Deep Convolutional Generator Adversarial Network.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function # print fun transform to 3.0 to 2.6+ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn # neural network \n",
    "import torch.nn.parallel # run paralllel execution\n",
    "import torch.optim as optim # optimizer\n",
    "import torch.utils.data # import data in mini batches using Dataloader function\n",
    "import torchvision.datasets as dst # datser\n",
    "import torchvision.transforms as transforms # create dependencies between 2 networks\n",
    "import torchvision.utils as vutils # visualization \n",
    "from torch.autograd import Variable # for working torch tensor we need torch variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_szie = 64\n",
    "image_size = 64 # generated image size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a list of transformations (scaling, tensor conversion, normalization) to apply to the input images.\n",
    "transform = transforms.Compose([transforms.Resize(image_size) , \n",
    "                                transforms.ToTensor() , \n",
    "                                transforms.Normalize((0.5 , 0.5 , 0.5) , (0.5 , 0.5 , 0.5))\n",
    "                               ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "dataset = dst.CIFAR10(root = 'data' , download = True , transform = transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_szie , shuffle=True , num_workers=2)\n",
    "# num worker means CPU multicore use for loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize th weight init function which takes neural network as input\n",
    "# and set all weights of Neural Network\n",
    "\n",
    "def weights_init(m):\n",
    "    classname = m.__class__.__name__ # getting class name\n",
    "    \n",
    "    if classname.find('Conv') != -1 : # if conv then set this weights\n",
    "        m.weight.data.normal_(0.0 , 0.02)\n",
    "    elif classname.find('Batchnorm') != -1: # if batchnoram then set this weights\n",
    "        m.weight.data.normal_(1.0 , 0.02)\n",
    "        m.bias.data.fill_(0) # bias fil with zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generator Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class G(nn.Module):\n",
    "    \n",
    "    def __init__(self): \n",
    "        super(G , self).__init__() # calling super classs methos on this class future object 1st module\n",
    "        \n",
    "        # 2 nd meta module which consists of many layers or modules.\n",
    "        self.main = nn.Sequential(\n",
    "        \n",
    "        # we are using inverse conv. layer for Generator because it genrates the images.\n",
    "            \n",
    "        # ConvTranspose2d(input , output , kernelsize , strides , padding , bias)    \n",
    "        nn.ConvTranspose2d(100 , 512 , 4 , 1 , 0 , bias = True),\n",
    "        nn.BatchNorm2d(512), # we batchnormalize the output os 512 neurons\n",
    "        nn.ReLU(True),\n",
    "        \n",
    "        nn.ConvTranspose2d(512 , 256 , 4 , 2 , 1 , bias = True),\n",
    "        nn.BatchNorm2d(256),\n",
    "        nn.ReLU(True),\n",
    "        \n",
    "        nn.ConvTranspose2d(256 , 128 , 4 , 2 , 1 , bias = True),\n",
    "        nn.BatchNorm2d(128),\n",
    "        nn.ReLU(True),\n",
    "        \n",
    "        nn.ConvTranspose2d(128 , 64 , 4 , 2 , 1 , bias = True),\n",
    "        nn.BatchNorm2d(64),\n",
    "        nn.ReLU(True),\n",
    "        \n",
    "        #last layer of Inverse conv. layer which genrate image so we set output = 3 which is color channels.\n",
    "        nn.ConvTranspose2d(64 , 3 , 4 , 2 , 1 , bias = True),\n",
    "        nn.Tanh() ) # tanh value between -1 to 1 and centerd near zero.\n",
    "        \n",
    "        # We have to generate image linearity between -1 and +1 and centerd zero \n",
    "        # why is that ?\n",
    "        # We have to make standard same as images of dataset becuase this G. images goes into D. network \n",
    "        \n",
    "    # forward propogation\n",
    "    \n",
    "    def forward(self , input):  # input is 100x100 noise vector which is useful for creating images\n",
    "        output = self.main(input)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Object of Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G(\n",
       "  (main): Sequential(\n",
       "    (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1))\n",
       "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU(inplace=True)\n",
       "    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (5): ReLU(inplace=True)\n",
       "    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (8): ReLU(inplace=True)\n",
       "    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (11): ReLU(inplace=True)\n",
