autoencoder_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pytorch autoencoer classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "from torchvision import datasets,transforms\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import copy\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Downloading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainLoader=torch.utils.data.DataLoader(datasets.FashionMNIST('./fmnist/',train=True,\n",
    "                                                             download=True,\n",
    "                                                             transform=transforms.Compose(\n",
    "                                                                 [transforms.ToTensor()])),batch_size\n",
    "                                       =1024,shuffle=True,num_workers=1)\n",
    "#可以看到这里的batch_size=1024\n",
    "testLoader=torch.utils.data.DataLoader(datasets.FashionMNIST('./fmnist/',train=False,\n",
    "                                                             download=True,\n",
    "                                                             transform=transforms.Compose(\n",
    "                                                                 [transforms.ToTensor()])),batch_size\n",
    "                                       =1024,shuffle=True,num_workers=1)\n",
    "#对于这个数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'torch.utils.data.dataloader.DataLoader'>\n",
      "Dataset FashionMNIST\n",
      "    Number of datapoints: 60000\n",
      "    Root location: ./fmnist/\n",
      "    Split: Train\n",
      "    StandardTransform\n",
      "Transform: Compose(\n",
      "               ToTensor()\n",
      "           )\n",
      "1024\n",
      "1\n",
      "<class 'tuple'>\n",
      "2\n",
      "tensor([[[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0039, 0.0000, 0.0000, 0.0510,\n",
      "          0.2863, 0.0000, 0.0000, 0.0039, 0.0157, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0039, 0.0039, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0118, 0.0000, 0.1412, 0.5333,\n",
      "          0.4980, 0.2431, 0.2118, 0.0000, 0.0000, 0.0000, 0.0039, 0.0118,\n",
      "          0.0157, 0.0000, 0.0000, 0.0118],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0235, 0.0000, 0.4000, 0.8000,\n",
      "          0.6902, 0.5255, 0.5647, 0.4824, 0.0902, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0471, 0.0392, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.6078, 0.9255,\n",
      "          0.8118, 0.6980, 0.4196, 0.6118, 0.6314, 0.4275, 0.2510, 0.0902,\n",
      "          0.3020, 0.5098, 0.2824, 0.0588],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0039, 0.0000, 0.2706, 0.8118, 0.8745,\n",
      "          0.8549, 0.8471, 0.8471, 0.6392, 0.4980, 0.4745, 0.4784, 0.5725,\n",
      "          0.5529, 0.3451, 0.6745, 0.2588],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0039, 0.0039, 0.0039, 0.0000, 0.7843, 0.9098, 0.9098,\n",
      "          0.9137, 0.8980, 0.8745, 0.8745, 0.8431, 0.8353, 0.6431, 0.4980,\n",
      "          0.4824, 0.7686, 0.8980, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.7176, 0.8824, 0.8471,\n",
      "          0.8745, 0.8941, 0.9216, 0.8902, 0.8784, 0.8706, 0.8784, 0.8667,\n",
      "          0.8745, 0.9608, 0.6784, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.7569, 0.8941, 0.8549,\n",
      "          0.8353, 0.7765, 0.7059, 0.8314, 0.8235, 0.8275, 0.8353, 0.8745,\n",
      "          0.8627, 0.9529, 0.7922, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0039, 0.0118, 0.0000, 0.0471, 0.8588, 0.8627, 0.8314,\n",
      "          0.8549, 0.7529, 0.6627, 0.8902, 0.8157, 0.8549, 0.8784, 0.8314,\n",
      "          0.8863, 0.7725, 0.8196, 0.2039],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0235, 0.0000, 0.3882, 0.9569, 0.8706, 0.8627,\n",
      "          0.8549, 0.7961, 0.7765, 0.8667, 0.8431, 0.8353, 0.8706, 0.8627,\n",
      "          0.9608, 0.4667, 0.6549, 0.2196],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0157, 0.0000, 0.0000, 0.2157, 0.9255, 0.8941, 0.9020,\n",
