape)\n",
    "\n",
    "# decoding된 값 \n",
    "decoded_imgs = model(x_test_nd).asnumpy()\n",
    "print(decoded_imgs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x173c424a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test raw data vs decoded data\n",
    "plot_result(test_i, decoded_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175ef0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# encoded data vs decoded data\n",
    "plot_result(encoded_imgs, decoded_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 16)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "# denosied images encoding된 값\n",
    "encoded_imgs_2 = model.encoder(x_test_nd_n).asnumpy()\n",
    "print(encoded_imgs.shape)\n",
    "\n",
    "# denosied images decoding된 값 \n",
    "decoded_imgs_2 = model(x_test_nd_n).asnumpy()\n",
    "print(decoded_imgs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3gG2Y1eaZaQv7RoVtbnMSLk5jVDvWQw45JMWaMsiyNU7bV2tvlvJwmp7KIzgd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\n1fnmW9AmY1GfNXj86T14880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WaOP31xoEDNcx0PvENQk23XTT0CZWvzNlXczBNTS4vsaa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\n67UB4Nlnn00xa2/VTnj55ZdPsdZ0Ymty1tJrvR2+r2zDmet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1PlaVaubSxvU9uA/zvcnJ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\nRGQB+COOiIiIiIiIiMgC+D9ganGaIh/VFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1146a6710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_result(test_i_n, decoded_imgs_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Deep AE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class autoencoder_deep(gluon.Block):\n",
    "    def __init__(self):\n",
    "        super(autoencoder_deep, self).__init__()\n",
    "        with self.name_scope():\n",
    "            self.encoder = gluon.nn.Sequential('encoder_')\n",
    "            with self.encoder.name_scope():\n",
    "                self.encoder.add(gluon.nn.Dense(128, activation='relu'))\n",
    "                self.encoder.add(gluon.nn.Dense(64, activation='relu'))\n",
    "                self.encoder.add(gluon.nn.Dense(32, activation='relu'))\n",
    "                self.encoder.add(gluon.nn.Dense(16)) # latent layer\n",
    "                \n",
    "            self.decoder = gluon.nn.Sequential('decoder_')\n",
    "            with self.decoder.name_scope():\n",
    "                self.decoder.add(gluon.nn.Dense(32, activation='relu'))\n",
    "                self.decoder.add(gluon.nn.Dense(64, activation='relu'))\n",
    "                self.decoder.add(gluon.nn.Dense(128, activation='relu'))\n",
    "                self.decoder.add(gluon.nn.Dense(28 * 28, activation='sigmoid'))\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.encoder(x)\n",
    "        x = self.decoder(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learning_rate = 1e-3\n",
    "\n",
    "model_2 = autoencoder_deep()\n",
    "model_2.collect_params().initialize(mx.init.Xavier(), ctx=ctx)\n",
    "criterion = gluon.loss.L2Loss()\n",
    "optimizer = gluon.Trainer(model_2.collect_params(), 'adam',\n",
    "                          {'learning_rate': learning_rate,\n",
    "                           'wd': 1e-5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Block.collect_params of autoencoder_deep(\n",
       "  (decoder): Sequential(\n",
       "    (0): Dense(None -> 32, Activation(relu))\n",
       "    (1): Dense(None -> 64, Activation(relu))\n",
       "    (2): Dense(None -> 128, Activation(relu))\n",
       "    (3): Dense(None -> 784, Activation(sigmoid))\n",
       "  )\n",
       "  (encoder): Sequential(\n",
       "    (0): Dense(None -> 128, Activation(relu))\n",
       "    (1): Dense(None -> 64, Activation(relu))\n",
       "    (2): Dense(None -> 32, Activation(relu))\n",
       "    (3): Dense(None -> 16, linear)\n",
       "  )\n",
       ")>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2.collect_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch [1/20], loss:0.0344\n",
      "epoch [2/20], loss:0.0192\n",
      "epoch [3/20], loss:0.0163\n",
      "epoch [4/20], loss:0.0147\n",
      "epoch [5/20], loss:0.0137\n",
      "epoch [6/20], loss:0.0131\n",
      "epoch [7/20], loss:0.0127\n",
      "epoch [8/20], loss:0.0124\n",