       "    (12): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (13): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netG = G() \n",
    "netG.apply(weights_init) # initilaize the weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discriminator Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class D(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(D , self).__init__()\n",
    "        \n",
    "        self.main = nn.Sequential(\n",
    "            \n",
    "        # Conv2d(input , output , kernelsize , stride , padding , bias)\n",
    "        nn.Conv2d(3 , 64 , 4 , 2 , 1, bias = True),\n",
    "            \n",
    "        # here we are not normalized first network and we also apply leakyrelu activaiotn fucntion\n",
    "        nn.LeakyReLU(0.2 , inplace = True),\n",
    "            \n",
    "        nn.Conv2d(64 , 128 , 4 , 2 , 1 , bias = True),\n",
    "        nn.BatchNorm2d(128),\n",
    "        nn.LeakyReLU(0.2 , True),\n",
    "            \n",
    "        nn.Conv2d(128 , 256 , 4 , 2 , 1 , bias = True),\n",
    "        nn.BatchNorm2d(256),\n",
    "        nn.LeakyReLU(0.2 , True),\n",
    "        \n",
    "        nn.Conv2d(256 , 512 , 4 , 2 , 1 , bias = True),\n",
    "        nn.BatchNorm2d(512),\n",
    "        nn.LeakyReLU(0.2 , True),    \n",
    "        \n",
    "        # final layer is 1 neurons because discriminator discriminates the image wheather it is accepted ot not\n",
    "        # 1 - Accepted\n",
    "        # 0 - Rejected\n",
    "        # finally we choose sigmoid becuase it genrates o/p between 0 and 1.\n",
    "        \n",
    "        nn.Conv2d(512 , 1 , 4 , 1 , 0 , bias = True), \n",
    "        nn.Sigmoid()\n",
    "        )\n",
    "        \n",
    "    def forward(self ,input):\n",
    "        output = self.main(input)\n",
    "        return output.view(-1) # d network is CNN so at last we flatten the layer so we simplt do that by this trick."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Object of Discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "D(\n",
       "  (main): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (1): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (4): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (10): LeakyReLU(negative_slope=0.2, inplace=True)\n",
       "    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1))\n",
       "    (12): Sigmoid()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netD = D()\n",
    "netD.apply(weights_init)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training The Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating loss \n",
    "# In this case we are using binary cross loss\n",
    "\n",
    "criterion = nn.BCELoss()\n",
    "# Adam( parameters , learnning rate , coefficients)\n",
    "optimizerG = optim.Adam(netG.parameters() , lr = 0.0002 , betas = (0.5 , 0.999))\n",
    "optimizerD = optim.Adam(netD.parameters() , lr = 0.0002 , betas = (0.5 , 0.999))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 / 10][0 / 782] Loss_D: 4.842878098543224e-08 Loss_G: 34.51014709472656\n",
      "[0 / 10][1 / 782] Loss_D: 4.153714598942315e-07 Loss_G: 33.709407806396484\n",
      "[0 / 10][2 / 782] Loss_D: 5.215413807491132e-07 Loss_G: 33.859432220458984\n",
      "[0 / 10][3 / 782] Loss_D: 3.4645222513063345e-07 Loss_G: 33.657379150390625\n",
      "[0 / 10][4 / 782] Loss_D: 1.4528640690514294e-07 Loss_G: 33.79772186279297\n",
      "[0 / 10][5 / 782] Loss_D: 3.911559929292707e-07 Loss_G: 33.894737243652344\n",
      "[0 / 10][6 / 782] Loss_D: 1.3597320958069758e-07 Loss_G: 33.722450256347656\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-35-e4bc162f708b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m         \u001b[1;31m# creating noise images for every batch with size of 100. 100 features of 1x1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m         \u001b[0mnoise\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mVariable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m         \u001b[0mfake\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnetG\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnoise\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# input noise in G. network it output fake image that is out input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m         \u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mVariable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# rejected so zeros array\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnetD\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfake\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# this means don't calculat gradient for this output save computational power for D. net only\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    