Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p torch
Sebastian Raschka CPython 3.7.3 IPython 7.6.1 torch 1.2.0
Implementation of a very simple/rudimentary Wasserstein GAN using just fully connected layers.
The Wasserstein GAN is based on the paper
The main differences to a regular GAN are annotated in the code. In short, the main differences are
import time
import numpy as np
import torch
import torch.nn.functional as F
from torchvision import datasets
from torchvision import transforms
import torch.nn as nn
from torch.utils.data import DataLoader
if torch.cuda.is_available():
torch.backends.cudnn.deterministic = True
##########################
### SETTINGS
##########################
# Device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Hyperparameters
random_seed = 0
generator_learning_rate = 0.0005
discriminator_learning_rate = 0.0005
NUM_EPOCHS = 100
BATCH_SIZE = 128
LATENT_DIM = 50
IMG_SHAPE = (1, 28, 28)
IMG_SIZE = 1
for x in IMG_SHAPE:
IMG_SIZE *= x
## WGAN-specific settings
num_iter_critic = 5
weight_clip_value = 0.01
##########################
### MNIST DATASET
##########################
# Note transforms.ToTensor() scales input images
# to 0-1 range
train_dataset = datasets.MNIST(root='data',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = datasets.MNIST(root='data',
train=False,
transform=transforms.ToTensor())
train_loader = DataLoader(dataset=train_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
test_loader = DataLoader(dataset=test_dataset,
batch_size=BATCH_SIZE,
shuffle=False)
# Checking the dataset
for images, labels in train_loader:
print('Image batch dimensions:', images.shape)
print('Image label dimensions:', labels.shape)
break
Image batch dimensions: torch.Size([128, 1, 28, 28]) Image label dimensions: torch.Size([128])
##########################
### MODEL
##########################
def wasserstein_loss(y_true, y_pred):
return torch.mean(y_true * y_pred)
class GAN(torch.nn.Module):
def __init__(self):
super(GAN, self).__init__()
self.generator = nn.Sequential(
nn.Linear(LATENT_DIM, 128),
nn.LeakyReLU(inplace=True),
#nn.Dropout(p=0.5),
nn.Linear(128, IMG_SIZE),
nn.Tanh()
)
self.discriminator = nn.Sequential(
nn.Linear(IMG_SIZE, 128),
nn.LeakyReLU(inplace=True),
#nn.Dropout(p=0.5),
nn.Linear(128, 1),
nn.Sigmoid()
)
def generator_forward(self, z):
img = self.generator(z)
return img
def discriminator_forward(self, img):
pred = model.discriminator(img)
return pred.view(-1)
torch.manual_seed(random_seed)
model = GAN()
model = model.to(device)
optim_gener = torch.optim.Adam(model.generator.parameters(), lr=generator_learning_rate)
optim_discr = torch.optim.Adam(model.discriminator.parameters(), lr=discriminator_learning_rate)
start_time = time.time()
discr_costs = []
gener_costs = []
for epoch in range(NUM_EPOCHS):
model = model.train()
for batch_idx, (features, targets) in enumerate(train_loader):
features = (features - 0.5)*2.