      "epoch [9/20], loss:0.0121\n",
      "epoch [10/20], loss:0.0119\n",
      "epoch [11/20], loss:0.0117\n",
      "epoch [12/20], loss:0.0115\n",
      "epoch [13/20], loss:0.0114\n",
      "epoch [14/20], loss:0.0111\n",
      "epoch [15/20], loss:0.0109\n",
      "epoch [16/20], loss:0.0107\n",
      "epoch [17/20], loss:0.0106\n",
      "epoch [18/20], loss:0.0105\n",
      "epoch [19/20], loss:0.0104\n",
      "epoch [20/20], loss:0.0103\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 20\n",
    "data_loader = x_train_lodaer\n",
    "model = model_2\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    running_loss = 0.0\n",
    "    n_total = 0.0\n",
    "    \n",
    "    for data in data_loader:\n",
    "        img = data\n",
    "        img = img.as_in_context(ctx)\n",
    "#         img = img.reshape((img.shape[0], -1)).as_in_context(ctx)\n",
    "\n",
    "        with mx.autograd.record():\n",
    "            output = model(img)\n",
    "            loss = criterion(output, img) # autoencoder의 target 값\n",
    "        loss.backward()\n",
    "        optimizer.step(img.shape[0])\n",
    "        running_loss += mx.nd.sum(loss).asscalar()\n",
    "        n_total += img.shape[0]\n",
    "    # ===================log========================\n",
    "    print('epoch [{}/{}], loss:{:.4f}'\n",
    "          .format(epoch + 1, num_epochs, running_loss / n_total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 16)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "# encoding된 값\n",
    "encoded_imgs = model.encoder(x_test_nd).asnumpy()\n",
    "print(encoded_imgs.shape)\n",
    "\n",
    "# decoding된 값 \n",
    "decoded_imgs = model(x_test_nd).asnumpy()\n",
    "print(decoded_imgs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1380ea518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test raw data vs decoded data\n",
    "plot_result(test_i, decoded_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x176a41ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# encoded data vs decoded data\n",
    "plot_result(encoded_imgs, decoded_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 16)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "# denosied images encoding된 값\n",
    "encoded_imgs_2 = model.encoder(x_test_nd_n).asnumpy()\n",
    "print(encoded_imgs.shape)\n",
    "\n",
    "# denosied images decoding된 값 \n",
    "decoded_imgs_2 = model(x_test_nd_n).asnumpy()\n",
    "print(decoded_imgs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zhW2NNdZIMdPEIyJ23333FN94443ZNk21qqhSfStow6w2z6Qv7BsV2tyWJFxM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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146a2a320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_result(test_i_n, decoded_imgs_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 3. CNN AE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "class autoencoder_cnn(gluon.Block):\n",
    "    def __init__(self):\n",
    "        super(autoencoder_cnn, self).__init__()\n",
    "        with self.name_scope():\n",
    "            self.encoder = gluon.nn.Sequential('encoder_')\n",
    "            with self.encoder.name_scope():\n",
    "                # b, 16, 10, 10\n",
    "                self.encoder.add(\n",
    "                    gluon.nn.Conv2D(\n",
    "                        16, 3, strides=3, padding=1, activation='relu'))\n",
    "                self.encoder.add(gluon.nn.MaxPool2D(2, 2))  # b, 16, 5, 5\n",
    "                self.encoder.add(\n",
    "                    gluon.nn.Conv2D(8, 3, strides=2, padding=1,\n",
    "                                activation='relu'))  # b, 8, 3, 3\n",
    "                self.encoder.add(gluon.nn.MaxPool2D(2, 1))  # b, 8, 2, 2\n",
    "\n",
    "            self.decoder = gluon.nn.Sequential('decoder_')\n",
    "            with self.decoder.name_scope():\n",
    "                self.decoder.add(\n",
    "                    gluon.nn.Conv2DTranspose(16, 3, strides=2, activation='relu'))\n",
    "                self.decoder.add(\n",
    "                    gluon.nn.Conv2DTranspose(\n",
    "                        8, 5, strides=3, padding=1, activation='relu'))\n",
    "                self.decoder.add(\n",
    "                    gluon.nn.Conv2DTranspose(\n",