      "          0.8941, 0.9412, 0.9098, 0.8353, 0.8549, 0.8745, 0.9176, 0.8510,\n",
      "          0.8510, 0.8196, 0.3608, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0039, 0.0157, 0.0235, 0.0275, 0.0078, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.9294, 0.8863, 0.8510, 0.8745,\n",
      "          0.8706, 0.8588, 0.8706, 0.8667, 0.8471, 0.8745, 0.8980, 0.8431,\n",
      "          0.8549, 1.0000, 0.3020, 0.0000],\n",
      "         [0.0000, 0.0118, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.2431, 0.5686, 0.8000, 0.8941, 0.8118, 0.8353, 0.8667,\n",
      "          0.8549, 0.8157, 0.8275, 0.8549, 0.8784, 0.8745, 0.8588, 0.8431,\n",
      "          0.8784, 0.9569, 0.6235, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0706, 0.1725, 0.3216, 0.4196,\n",
      "          0.7412, 0.8941, 0.8627, 0.8706, 0.8510, 0.8863, 0.7843, 0.8039,\n",
      "          0.8275, 0.9020, 0.8784, 0.9176, 0.6902, 0.7373, 0.9804, 0.9725,\n",
      "          0.9137, 0.9333, 0.8431, 0.0000],\n",
      "         [0.0000, 0.2235, 0.7333, 0.8157, 0.8784, 0.8667, 0.8784, 0.8157,\n",
      "          0.8000, 0.8392, 0.8157, 0.8196, 0.7843, 0.6235, 0.9608, 0.7569,\n",
      "          0.8078, 0.8745, 1.0000, 1.0000, 0.8667, 0.9176, 0.8667, 0.8275,\n",
      "          0.8627, 0.9098, 0.9647, 0.0000],\n",
      "         [0.0118, 0.7922, 0.8941, 0.8784, 0.8667, 0.8275, 0.8275, 0.8392,\n",
      "          0.8039, 0.8039, 0.8039, 0.8627, 0.9412, 0.3137, 0.5882, 1.0000,\n",
      "          0.8980, 0.8667, 0.7373, 0.6039, 0.7490, 0.8235, 0.8000, 0.8196,\n",
      "          0.8706, 0.8941, 0.8824, 0.0000],\n",
      "         [0.3843, 0.9137, 0.7765, 0.8235, 0.8706, 0.8980, 0.8980, 0.9176,\n",
      "          0.9765, 0.8627, 0.7608, 0.8431, 0.8510, 0.9451, 0.2549, 0.2863,\n",
      "          0.4157, 0.4588, 0.6588, 0.8588, 0.8667, 0.8431, 0.8510, 0.8745,\n",
      "          0.8745, 0.8784, 0.8980, 0.1137],\n",
      "         [0.2941, 0.8000, 0.8314, 0.8000, 0.7569, 0.8039, 0.8275, 0.8824,\n",
      "          0.8471, 0.7255, 0.7725, 0.8078, 0.7765, 0.8353, 0.9412, 0.7647,\n",
      "          0.8902, 0.9608, 0.9373, 0.8745, 0.8549, 0.8314, 0.8196, 0.8706,\n",
      "          0.8627, 0.8667, 0.9020, 0.2627],\n",
      "         [0.1882, 0.7961, 0.7176, 0.7608, 0.8353, 0.7725, 0.7255, 0.7451,\n",
      "          0.7608, 0.7529, 0.7922, 0.8392, 0.8588, 0.8667, 0.8627, 0.9255,\n",
      "          0.8824, 0.8471, 0.7804, 0.8078, 0.7294, 0.7098, 0.6941, 0.6745,\n",
      "          0.7098, 0.8039, 0.8078, 0.4510],\n",
      "         [0.0000, 0.4784, 0.8588, 0.7569, 0.7020, 0.6706, 0.7176, 0.7686,\n",
      "          0.8000, 0.8235, 0.8353, 0.8118, 0.8275, 0.8235, 0.7843, 0.7686,\n",
      "          0.7608, 0.7490, 0.7647, 0.7490, 0.7765, 0.7529, 0.6902, 0.6118,\n",
      "          0.6549, 0.6941, 0.8235, 0.3608],\n",
      "         [0.0000, 0.0000, 0.2902, 0.7412, 0.8314, 0.7490, 0.6863, 0.6745,\n",
      "          0.6863, 0.7098, 0.7255, 0.7373, 0.7412, 0.7373, 0.7569, 0.7765,\n",
      "          0.8000, 0.8196, 0.8235, 0.8235, 0.8275, 0.7373, 0.7373, 0.7608,\n",
      "          0.7529, 0.8471, 0.6667, 0.0000],\n",
      "         [0.0078, 0.0000, 0.0000, 0.0000, 0.2588, 0.7843, 0.8706, 0.9294,\n",
      "          0.9373, 0.9490, 0.9647, 0.9529, 0.9569, 0.8667, 0.8627, 0.7569,\n",
      "          0.7490, 0.7020, 0.7137, 0.7137, 0.7098, 0.6902, 0.6510, 0.6588,\n",
      "          0.3882, 0.2275, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1569,\n",
      "          0.2392, 0.1725, 0.2824, 0.1608, 0.1373, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000]]])\n",
      "torch.Size([1, 28, 28])\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "print(type(trainLoader))\n",
    "print(trainLoader.dataset)\n",
    "print(trainLoader.batch_size)\n",
    "print(trainLoader.num_workers)\n",
    "print(type(trainLoader.dataset[0]))#是一个tuple\n",
    "print(len(trainLoader.dataset[0]))#是一个长度为2的tuple\n",
    "print(trainLoader.dataset[0][0])\n",