548\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    549\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 550\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    551\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    552\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-20-658cc2386979>\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# input is 100x100 noise vector which is useful for creating images\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m         \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    548\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    549\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 550\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    551\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    552\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\container.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m     98\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     99\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 100\u001b[1;33m             \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    101\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    102\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    548\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    549\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 550\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    551\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    552\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input, output_size)\u001b[0m\n\u001b[0;32m    792\u001b[0m         return F.conv_transpose2d(\n\u001b[0;32m    793\u001b[0m             \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpadding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 794\u001b[1;33m             output_padding, self.groups, self.dilation)\n\u001b[0m\u001b[0;32m    795\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    796\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for epoch in range(10):\n",
    "    # i index , data - mininbatches\n",
    "    # collect data from dataloader and 0 staring range\n",
    "    \n",
    "    for i , data in enumerate(dataloader , 0):\n",
    "        \n",
    "        ### 1 St Step\n",
    "        \n",
    "        netD.zero_grad()\n",
    "        \n",
    "        ## Training on Real Images\n",
    "        \n",
    "        real , _ = data\n",
    "        input = Variable(real) # Varibale special type which combinaitonof tensor and gradient\n",
    "        target = Variable(torch.ones(input.size()[0])) # accepeted so ones array\n",
    "        output = netD(input)\n",
    "        errD_real = criterion(output , target)\n",
    "        \n",
    "        ## Training on Fake Images\n",
    "        \n",
    "        # creating noise images for every batch with size of 100. 100 features of 1x1\n",
    "        noise = Variable(torch.randn(input.size()[0] , 100 , 1 , 1))\n",
    "        fake = netG(noise) # input noise in G. network it output fake image that is out input\n",
    "        target = Variable(torch.zeros(input.size()[0])) # rejected so zeros array\n",
    "        output = netD(fake.detach()) # this means don't calculat gradient for this output save computational power for D. net only\n",
    "        errD_fake = criterion(output , target)\n",
    "        \n",
    "        ## Backpropogating the error in D. network\n",
    "        \n",
    "        errD = errD_fake + errD_real # total loss\n",
    "        errD.backward() # backpropogation calculate loss\n",
    "        optimizerD.step()  # update the weights\n",
    "        \n",
    "        ### 2nd Step\n",
    "        \n",
    "        ## Train Generator\n",
    "        \n",
    "        netG.zero_grad()\n",
    "        target = Variable(torch.ones(input.size()[0]))\n",
    "        output = netD(fake)\n",
    "        errG = criterion(output , target)\n",
    "        \n",
    "        ## backpropogation\n",
    "        \n",
    "        errG.backward()\n",
    "        optimizerG.step()\n",
    "        \n",
    "        \n",
    "        # printing some information\n",
    "        \n",
    "        print('[{} / {}][{} / {}] Loss_D: {} Loss_G: {}'.format(epoch, 10, i, len(dataloader), errD.data, errG.data))\n",
    "        \n",
    "        # saving the images after 100 steps.\n",
    "        \n",
    "        if i % 100 == 0:\n",
    "            \n",
    "            vutils.save_image(real , '%s/real_sample.png' % 'results' , normalize = True)\n",
    "            \n",
    "            fake = netG(noise)\n",
    "            \n",
    "            vutils.save_image(fake.data , '%s/fake_sample_epoch_%03d.png' % ('results' , epoch) , normalize = True)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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