features = features.view(-1, IMG_SIZE).to(device)
targets = targets.to(device)
# Regular GAN:
# valid = torch.ones(targets.size(0)).float().to(device)
# fake = torch.zeros(targets.size(0)).float().to(device)
# WGAN:
valid = -(torch.ones(targets.size(0)).float()).to(device)
fake = torch.ones(targets.size(0)).float().to(device)
### FORWARD AND BACK PROP
# --------------------------
# Train Generator
# --------------------------
# Make new images
z = torch.zeros((targets.size(0), LATENT_DIM)).uniform_(-1.0, 1.0).to(device)
generated_features = model.generator_forward(z)
# Loss for fooling the discriminator
discr_pred = model.discriminator_forward(generated_features)
# Regular GAN:
# gener_loss = F.binary_cross_entropy_with_logits(discr_pred, valid)
# WGAN:
gener_loss = wasserstein_loss(valid, discr_pred)
optim_gener.zero_grad()
gener_loss.backward()
optim_gener.step()
# --------------------------
# Train Discriminator
# --------------------------
# WGAN: 5 loops for discriminator
for _ in range(num_iter_critic):
discr_pred_real = model.discriminator_forward(features.view(-1, IMG_SIZE))
# Regular GAN:
# real_loss = F.binary_cross_entropy_with_logits(discr_pred_real, valid)
# WGAN:
real_loss = wasserstein_loss(valid, discr_pred_real)
discr_pred_fake = model.discriminator_forward(generated_features.detach())
# Regular GAN:
# fake_loss = F.binary_cross_entropy_with_logits(discr_pred_fake, fake)
# WGAN:
fake_loss = wasserstein_loss(fake, discr_pred_fake)
# Regular GAN:
discr_loss = (real_loss + fake_loss)
# WGAN:
#discr_loss = -(real_loss - fake_loss)
optim_discr.zero_grad()
discr_loss.backward()
optim_discr.step()
# WGAN:
for p in model.discriminator.parameters():
p.data.clamp_(-weight_clip_value, weight_clip_value)
discr_costs.append(discr_loss.item())
gener_costs.append(gener_loss.item())
### LOGGING
if not batch_idx % 100:
print ('Epoch: %03d/%03d | Batch %03d/%03d | Gen/Dis Loss: %.4f/%.4f'
%(epoch+1, NUM_EPOCHS, batch_idx,
len(train_loader), gener_loss, discr_loss))
print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))
print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))
Epoch: 001/100 | Batch 000/469 | Gen/Dis Loss: -0.5135/-0.0640 Epoch: 001/100 | Batch 100/469 | Gen/Dis Loss: -0.6194/-0.1880 Epoch: 001/100 | Batch 200/469 | Gen/Dis Loss: -0.5824/-0.1102 Epoch: 001/100 | Batch 300/469 | Gen/Dis Loss: -0.5471/-0.0933 Epoch: 001/100 | Batch 400/469 | Gen/Dis Loss: -0.5313/-0.0952 Time elapsed: 0.31 min Epoch: 002/100 | Batch 000/469 | Gen/Dis Loss: -0.5421/-0.0747 Epoch: 002/100 | Batch 100/469 | Gen/Dis Loss: -0.5374/-0.0782 Epoch: 002/100 | Batch 200/469 | Gen/Dis Loss: -0.5264/-0.0668 Epoch: 002/100 | Batch 300/469 | Gen/Dis Loss: -0.5445/-0.0666 Epoch: 002/100 | Batch 400/469 | Gen/Dis Loss: -0.5525/-0.0531 Time elapsed: 0.67 min Epoch: 003/100 | Batch 000/469 | Gen/Dis Loss: -0.5433/-0.0530 Epoch: 003/100 | Batch 100/469 | Gen/Dis Loss: -0.5621/-0.0404 Epoch: 003/100 | Batch 200/469 | Gen/Dis Loss: -0.5358/-0.0523 Epoch: 003/100 | Batch 300/469 | Gen/Dis Loss: -0.5589/-0.0481 Epoch: 003/100 | Batch 400/469 | Gen/Dis Loss: -0.5147/-0.0620 Time elapsed: 0.99 min Epoch: 004/100 | Batch 000/469 | Gen/Dis Loss: -0.5347/-0.0536 Epoch: 004/100 | Batch 100/469 | Gen/Dis Loss: -0.5992/-0.0476 Epoch: 004/100 | Batch 200/469 | Gen/Dis Loss: -0.5231/-0.0504 Epoch: 004/100 | Batch 300/469 | Gen/Dis Loss: -0.5679/-0.0426 Epoch: 004/100 | Batch 400/469 | Gen/Dis Loss: -0.5395/-0.0475 Time elapsed: 1.33 min Epoch: 005/100 | Batch 000/469 | Gen/Dis Loss: -0.4806/-0.0564 Epoch: 005/100 | Batch 100/469 | Gen/Dis Loss: -0.6035/-0.0468 Epoch: 005/100 | Batch 200/469 | Gen/Dis Loss: -0.5137/-0.0450 Epoch: 005/100 | Batch 300/469 | Gen/Dis Loss: -0.5013/-0.0563 Epoch: 005/100 | Batch 400/469 | Gen/Dis Loss: -0.5868/-0.0287 Time elapsed: 1.66 min Epoch: 006/100 | Batch 000/469 | Gen/Dis Loss: -0.4917/-0.0369 Epoch: 006/100 | Batch 100/469 | Gen/Dis Loss: -0.5030/-0.0502 Epoch: 006/100 | Batch 200/469 | Gen/Dis Loss: -0.4951/-0.0464 Epoch: 006/100 | Batch 300/469 | Gen/Dis Loss: -0.6160/-0.0365 Epoch: 006/100 | Batch 400/469 | Gen/Dis Loss: -0.5193/-0.0601 Time elapsed: 1.97 min Epoch: 007/100 | Batch 000/469 | Gen/Dis Loss: -0.5331/-0.0579 Epoch: 007/100 | Batch 100/469 | Gen/Dis Loss: -0.5478/-0.0290 Epoch: 007/100 | Batch 200/469 | Gen/Dis Loss: -0.4940/-0.0487 Epoch: 007/100 | Batch 300/469 | Gen/Dis Loss: -0.5937/-0.0365 Epoch: 007/100 | Batch 400/469 | Gen/Dis Loss: -0.5475/-0.0380 Time elapsed: 2.30 min Epoch: 008/100 | Batch 000/469 | Gen/Dis Loss: -0.5870/-0.0421 Epoch: 008/100 | Batch 100/469 | Gen/Dis Loss: -0.5566/-0.0393 Epoch: 008/100 | Batch 200/469 | Gen/Dis Loss: -0.4578/-0.0559 Epoch: 008/100 | Batch 300/469 | Gen/Dis Loss: -0.5766/-0.0405 Epoch: 008/100 | Batch 400/469 | Gen/Dis Loss: -0.5155/-0.0310 Time elapsed: 2.62 min Epoch: 009/100 | Batch 000/469 | Gen/Dis Loss: -0.4966/-0.0448 Epoch: 009/100 | Batch 100/469 | Gen/Dis Loss: -0.4337/-0.0660 Epoch: 009/100 | Batch 200/469 | Gen/Dis Loss: -0.5877/-0.0370 Epoch: 009/100 | Batch 300/469 | Gen/Dis Loss: -0.5682/-0.0198 Epoch: 009/100 | Batch 400/469 | Gen/Dis Loss: -0.5051/-0.0430 Time elapsed: 2.95 min Epoch: 010/100 | Batch 000/469 | Gen/Dis Loss: -0.4873/-0.0286 Epoch: 010/100 | Batch 100/469 | Gen/Dis Loss: -0.4787/-0.0412 Epoch: 010/100 | Batch 200/469 | Gen/Dis Loss: -0.6193/-0.0297 Epoch: 010/100 | Batch 300/469 | Gen/Dis Loss: -0.5719/-0.0421 Epoch: 010/100 | Batch 400/469 | Gen/Dis Loss: -0.5004/-0.0257 Time elapsed: 3.26 min Epoch: 011/100 | Batch 000/469 | Gen/Dis Loss: -0.5055/-0.0461 Epoch: 011/100 | Batch 100/469 | Gen/Dis Loss: -0.4339/-0.0311 Epoch: 011/100 | Batch 200/469 | Gen/Dis Loss: -0.6052/-0.0417 Epoch: 011/100 | Batch 300/469 | Gen/Dis Loss: -0.5660/-0.0375 Epoch: 011/100 | Batch 400/469 | Gen/Dis Loss: -0.5332/-0.0258 Time elapsed: 3.58 min Epoch: 012/100 | Batch 000/469 | Gen/Dis Loss: -0.4895/-0.0338 Epoch: 012/100 | Batch 100/469 | Gen/Dis Loss: -0.4584/-0.0317 Epoch: 012/100 | Batch 200/469 | Gen/Dis Loss: -0.5424/-0.0308 Epoch: 012/100 | Batch 300/469 | Gen/Dis Loss: -0.5662/-0.0438 Epoch: 012/100 | Batch 400/469 | Gen/Dis Loss: -0.4723/-0.0480 Time elapsed: 3.90 min Epoch: 013/100 | Batch 000/469 | Gen/Dis Loss: -0.5315/-0.0265 Epoch: 013/100 | Batch 100/469 | Gen/Dis Loss: -0.4478/-0.0395 Epoch: 013/100 | Batch 200/469 | Gen/Dis Loss: -0.6145/-0.0308 Epoch: 013/100 | Batch 300/469 | Gen/Dis Loss: -0.5426/-0.0317 Epoch: 013/100 | Batch 400/469 | Gen/Dis Loss: -0.5044/-0.0273 Time elapsed: 4.24 min Epoch: 014/100 | Batch 000/469 | Gen/Dis Loss: -0.5857/-0.0308 Epoch: 014/100 | Batch 100/469 | Gen/Dis Loss: -0.4632/-0.0271 Epoch: 014/100 | Batch 200/469 | Gen/Dis Loss: -0.4915/-0.0430 Epoch: 014/100 | Batch 300/469 | Gen/Dis Loss: -0.4571/-0.0239 Epoch: 014/100 | Batch 400/469 | Gen/Dis Loss: -0.4950/-0.0555 Time elapsed: 4.56 min Epoch: 015/100 | Batch 000/469 | Gen/Dis Loss: -0.5115/-0.0419 Epoch: 015/100 | Batch 100/469 | Gen/Dis Loss: -0.5577/-0.0200 Epoch: 015/100 | Batch 200/469 | Gen/Dis Loss: -0.5806/-0.0407 Epoch: 015/100 | Batch 300/469 | Gen/Dis Loss: -0.5311/-0.0247 Epoch: 015/100 | Batch 400/469 | Gen/Dis Loss: -0.4802/-0.0566 Time elapsed: 4.92 min Epoch: 016/100 | Batch 000/469 | Gen/Dis Loss: -0.4536/-0.0400 Epoch: 016/100 | Batch 100/469 | Gen/Dis Loss: -0.5354/-0.0286 Epoch: 016/100 | Batch 200/469 | Gen/Dis Loss: -0.5230/-0.0277 Epoch: 016/100 | Batch 300/469 | Gen/Dis Loss: -0.4963/-0.0256 Epoch: 016/100 | Batch 400/469 | Gen/Dis Loss: -0.5028/-0.0228 Time elapsed: 5.27 min Epoch: 017/100 | Batch 000/469 | Gen/Dis Loss: -0.5002/-0.0224 Epoch: 017/100 | Batch 100/469 | Gen/Dis Loss: -0.4974/-0.0233 Epoch: 017/100 | Batch 200/469 | Gen/Dis Loss: -0.4967/-0.0251 Epoch: 017/100 | Batch 300/469 | Gen/Dis Loss: -0.5007/-0.0202 Epoch: 017/100 | Batch 400/469 | Gen/Dis Loss: -0.5016/-0.0198 Time elapsed: 5.59 min Epoch: 018/100 | Batch 000/469 | Gen/Dis Loss: -0.5041/-0.0187 Epoch: 018/100 | Batch 100/469 | Gen/Dis Loss: -0.4997/-0.0150 Epoch: 018/100 | Batch 200/469 | Gen/Dis Loss: -0.5153/-0.0248 Epoch: 018/100 | Batch 300/469 | Gen/Dis Loss: -0.4866/-0.0284 Epoch: 018/100 | Batch 400/469 | Gen/Dis Loss: -0.4979/-0.0143 Time elapsed: 5.92 min Epoch: 019/100 | Batch 000/469 | Gen/Dis Loss: -0.5012/-0.0175 Epoch: 019/100 | Batch 100/469 | Gen/Dis Loss: -0.4956/-0.0285 Epoch: 019/100 | Batch 200/469 | Gen/Dis Loss: -0.4957/-0.0239 Epoch: 019/100 | Batch 300/469 | Gen/Dis Loss: -0.4967/-0.0165 Epoch: 019/100 | Batch 400/469 | Gen/Dis Loss: -0.4951/-0.0361 Time elapsed: 6.28 min Epoch: 020/100 | Batch 000/469 | Gen/Dis Loss: -0.4956/-0.0271 Epoch: 020/100 | Batch 100/469 | Gen/Dis Loss: -0.4974/-0.0174 Epoch: 020/100 | Batch 200/469 | Gen/Dis Loss: -0.5101/-0.0281 Epoch: 020/100 | Batch 300/469 | Gen/Dis Loss: -0.5421/-0.0134 Epoch: 020/100 | Batch 400/469 | Gen/Dis Loss: -0.5162/-0.0199 Time elapsed: 6.60 min Epoch: 021/100 | Batch 000/469 | Gen/Dis Loss: -0.5339/-0.0183 Epoch: 021/100 | Batch 100/469 | Gen/Dis Loss: -0.5280/-0.0215 Epoch: 021/100 | Batch 200/469 | Gen/Dis Loss: -0.5358/-0.0175 Epoch: 021/100 | Batch 300/469 | Gen/Dis Loss: -0.5243/-0.0169 Epoch: 021/100 | Batch 400/469 | Gen/Dis Loss: -0.5205/-0.0170 Time elapsed: 6.94 min Epoch: 022/100 | Batch 000/469 | Gen/Dis Loss: -0.5277/-0.0142 Epoch: 022/100 | Batch 100/469 | Gen/Dis Loss: -0.5203/-0.0218 Epoch: 022/100 | Batch 200/469 | Gen/Dis Loss: -0.5025/-0.0174 Epoch: 022/100 | Batch 300/469 | Gen/Dis Loss: -0.4963/-0.0200 Epoch: 022/100 | Batch 400/469 | Gen/Dis Loss: -0.5015/-0.0262 Time elapsed: 7.28 min Epoch: 023/100 | Batch 000/469 | Gen/Dis Loss: -0.4411/-0.0624 Epoch: 023/100 | Batch 100/469 | Gen/Dis Loss: -0.3992/-0.0383 Epoch: 023/100 | Batch 200/469 | Gen/Dis Loss: -0.4401/-0.0334 Epoch: 023/100 | Batch 300/469 | Gen/Dis Loss: -0.5975/-0.0422 Epoch: 023/100 | Batch 400/469 | Gen/Dis Loss: -0.5350/-0.0105 Time elapsed: 7.61 min Epoch: 024/100 | Batch 000/469 | Gen/Dis Loss: -0.6127/-0.0073 Epoch: 024/100 | Batch 100/469 | Gen/Dis Loss: -0.5962/-0.0066 Epoch: 024/100 | Batch 200/469 | Gen/Dis Loss: -0.5505/-0.0109 Epoch: 024/100 | Batch 300/469 | Gen/Dis Loss: -0.5428/-0.0151 Epoch: 024/100 | Batch 400/469 | Gen/Dis Loss: -0.5220/-0.0129 Time elapsed: 7.94 min Epoch: 025/100 | Batch 000/469 | Gen/Dis Loss: -0.5298/-0.0157 Epoch: 025/100 | Batch 100/469 | Gen/Dis Loss: -0.5273/-0.0156 Epoch: 025/100 | Batch 200/469 | Gen/Dis Loss: -0.5280/-0.0135 Epoch: 025/100 | Batch 300/469 | Gen/Dis Loss: -0.5153/-0.0122 Epoch: 025/100 | Batch 400/469 | Gen/Dis Loss: -0.5199/-0.0169 Time elapsed: 8.28 min Epoch: 026/100 | Batch 000/469 | Gen/Dis Loss: -0.5183/-0.0158 Epoch: 026/100 | Batch 100/469 | Gen/Dis Loss: -0.5154/-0.0142 Epoch: 026/100 | Batch 200/469 | Gen/Dis Loss: -0.5109/-0.0104 Epoch: 026/100 | Batch 300/469 | Gen/Dis Loss: -0.4933/-0.0196 Epoch: 026/100 | Batch 400/469 | Gen/Dis Loss: -0.4977/-0.0119 Time elapsed: 8.62 min Epoch: 027/100 | Batch 000/469 | Gen/Dis Loss: -0.4141/-0.0329 Epoch: 027/100 | Batch 100/469 | Gen/Dis Loss: -0.4162/-0.0315 Epoch: 027/100 | Batch 200/469 | Gen/Dis Loss: -0.4167/-0.0266 Epoch: 027/100 | Batch 300/469 | Gen/Dis Loss: -0.4595/-0.0283 Epoch: 027/100 | Batch 400/469 | Gen/Dis Loss: -0.5303/-0.0371 Time elapsed: 8.96 min Epoch: 028/100 | Batch 000/469 | Gen/Dis Loss: -0.5231/-0.0361 Epoch: 028/100 | Batch 100/469 | Gen/Dis Loss: -0.4554/-0.0312 Epoch: 028/100 | Batch 200/469 | Gen/Dis Loss: -0.7106/-0.0115 Epoch: 028/100 | Batch 300/469 | Gen/Dis Loss: -0.5543/-0.0177 Epoch: 028/100 | Batch 400/469 | Gen/Dis Loss: -0.6160/-0.0154 Time elapsed: 9.30 min Epoch: 029/100 | Batch 