    "print(trainLoader.dataset[0][0].shape)#(1,28,28)\n",
    "print(trainLoader.dataset[0][1])#标签是9\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No.of samples in train set:60000\n",
      "No.of samples in test set:10000\n",
      "Counter({9: 6000, 0: 6000, 3: 6000, 2: 6000, 7: 6000, 5: 6000, 1: 6000, 6: 6000, 4: 6000, 8: 6000})\n"
     ]
    }
   ],
   "source": [
    "print(\"No.of samples in train set:\"+str(len(trainLoader.dataset)))#len求得即使数据集得个数\n",
    "print(\"No.of samples in test set:\"+str(len(testLoader.dataset)))\n",
    "# 测试集的样本和训练集\n",
    "label=[]\n",
    "for input,labels in trainLoader.dataset:\n",
    "    label.append(labels)\n",
    "from collections import Counter\n",
    "print(Counter(label))#可以看到各个类别的样本数都是6000个相等的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f94b9f8fef0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#sample image from the dataset\n",
    "img=trainLoader.dataset[0][0]\n",
    "img_np=img.squeeze(0).numpy()#直接删去了第0维的维度\n",
    "print(img_np.shape)\n",
    "plt.imshow(img_np,cmap='gray')#将图显示出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "use_gpu=torch.cuda.is_available()\n",
    "print(use_gpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define the Autoencoder class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "class autoencoder(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(autoencoder,self).__init__()\n",
    "        self.encoder=nn.Sequential(\n",
    "            nn.Linear(28*28,100),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(100,100),\n",
    "            nn.ReLU())#将结构继续划分，并不是一直所谓的层的累加，而是分成了encoder和decoder\n",
    "        self.decoder=nn.Sequential(\n",
    "            nn.Linear(100,100),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(100,28*28),\n",
    "            nn.ReLU())\n",
    "    \n",
    "    def forward(self,x):\n",
    "        x=self.encoder(x)\n",
    "        x=self.decoder(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# for data in trainLoader:\n",
    "#     inputs,labels=data\n",
    "#     print(inputs.shape)\n",
    "#     print(labels.shape)\n",
    "# torch.Size([1024, 1, 28, 28])\n",
    "#torch.Size([1024])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0467)\n",
      "0.046666678\n"
     ]
    }
   ],
   "source": [
    "# cri=nn.MSELoss()\n",
    "# a=torch.tensor([[1,2,3],[4,5,6],[7,8,9]])\n",
    "# b=torch.tensor([[1.2,2.3,3],[4.4,5.2,6.3],[7,8,9]])\n",
    "# print(cri(a,b))\n",
    "# #它这个地方和keras的定义的MSE是一样的\n",
    "# import keras\n",
    "# import keras.backend as K\n",
    "# import keras.layers as KL\n",
    "# from keras import layers\n",
    "# from keras.layers import Input\n",
    "# from keras.layers import Lambda\n",
    "# from keras.models import Model\n",
    "# from keras.models import Sequential\n",
    "# from keras.losses import mean_squared_error #导入这个\n",
    "# import tensorflow as tf\n",
    "# import pandas as pd\n",
    "# import numpy as np\n",
    "\n",
    "# def cons_loss(input_layer,output):\n",
    "#     return K.mean(mean_squared_error(input_layer,output))  #这个地方有一个K.mean(),如果不使用K.mean(),那么返回的是一个向量\n",
    "\n",
    "# tf.InteractiveSession()\n",
    "# a=np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
    "# b=np.array([[1.2,2.3,3],[4.4,5.2,6.3],[7,8,9]])\n",
    "\n",
    "# c_loss=cons_loss(a,b)\n",
    "# print(c_loss.eval())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining function for training the network for reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definining the training routine\n",
    "def train_model_recon(model,criterion,optimizer,num_epochs):\n",
    "    start=time.time()\n",
    "    train_loss=[]\n",
    "    batch_loss=[]\n",
    "    cnt=0;\n",
    "    for epoch in range(num_epochs):\n",
    "        epochStartTime=time.time()\n",
    "        print('Epoch{}/{}'.format(epoch,num_epochs-1))#因为epoch是从0开始的，所以num_epochs需要减去1\n",
    "        #假如num_epochs=5,最终显示的是0/4,1/4,2/4,3/4,4/4\n",
    "        \n",
    "        running_loss=0.0\n",