    "                        1, 2, strides=2, padding=1, activation='tanh'))\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.encoder(x)\n",
    "        x = self.decoder(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learning_rate = 1e-3\n",
    "\n",
    "model_3 = autoencoder_cnn()\n",
    "model_3.collect_params().initialize(mx.init.Xavier(), ctx=ctx)\n",
    "criterion = gluon.loss.L2Loss()\n",
    "optimizer = gluon.Trainer(model_3.collect_params(), 'adam',\n",
    "                          {'learning_rate': learning_rate,\n",
    "                           'wd': 1e-5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch [1/20], loss:0.0341\n",
      "epoch [2/20], loss:0.0221\n",
      "epoch [3/20], loss:0.0195\n",
      "epoch [4/20], loss:0.0184\n",
      "epoch [5/20], loss:0.0176\n",
      "epoch [6/20], loss:0.0171\n",
      "epoch [7/20], loss:0.0166\n",
      "epoch [8/20], loss:0.0162\n",
      "epoch [9/20], loss:0.0159\n",
      "epoch [10/20], loss:0.0156\n",
      "epoch [11/20], loss:0.0153\n",
      "epoch [12/20], loss:0.0151\n",
      "epoch [13/20], loss:0.0148\n",
      "epoch [14/20], loss:0.0146\n",
      "epoch [15/20], loss:0.0144\n",
      "epoch [16/20], loss:0.0143\n",
      "epoch [17/20], loss:0.0141\n",
      "epoch [18/20], loss:0.0140\n",
      "epoch [19/20], loss:0.0139\n",
      "epoch [20/20], loss:0.0138\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 20\n",
    "data_loader = x_train_lodaer_cnn\n",
    "model = model_3\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    running_loss = 0.0\n",
    "    n_total = 0.0\n",
    "    \n",
    "    for data in data_loader:\n",
    "        img = data.as_in_context(ctx)\n",
    "        batch = img.shape[0]\n",
    "\n",
    "        with mx.autograd.record():\n",
    "            output = model(img)\n",
    "            loss = criterion(output, img) # autoencoder의 target 값\n",
    "        loss.backward()\n",
    "        optimizer.step(img.shape[0])\n",
    "        running_loss += mx.nd.sum(loss).asscalar()\n",
    "        n_total += img.shape[0]\n",
    "    # ===================log========================\n",
    "    print('epoch [{}/{}], loss:{:.4f}'\n",
    "          .format(epoch + 1, num_epochs, running_loss / n_total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n"
     ]
    }
   ],
   "source": [
    "list(x_test_nd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 8, 2, 2)\n",
      "(10000, 1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# encoding된 값\n",
    "encoded_imgs = model.encoder(x_test_nd_cnn).asnumpy()\n",
    "print(encoded_imgs.shape)\n",
    "\n",
    "# decoding된 값 \n",
    "decoded_imgs = model(x_test_nd_cnn).asnumpy()\n",
    "print(decoded_imgs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x170123e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test raw data vs decoded data\n",
    "plot_result(test_i, decoded_imgs.reshape(10000,784))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8vOI1jusoxWuaUkoPPvhgU479PqX83m5NiN7F52yZ3/+a17ymKZdrjMU1WOKx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\nPLApx2duSiktXbq0Kd9+++09tph+Gz/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1176cf160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 10\n",
    "plt.figure(figsize=(20, 8))\n",
    "for i in range(n):\n",
    "    ax = plt.subplot(2, n, i +1)\n",
    "    plt.imshow(encoded_imgs[i].reshape(2, 4 * 4).T)\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    \n",
    "        # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + 1 + n)\n",
    "    plt.imshow(decoded_imgs[i].reshape(decoded_imgs[i].shape[1], decoded_imgs[i].shape[1]))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 8, 2, 2)\n",
      "(10000, 1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# encoding된 값\n",
    "encoded_imgs_2 = model.encoder(x_test_nd_cnn_n).asnumpy()\n",
    "print(encoded_imgs.shape)\n",
    "\n",
    "# decoding된 값 \n",
    "encoded_imgs_2 = model(x_test_nd_cnn_n).asnumpy()\n",
    "print(decoded_imgs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1283a9f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test raw data vs decoded data\n",
    "plot_result(test_i, decoded_imgs_2.reshape(10000,784))"
   ]
  }
 ],
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