000/469 | Gen/Dis Loss: -0.6364/-0.0123 Epoch: 029/100 | Batch 100/469 | Gen/Dis Loss: -0.5711/-0.0187 Epoch: 029/100 | Batch 200/469 | Gen/Dis Loss: -0.4897/-0.0145 Epoch: 029/100 | Batch 300/469 | Gen/Dis Loss: -0.5124/-0.0121 Epoch: 029/100 | Batch 400/469 | Gen/Dis Loss: -0.5241/-0.0105 Time elapsed: 9.63 min Epoch: 030/100 | Batch 000/469 | Gen/Dis Loss: -0.5315/-0.0108 Epoch: 030/100 | Batch 100/469 | Gen/Dis Loss: -0.5224/-0.0134 Epoch: 030/100 | Batch 200/469 | Gen/Dis Loss: -0.5097/-0.0133 Epoch: 030/100 | Batch 300/469 | Gen/Dis Loss: -0.5200/-0.0090 Epoch: 030/100 | Batch 400/469 | Gen/Dis Loss: -0.5248/-0.0105 Time elapsed: 9.98 min Epoch: 031/100 | Batch 000/469 | Gen/Dis Loss: -0.5087/-0.0090 Epoch: 031/100 | Batch 100/469 | Gen/Dis Loss: -0.5033/-0.0110 Epoch: 031/100 | Batch 200/469 | Gen/Dis Loss: -0.5164/-0.0088 Epoch: 031/100 | Batch 300/469 | Gen/Dis Loss: -0.4947/-0.0122 Epoch: 031/100 | Batch 400/469 | Gen/Dis Loss: -0.5088/-0.0133 Time elapsed: 10.32 min Epoch: 032/100 | Batch 000/469 | Gen/Dis Loss: -0.5059/-0.0182 Epoch: 032/100 | Batch 100/469 | Gen/Dis Loss: -0.5062/-0.0120 Epoch: 032/100 | Batch 200/469 | Gen/Dis Loss: -0.4882/-0.0181 Epoch: 032/100 | Batch 300/469 | Gen/Dis Loss: -0.5167/-0.0123 Epoch: 032/100 | Batch 400/469 | Gen/Dis Loss: -0.4355/-0.0403 Time elapsed: 10.65 min Epoch: 033/100 | Batch 000/469 | Gen/Dis Loss: -0.4432/-0.0723 Epoch: 033/100 | Batch 100/469 | Gen/Dis Loss: -0.5519/-0.0591 Epoch: 033/100 | Batch 200/469 | Gen/Dis Loss: -0.4846/-0.0375 Epoch: 033/100 | Batch 300/469 | Gen/Dis Loss: -0.4178/-0.0216 Epoch: 033/100 | Batch 400/469 | Gen/Dis Loss: -0.4613/-0.0146 Time elapsed: 11.00 min Epoch: 034/100 | Batch 000/469 | Gen/Dis Loss: -0.4342/-0.0706 Epoch: 034/100 | Batch 100/469 | Gen/Dis Loss: -0.4615/-0.0238 Epoch: 034/100 | Batch 200/469 | Gen/Dis Loss: -0.3540/-0.0359 Epoch: 034/100 | Batch 300/469 | Gen/Dis Loss: -0.5712/-0.0543 Epoch: 034/100 | Batch 400/469 | Gen/Dis Loss: -0.6412/-0.0186 Time elapsed: 11.35 min Epoch: 035/100 | Batch 000/469 | Gen/Dis Loss: -0.6714/-0.0031 Epoch: 035/100 | Batch 100/469 | Gen/Dis Loss: -0.6070/-0.0054 Epoch: 035/100 | Batch 200/469 | Gen/Dis Loss: -0.5227/-0.0107 Epoch: 035/100 | Batch 300/469 | Gen/Dis Loss: -0.5110/-0.0098 Epoch: 035/100 | Batch 400/469 | Gen/Dis Loss: -0.5154/-0.0102 Time elapsed: 11.67 min Epoch: 036/100 | Batch 000/469 | Gen/Dis Loss: -0.5224/-0.0131 Epoch: 036/100 | Batch 100/469 | Gen/Dis Loss: -0.5149/-0.0125 Epoch: 036/100 | Batch 200/469 | Gen/Dis Loss: -0.5129/-0.0117 Epoch: 036/100 | Batch 300/469 | Gen/Dis Loss: -0.5269/-0.0085 Epoch: 036/100 | Batch 400/469 | Gen/Dis Loss: -0.5165/-0.0101 Time elapsed: 12.00 min Epoch: 037/100 | Batch 000/469 | Gen/Dis Loss: -0.5115/-0.0081 Epoch: 037/100 | Batch 100/469 | Gen/Dis Loss: -0.5068/-0.0089 Epoch: 037/100 | Batch 200/469 | Gen/Dis Loss: -0.5203/-0.0099 Epoch: 037/100 | Batch 300/469 | Gen/Dis Loss: -0.6883/-0.0153 Epoch: 037/100 | Batch 400/469 | Gen/Dis Loss: -0.5024/-0.0118 Time elapsed: 12.34 min Epoch: 038/100 | Batch 000/469 | Gen/Dis Loss: -0.5234/-0.0365 Epoch: 038/100 | Batch 100/469 | Gen/Dis Loss: -0.4579/-0.0261 Epoch: 038/100 | Batch 200/469 | Gen/Dis Loss: -0.5098/-0.0212 Epoch: 038/100 | Batch 300/469 | Gen/Dis Loss: -0.4729/-0.0302 Epoch: 038/100 | Batch 400/469 | Gen/Dis Loss: -0.4838/-0.0219 Time elapsed: 12.67 min Epoch: 039/100 | Batch 000/469 | Gen/Dis Loss: -0.5327/-0.0189 Epoch: 039/100 | Batch 100/469 | Gen/Dis Loss: 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Epoch: 060/100 | Batch 000/469 | Gen/Dis Loss: -0.4892/-0.0026 Epoch: 060/100 | Batch 100/469 | Gen/Dis Loss: -0.4848/-0.0038 Epoch: 060/100 | Batch 200/469 | Gen/Dis Loss: -0.5323/-0.0019 Epoch: 060/100 | Batch 300/469 | Gen/Dis Loss: -0.5086/-0.0020 Epoch: 060/100 | Batch 400/469 | Gen/Dis Loss: -0.4910/-0.0010 Time elapsed: 20.01 min Epoch: 061/100 | Batch 000/469 | Gen/Dis Loss: -0.4976/-0.0008 Epoch: 061/100 | Batch 100/469 | Gen/Dis Loss: -0.5041/-0.0012 Epoch: 061/100 | Batch 200/469 | Gen/Dis Loss: -0.4993/-0.0012 Epoch: 061/100 | Batch 300/469 | Gen/Dis Loss: -0.4994/-0.0012 Epoch: 061/100 | Batch 400/469 | Gen/Dis Loss: -0.5033/-0.0015 Time elapsed: 20.35 min Epoch: 062/100 | Batch 000/469 | Gen/Dis Loss: -0.5482/-0.0118 Epoch: 062/100 | Batch 100/469 | Gen/Dis Loss: -0.4645/-0.0004 Epoch: 062/100 | Batch 200/469 | Gen/Dis Loss: -0.5043/-0.0009 Epoch: 062/100 | Batch 300/469 | Gen/Dis Loss: -0.5003/-0.0042 Epoch: 062/100 | Batch 400/469 | Gen/Dis Loss: -0.4903/-0.0025 Time 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| Gen/Dis Loss: -0.5151/-0.0162 Epoch: 080/100 | Batch 400/469 | Gen/Dis Loss: -0.5094/-0.0035 Time elapsed: 26.65 min Epoch: 081/100 | Batch 000/469 | Gen/Dis Loss: -0.4744/-0.0154 Epoch: 081/100 | Batch 100/469 | Gen/Dis Loss: -0.5015/-0.0085 Epoch: 081/100 | Batch 200/469 | Gen/Dis Loss: -0.4256/-0.0220 Epoch: 081/100 | Batch 300/469 | Gen/Dis Loss: -0.5114/-0.0264 Epoch: 081/100 | Batch 400/469 | Gen/Dis Loss: -0.5249/-0.0219 Time elapsed: 26.97 min Epoch: 082/100 | Batch 000/469 | Gen/Dis Loss: -0.5402/-0.0034 Epoch: 082/100 | Batch 100/469 | Gen/Dis Loss: -0.4999/-0.0112 Epoch: 082/100 | Batch 200/469 | Gen/Dis Loss: -0.5099/-0.0076 Epoch: 082/100 | Batch 300/469 | Gen/Dis Loss: -0.5023/-0.0015 Epoch: 082/100 | Batch 400/469 | Gen/Dis Loss: -0.5537/-0.0003 Time elapsed: 27.33 min Epoch: 083/100 | Batch 000/469 | Gen/Dis Loss: -0.5181/-0.0019 Epoch: 083/100 | Batch 100/469 | Gen/Dis Loss: -0.5098/-0.0013 Epoch: 083/100 | Batch 200/469 | Gen/Dis Loss: -0.5034/-0.0004 Epoch: 083/100 | Batch 300/469 | Gen/Dis Loss: -0.5203/-0.0005 Epoch: 083/100 | Batch 400/469 | Gen/Dis Loss: -0.5169/-0.0004 Time elapsed: 27.68 min Epoch: 084/100 | Batch 000/469 | Gen/Dis Loss: -0.5269/-0.0023 Epoch: 084/100 | Batch 100/469 | Gen/Dis