    "        #loading data in batches\n",
    "        batch=0\n",
    "        for data in trainLoader:\n",
    "            inputs,labels=data #每次产生1024个样本\n",
    "            if use_gpu:\n",
    "                inputs,labels=Variable(inputs.view(inputs.size(0),-1).cuda()),\n",
    "                Variable(inputs.view(inputs.size(0),-1).cuda())\n",
    "            else:\n",
    "                inputs,labels=Variable(inputs.view(inputs.size(0),-1)),Variable(inputs.view(inputs.size(0),-1))\n",
    "            #labels的影响不大，主要是inputs的取值\n",
    "            #Initializing model gradients to zero\n",
    "            model.zero_grad()\n",
    "            #Data feed-forawrd through the network\n",
    "            outputs=model(inputs)\n",
    "            #Finding the MSE\n",
    "            #criterion=F.mse_loss()\n",
    "            loss=criterion(outputs,labels)\n",
    "            \n",
    "            #Backpropagating the loss and updating the model\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            #Accumulating the loss for each batch\n",
    "            running_loss+=loss.data.item()\n",
    "            batch_loss.append(loss.data.item())\n",
    "            if batch==0:\n",
    "                totalLoss=loss\n",
    "                batch+=1\n",
    "            else:\n",
    "                totalLoss+=loss\n",
    "                batch+=1\n",
    "            cnt+=1\n",
    "        epoch_loss=running_loss/60000#Total loss for one epoch\n",
    "        train_loss.append(epoch_loss)\n",
    "        \n",
    "        print(\"Epoch loss:{:.6f}\".format(epoch_loss))\n",
    "        epochTimeEnd=time.time()-epochStartTime\n",
    "        print('Epoch complete in {:.0f}m{:.0f}s'.format(epochTimeEnd//60,epochTimeEnd%60))\n",
    "        print('-'*25)\n",
    "        #plotting Loss vs Epochs\n",
    "        fig1=plt.figure(1)\n",
    "        plt.plot(range(epoch+1),train_loss,'r--',label='train')\n",
    "        if epoch==0:\n",
    "            plt.legend(loc='upper left')\n",
    "            plt.xlabel('Epochs')\n",
    "            plt.ylabel(\"Loss\")\n",
    "        fig1.savefig(\"aeRecon_LossPlot.png\")\n",
    "        fig1=plt.figure(2)\n",
    "        plt.plot(range(cnt),batch_loss,'g--',label='train')\n",
    "        if epoch==0:\n",
    "            plt.legend(loc='upper left')\n",
    "            plt.xlabel('batchs')\n",
    "            plt.ylabel(\"batch_Loss\")\n",
    "        fig1.savefig(\"aeRecon_batchLossPlot.png\")\n",
    "    print(\"总的迭代次数={}\".format(cnt))    \n",
    "    time_elapsed=time.time()-start\n",
    "    print('Training commplete in {:.0f}m{:.0f}'.format(time_elapsed//60,time_elapsed%60))\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model initialization and training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "autoencoder(\n",
      "  (encoder): Sequential(\n",
      "    (0): Linear(in_features=784, out_features=100, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=100, out_features=100, bias=True)\n",
      "    (3): ReLU()\n",
      "  )\n",
      "  (decoder): Sequential(\n",
      "    (0): Linear(in_features=100, out_features=100, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=100, out_features=784, bias=True)\n",
      "    (3): ReLU()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "net=autoencoder()\n",
    "print(net)\n",
    "if use_gpu:\n",
    "    net=net.cuda()\n",
    "init_weights=copy.deepcopy(net.encoder[0].weight.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 4.4145e-01,  2.4103e-01,  1.6920e-01,  3.0202e-01, -3.4940e-01,\n",
      "         -1.2305e-01, -5.6889e-01,  8.9676e-02, -9.5698e-01,  5.2230e-01,\n",
      "         -7.9785e-03, -1.3330e+00,  3.5441e-01, -1.2320e-01, -1.6783e-01,\n",
      "          6.1074e-01,  3.5396e-01, -1.5136e-01,  3.7355e-01,  3.4734e-01,\n",
      "         -3.4029e-01,  6.5080e-01, -1.5417e-01, -1.1185e+00,  7.1391e-01,\n",
      "         -2.8046e-01, -5.5633e-01,  2.5062e-01, -8.0416e-02, -4.6037e-01,\n",
