I hereby claim:
- I am exoceus on github.
- I am jatinm (https://keybase.io/jatinm) on keybase.
- I have a public key ASAUIjspKmZ4Ez0B6utqyJFdpT_3KWLj-1Togaz1jAO-Ngo
To claim this, I am signing this object:
# Number of workers for dataloader | |
workers = 4 | |
# Batch size during training | |
batch_size = 128 | |
# Spatial size of training images. All images will be resized to this | |
# size using a transformer. | |
image_size = 64 |
# Training Loop | |
# Lists to keep track of progress | |
img_list = [] | |
G_losses = [] | |
D_losses = [] | |
iters = 0 | |
print("Starting Training Loop...") | |
# For each epoch |
# Initialize BCELoss function | |
criterion = nn.BCELoss() | |
# Create batch of latent vectors that we will use to visualize | |
# the progression of the generator | |
fixed_noise = torch.randn(64, nz, 1, 1, device=device) | |
# Establish convention for real and fake labels during training | |
real_label = 1 | |
fake_label = 0 |
class Generator(nn.Module): | |
def __init__(self, ngpu): | |
super(Generator, self).__init__() | |
self.ngpu = ngpu | |
self.main = nn.Sequential( | |
# input is Z, going into a convolution | |
nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False), | |
nn.BatchNorm2d(ngf * 8), | |
nn.ReLU(True), | |
# state size. (ngf*8) x 4 x 4 |
class Discriminator(nn.Module): | |
def __init__(self, ngpu): | |
super(Discriminator, self).__init__() | |
self.ngpu = ngpu | |
self.main = nn.Sequential( | |
# input is (nc) x 64 x 64 | |
nn.Conv2d(nc, ndf, 4, 2, 1, bias=False), | |
nn.LeakyReLU(0.2, inplace=True), | |
# state size. (ndf) x 32 x 32 | |
nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False), |
# Getting dependencies | |
import time | |
import numpy as np | |
import collections | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.optim as optim |
I hereby claim:
To claim this, I am signing this object: