# Jovian Commit Essentials
# Please retain and execute this cell without modifying the contents for `jovian.commit` to work
!pip install jovian --upgrade -q
import jovian
jovian.set_project('deep-convolutional-gans')
jovian.set_colab_id('1OJEQiXUxAy6TkfsasO_TjjZBDQWg8NFZ')

import torch 
import torch.nn as nn
import numpy as np
import torchvision.transforms as transforms
import torchvision.datasets as dataset
from   torch.utils.data import DataLoader
import torch.optim as optim

def initialize_weights(model):
  for m in model.modules():
    if isinstance(m,(nn.Conv2d,nn.ConvTranspose2d,nn.BatchNorm2d)):
      nn.init.normal_(m.weight.data, 0.0,0.02)

from torch.nn.modules.activation import Sigmoid
class Descriminator(nn.Module):
  def __init__(self,in_ch,feature_d):
    super().__init__()
    self.desc = nn.Sequential(
        nn.Conv2d(in_ch,feature_d,kernel_size=4,stride=2,padding=1),
        nn.LeakyReLU(0.2),
        self._block(feature_d,feature_d*2,4,2,1),
        self._block(feature_d*2,feature_d*4,4,2,1),
        self._block(feature_d*4,feature_d*8,4,2,1),
        nn.Conv2d(feature_d*8,1,4,1,0),
        nn.Sigmoid()
    )
  def _block(self,in_ch,out_ch,kernel_size,stride,padding):  
    return nn.Sequential(
        nn.Conv2d(in_ch,out_ch,kernel_size,stride,padding),
        nn.BatchNorm2d(out_ch),
        nn.LeakyReLU(0.2)
    )
  def forward(self,input):
    return self.desc(input)