      "          4.6330e-04,  3.8377e-01, -1.0792e+00, -1.1374e+00, -1.2033e+00,\n",
      "          7.9430e-02, -4.3411e-01,  4.0356e-01,  2.8802e-02,  1.0820e-01,\n",
      "         -3.6238e-01,  1.3675e-01,  8.8382e-02,  4.5258e-01, -6.0822e-01,\n",
      "          1.7649e+00, -1.1446e-01,  7.4526e-02, -2.0948e-01, -1.0546e-01,\n",
      "          5.2815e-01,  3.6462e-02, -2.5897e-01, -1.7104e-01, -1.7974e+00,\n",
      "         -8.5465e-01,  5.8211e-01,  8.1504e-01,  1.7434e+00,  6.7885e-01,\n",
      "         -2.0474e-01,  4.1012e-01, -4.7928e-02, -3.8415e-01,  1.2486e+00,\n",
      "         -1.8439e-01,  3.5672e-01,  1.2654e-02, -4.2544e-01, -1.2124e+00,\n",
      "          7.1186e-01,  9.0334e-01,  7.5435e-01,  1.2458e-01,  1.0405e-01,\n",
      "         -3.3146e-01,  3.6595e-01, -1.3647e+00,  4.0045e-01,  5.1927e-01,\n",
      "         -9.0778e-01,  9.0125e-02, -8.5943e-02,  5.1903e-01, -1.2245e+00,\n",
      "          5.6228e-01,  1.0287e-01, -9.5536e-01,  2.0262e-01,  3.4400e-01,\n",
      "         -1.3748e-02,  1.0697e+00, -8.1273e-01,  2.0030e-01,  2.8612e-01,\n",
      "          1.5544e+00,  1.4005e-01,  1.4546e-01, -1.0850e+00, -1.2212e-01]],\n",
      "       grad_fn=<AddmmBackward>)\n",
      "torch.Size([1, 100])\n"
     ]
    }
   ],
   "source": [
    "# print(net.encoder)#上面的网络结构有0，1，2，3\n",
    "# print(type(net.encoder))\n",
    "# print(net.encoder[0])\n",
    "# print(type(net.encoder[0]))\n",
    "# #注意这个取层的方式\n",
    "# print(net.encoder[0].weight.data)\n",
    "# m=net.encoder[0]#直接把第一层取出来，然后去预测\n",
    "# input=torch.randn(1,784)#这样确实很方便\n",
    "# output=m(input)\n",
    "# print(output)\n",
    "# print(output.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch0/4\n",
      "Epoch loss:0.000122\n",
      "Epoch complete in 0m7s\n",
      "-------------------------\n",
      "Epoch1/4\n",
      "Epoch loss:0.000086\n",
      "Epoch complete in 0m6s\n",
      "-------------------------\n",
      "Epoch2/4\n",
      "Epoch loss:0.000068\n",
      "Epoch complete in 0m6s\n",
      "-------------------------\n",
      "Epoch3/4\n",
      "Epoch loss:0.000061\n",
      "Epoch complete in 0m7s\n",
      "-------------------------\n",
      "Epoch4/4\n",
      "Epoch loss:0.000054\n",
      "Epoch complete in 0m7s\n",
      "-------------------------\n",
      "总的迭代次数=295\n",
      "Training commplete in 0m34\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"
    },
    {
     "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": [
    "criterion=nn.MSELoss()\n",
    "optimizer=optim.SGD(net.parameters(),lr=0.5,momentum=0.9)\n",
    "net=train_model_recon(net,criterion,optimizer,num_epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "TestImg=testLoader.dataset[0][0]\n",
    "if use_gpu:\n",
    "    outputImg=net(Variable(TestImg.view(TestImg.size(0),-1)).cuda())#预测的时候需要将它转化成Variable类型，才能预测\n",
    "else:\n",
    "    outputImg=net(Variable(TestImg.view(TestImg.size(0),-1)))\n",
    "\n",
    "outputImg=outputImg.data.view(-1,28,28).cpu()\n",
    "\n",
    "fig=plt.figure()\n",
    "plot=fig.add_subplot(1,2,1)\n",
    "img=np.array(TestImg.numpy())[0]\n",
    "plot.set_title(\"Oringal Image\")\n",
    "imgplot=plt.imshow(img,cmap=\"gray\")\n",
    "\n",
    "plot=fig.add_subplot(1,2,2)\n",
    "img=np.array(outputImg.numpy())[0]\n",
    "plot.set_title(\"Reconstructed Image\")\n",
    "imgplot=plt.imshow(img,cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of weights of encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "trained_weights=copy.deepcopy(net.encoder[0].weight.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_weights=(1+init_weights)*127.5\n",
    "trained_weights=(1+trained_weights)*127.5\n",
    "\n",
    "if use_gpu:\n",
    "    init_weights=init_weights.view(-1,280,280).byte().cpu()\n",
    "    trained_weights=trained_weights.view(-1,280,280).byte().cpu()\n",
    "else:\n",
    "    init_weights=init_weights.view(-1,280,280).byte()\n",
    "    trained_weights=trained_weights.view(-1,280,280).byte()\n",
    "    \n",
    "d_weights=init_weights-trained_weights\n",