Loss: -0.5413/0.0009 Epoch: 084/100 | Batch 200/469 | Gen/Dis Loss: -0.5025/-0.0005 Epoch: 084/100 | Batch 300/469 | Gen/Dis Loss: -0.4953/-0.0007 Epoch: 084/100 | Batch 400/469 | Gen/Dis Loss: -0.4954/-0.0002 Time elapsed: 28.01 min Epoch: 085/100 | Batch 000/469 | Gen/Dis Loss: -0.5085/-0.0006 Epoch: 085/100 | Batch 100/469 | Gen/Dis Loss: -0.5094/-0.0003 Epoch: 085/100 | Batch 200/469 | Gen/Dis Loss: -0.4905/-0.0010 Epoch: 085/100 | Batch 300/469 | Gen/Dis Loss: -0.4985/-0.0005 Epoch: 085/100 | Batch 400/469 | Gen/Dis Loss: -0.5243/-0.0019 Time elapsed: 28.32 min Epoch: 086/100 | Batch 000/469 | Gen/Dis Loss: -0.5194/-0.0017 Epoch: 086/100 | Batch 100/469 | Gen/Dis Loss: -0.4573/-0.0016 Epoch: 086/100 | Batch 200/469 | Gen/Dis Loss: -0.4788/-0.0000 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Epoch: 098/100 | Batch 200/469 | Gen/Dis Loss: -0.5064/-0.0004 Epoch: 098/100 | Batch 300/469 | Gen/Dis Loss: -0.5039/-0.0007 Epoch: 098/100 | Batch 400/469 | Gen/Dis Loss: -0.5418/-0.0339 Time elapsed: 32.61 min Epoch: 099/100 | Batch 000/469 | Gen/Dis Loss: -0.5468/-0.0073 Epoch: 099/100 | Batch 100/469 | Gen/Dis Loss: -0.4540/-0.0241 Epoch: 099/100 | Batch 200/469 | Gen/Dis Loss: -0.5279/-0.0195 Epoch: 099/100 | Batch 300/469 | Gen/Dis Loss: -0.4611/-0.0213 Epoch: 099/100 | Batch 400/469 | Gen/Dis Loss: -0.4722/-0.0154 Time elapsed: 32.94 min Epoch: 100/100 | Batch 000/469 | Gen/Dis Loss: -0.4558/-0.0172 Epoch: 100/100 | Batch 100/469 | Gen/Dis Loss: -0.5864/-0.0064 Epoch: 100/100 | Batch 200/469 | Gen/Dis Loss: -0.4810/-0.0077 Epoch: 100/100 | Batch 300/469 | Gen/Dis Loss: -0.4921/-0.0127 Epoch: 100/100 | Batch 400/469 | Gen/Dis Loss: -0.4773/-0.0143 Time elapsed: 33.30 min Total Training Time: 33.30 min
%matplotlib inline
import matplotlib.pyplot as plt
ax1 = plt.subplot(1, 1, 1)
ax1.plot(range(len(gener_costs)), gener_costs, label='Generator loss')
ax1.plot(range(len(discr_costs)), discr_costs, label='Discriminator loss')
ax1.set_xlabel('Iterations')
ax1.set_ylabel('Loss')
ax1.legend()
###################
# Set scond x-axis
ax2 = ax1.twiny()
newlabel = list(range(NUM_EPOCHS+1))
iter_per_epoch = len(train_loader)
newpos = [e*iter_per_epoch for e in newlabel]
ax2.set_xticklabels(newlabel[::10])
ax2.set_xticks(newpos[::10])
ax2.xaxis.set_ticks_position('bottom')
ax2.xaxis.set_label_position('bottom')
ax2.spines['bottom'].set_position(('outward', 45))
ax2.set_xlabel('Epochs')
ax2.set_xlim(ax1.get_xlim())
###################
plt.show()
##########################
### VISUALIZATION
##########################
model.eval()
# Make new images
z = torch.zeros((5, LATENT_DIM)).uniform_(-1.0, 1.0).to(device)
generated_features = model.generator_forward(z)
imgs = generated_features.view(-1, 28, 28)
fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(20, 2.5))
for i, ax in enumerate(axes):
axes[i].imshow(imgs[i].to(torch.device('cpu')).detach(), cmap='binary')