    "\n",
    "fig=plt.figure()\n",
    "plot=fig.add_subplot(1,3,1)\n",
    "img=np.array(init_weights.numpy())[0]\n",
    "plot.set_title(\"Initial weights\")\n",
    "imgplot=plt.imshow(img,cmap=\"gray\")\n",
    "\n",
    "plot=fig.add_subplot(1,3,2)\n",
    "img=np.array(trained_weights.numpy())[0]\n",
    "plot.set_title(\"trained weights\")\n",
    "imgplot=plt.imshow(img,cmap=\"gray\")\n",
    "\n",
    "plot=fig.add_subplot(1,3,3)\n",
    "img=np.array(d_weights.numpy())[0]\n",
    "plot.set_title(\"weights update\")\n",
    "imgplot=plt.imshow(img,cmap=\"gray\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Sequential(\n",
      "    (0): Linear(in_features=784, out_features=100, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=100, out_features=100, bias=True)\n",
      "    (3): ReLU()\n",
      "  )\n",
      ")\n",
      "<generator object Module.children at 0x7f943fcb21a8>\n",
      "[Sequential(\n",
      "  (0): Linear(in_features=784, out_features=100, bias=True)\n",
      "  (1): ReLU()\n",
      "  (2): Linear(in_features=100, out_features=100, bias=True)\n",
      "  (3): ReLU()\n",
      "), Sequential(\n",
      "  (0): Linear(in_features=100, out_features=100, bias=True)\n",
      "  (1): ReLU()\n",
      "  (2): Linear(in_features=100, out_features=784, bias=True)\n",
      "  (3): ReLU()\n",
      ")]\n",
      "[Sequential(\n",
      "  (0): Linear(in_features=784, out_features=100, bias=True)\n",
      "  (1): ReLU()\n",
      "  (2): Linear(in_features=100, out_features=100, bias=True)\n",
      "  (3): ReLU()\n",
      ")]\n",
      "Sequential(\n",
      "  (0): Linear(in_features=784, out_features=100, bias=True)\n",
      "  (1): ReLU()\n",
      "  (2): Linear(in_features=100, out_features=100, bias=True)\n",
      "  (3): ReLU()\n",
      ")\n",
      "Sequential(\n",
      "  (0): Linear(in_features=784, out_features=100, bias=True)\n",
      "  (1): ReLU()\n",
      "  (2): Linear(in_features=100, out_features=100, bias=True)\n",
      "  (3): ReLU()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "dd=nn.Sequential(*list(net.children())[:-1])\n",
    "print(dd)\n",
    "print(net.children())\n",
    "print(list(net.children()))\n",
    "print(list(net.children())[:-1])\n",
    "print(*(list(net.children())[:-1]))\n",
    "print((*(list(net.children()))[:-1]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Autoencoder for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Sequential(\n",
      "    (0): Linear(in_features=784, out_features=100, bias=True)\n",
      "    (1): ReLU()\n",
      "    (2): Linear(in_features=100, out_features=100, bias=True)\n",
      "    (3): ReLU()\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=100, out_features=10, bias=True)\n",
      "    (1): LogSoftmax()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "new_classifier=nn.Sequential(*list(net.children())[:-1])\n",
    "net=new_classifier\n",
    "net.add_module(\"classifier\",nn.Sequential(nn.Linear(100,10),nn.LogSoftmax()))#这个地方nn.Logsoftmax()是一个类，相当于一个激活函数\n",
    "print(net)#这里nn.logsoftmax并不是损失函数\n",
    "if use_gpu:\n",
    "    net=net.cuda()\n",
    "cll_weights=copy.deepcopy(net[0][0].weight.data)\n",
    "init_classifier_weights=copy.deepcopy(net.classifier[0].weight.data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining function for training the network for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining function for training the network for classification\n",
    "def train_model_classif(model,criterion,optimizer,num_epochs):\n",
    "    start=time.time()\n",
    "    train_loss=[]#list for saving the loss per epoch\n",
    "    train_acc=[]#list for saving the accuracy per epoch\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        epochStartTime=time.time()\n",
    "        print('Epoch{}/{}'.format(epoch,num_epochs-1))\n",
    "        \n",
    "        running_loss=0.0\n",
    "        running_corrects=0.0\n",
    "        #loading data in batches\n",
    "        batch=0\n",
    "        for data in trainLoader:\n",
    "            inputs,labels=data\n",
    "            if use_gpu:\n",
    "                inputs,labels=Variable(inputs.view(inputs.size(0),-1).cuda()),\n",
    "                Variable(labels.cuda())\n",
    "            else:\n",
    "                inputs,labels=Variable(inputs.view(inputs.size(0),-1)),Variable(labels)\n",
    "            #Initializing model gradients to zero\n",
    "            model.zero_grad()\n",
    "            #Data feed-forawrd through the network\n",
    "            outputs=model(inputs)\n",
    "            #Finding the MSE\n",
    "            #criterion=F.mse_loss()\n",
    "            loss=criterion(outputs,labels)\n",
    "            \n",
    "            #Backpropagating the loss and updating the model\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            #Accumulating the loss for each batch\n",
    "            running_loss+=loss.data.item()\n",
    "            _,preds=torch.max(outputs.data,1)\n",
    "            #Acculating correct predictions for each batch\n",
    "            running_corrects+=torch.sum(preds==labels.data)\n",
    "        \n",
    "\n",
    "        epoch_loss=running_loss/60000.0#Total loss for one epoch\n",
    "        train_loss.append(epoch_loss)#saving the loss over plotting\n",
    "        epoch_acc=float(running_corrects)/60000.0 #Average accuracy for\n",
    "        train_acc.append(epoch_acc)#saving\n",
    "        \n",
    "        print(\"Epoch loss:{:.6f},Epoch Accuracy:{:.6f}\".format(epoch_loss,epoch_acc))\n",
    "        epochTimeEnd=time.time()-epochStartTime\n",
    "        print('Epoch complete in {:.0f}m{:.0f}s'.format(epochTimeEnd//60,epochTimeEnd%60))\n",
    "        print('-'*25)\n",
    "        #plotting Loss vs Epochs\n",
    "        fig1=plt.figure(1)\n",
    "        plt.plot(range(epoch+1),train_loss,'r--',label='train')\n",
    "        if epoch==0:\n",
    "            plt.legend(loc='upper left')\n",
    "            plt.xlabel('Epochs')\n",
    "            plt.ylabel(\"Loss\")\n",
    "        fig1.savefig(\"aeRecon_LossPlot.png\")\n",
    "        fig1=plt.figure(2)\n",
    "        plt.plot(range(epoch+1),train_acc,'g--',label='train')\n",
    "        if epoch==0:\n",
    "            plt.legend(loc='upper left')\n",
    "            plt.xlabel('Epochs')\n",
    "            plt.ylabel(\"Accuracy\")\n",
    "        fig1.savefig(\"aeAccuracyPlot.png\")\n",
    "    time_elapsed=time.time()-start\n",
    "    print('Training commplete in {:.0f}m{:.0f}'.format(time_elapsed//60,time_elapsed%60))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch0/4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zhangjingxiao/.conda/envs/py36/lib/python3.6/site-packages/torch/nn/modules/container.py:100: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  input = module(input)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch loss:0.000427,Epoch Accuracy:0.848083\n",
      "Epoch complete in 0m6s\n",
      "-------------------------\n",
      "Epoch1/4\n",
      "Epoch loss:0.000420,Epoch Accuracy:0.851450\n",
      "Epoch complete in 0m6s\n",
      "-------------------------\n",
      "Epoch2/4\n",
      "Epoch loss:0.000415,Epoch Accuracy:0.852933\n",
      "Epoch complete in 0m6s\n",
      "-------------------------\n",
      "Epoch3/4\n",
      "Epoch loss:0.000409,Epoch Accuracy:0.854217\n",
      "Epoch complete in 0m6s\n",
      "-------------------------\n",
      "Epoch4/4\n",
      "Epoch loss:0.000404,Epoch Accuracy:0.856500\n",
      "Epoch complete in 0m6s\n",
      "-------------------------\n",
      "Training commplete in 0m31\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"
    },
    {
     "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": [
    "# Defining loss function and training the network\n",
    "criterion=nn.NLLLoss()\n",
    "optimizer=optim.SGD(net.parameters(),lr=0.01,momentum=0.9)\n",
    "net=train_model_classif(net,criterion,optimizer,num_epochs=5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance evaluation of trained network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zhangjingxiao/.conda/envs/py36/lib/python3.6/site-packages/torch/nn/modules/container.py:100: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  input = module(input)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set=tensor(0.8317)\n"
     ]
    }
   ],
   "source": [
    "# Finding testing accuracy\n",
    "test_running_corr=0\n",
    "#loading data in batches\n",
    "batches=0\n",
    "runing_corrects=0.0\n",
    "for tsData in testLoader:\n",
    "    inputs,labels=tsData\n",
    "    #wrap them in Variable\n",
    "    if use_gpu:\n",
    "        inputs,labels=Variable(inputs.view(inputs.size(0),-1).cuda()),Variable(labels.cuda())\n",
    "    else:\n",
    "        inputs,labels=Variable(inputs.view(inputs.size(0),-1)),Variable(labels)\n",
    "    #Feedforwarded train data batch through model\n",
    "    output=net(inputs)\n",
    "    _,preds=output.data.max(1)\n",
    "    runing_corrects+=torch.sum(preds==labels.data)\n",
    "\n",
    "#Finding total number of correct predictions\n",
    "ts_acc=runing_corrects/10000.0\n",
    "print(\"Accuracy on test set=\"+str(ts_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of weights of encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "cll_weightsl_ft=copy.deepcopy(net[0][0].weight.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cll_weights=(1+cll_weights)*127.5\n",
    "cll_weights_ft=(1+cll_weightsl_ft)*127.5\n",
    "if use_gpu:\n",
    "    cll_weights=cll_weights.view(-1,280,280).byte().cpu()\n",
    "    cll_weights_ft=cll_weights_ft.view(-1,280,280).byte().cpu()\n",
    "else:\n",
    "    cll_weights=cll_weights.view(-1,280,280).byte()\n",
    "    cll_weights_ft=cll_weights_ft.view(-1,280,280).byte()\n",
    "    \n",
    "d_weights=cll_weights-cll_weights_ft\n",
    "\n",
    "fig=plt.figure()\n",
    "plot=fig.add_subplot(1,3,1)\n",
    "img=np.array(cll_weights.numpy())[0]\n",
    "plot.set_title(\"Encoder weights\")\n",
    "imgplot=plt.imshow(img,cmap=\"gray\")\n",
    "\n",
    "plot=fig.add_subplot(1,3,2)\n",
    "img=np.array(cll_weights_ft.numpy())[0]\n",
    "plot.set_title(\"Finetuned weights\")\n",
    "imgplot=plt.imshow(img,cmap=\"gray\")\n",
    "\n",
    "plot=fig.add_subplot(1,3,3)\n",
    "img=np.array(d_weights.numpy())[0]\n",
    "plot.set_title(\"weights update\")\n",
    "imgplot=plt.imshow(img,cmap=\"gray\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualzation of weights of classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "trained_classifier_weights=copy.deepcopy(net.classifier[0].weight.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_classifier_weights=(1+init_classifier_weights)*255\n",
    "trained_classifier_weights=(1+trained_classifier_weights)*255\n",
    "\n",
    "if use_gpu:\n",
    "    init_classifier_weights=init_classifier_weights.view(-1,40,25).byte().cpu()\n",
    "    trained_classifier_weights=trained_classifier_weights.view(-1,40,25).byte().cpu()\n",
    "else:\n",
    "    init_classifier_weights=init_classifier_weights.view(-1,40,25).byte()\n",
    "    trained_classifier_weights=trained_classifier_weights.view(-1,40,25).byte()\n",
    "    \n",
    "d_weights=init_classifier_weights-trained_classifier_weights\n",
    "\n",
    "fig=plt.figure()\n",
    "plot=fig.add_subplot(1,3,1)\n",
    "img=np.array(init_classifier_weights.numpy())[0]\n",
    "plot.set_title(\"init_classifierial weights\")\n",
    "imgplot=plt.imshow(img,cmap=\"gray\")\n",
    "\n",
    "plot=fig.add_subplot(1,3,2)\n",
    "img=np.array(trained_classifier_weights.numpy())[0]\n",
    "plot.set_title(\"trained_classifier weights\")\n",
    "imgplot=plt.imshow(img,cmap=\"gray\")\n",
    "\n",
    "plot=fig.add_subplot(1,3,3)\n",
    "img=np.array(d_weights.numpy())[0]\n",
    "plot.set_title(\"weights update\")\n",
    "imgplot=plt.imshow(img,cmap=\"gray\")\n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python(py36)",
   "language": "python",
   "name": "py36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}