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 standard GAN.
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:2" if torch.cuda.is_available() else "cpu")
# Hyperparameters
random_seed = 123
generator_learning_rate = 0.001
discriminator_learning_rate = 0.001
NUM_EPOCHS = 100
BATCH_SIZE = 128
LATENT_DIM = 75
IMG_SHAPE = (1, 28, 28)
IMG_SIZE = 1
for x in IMG_SHAPE:
IMG_SIZE *= x
##########################
### 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
##########################
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)
valid = torch.ones(targets.size(0)).float().to(device)
fake = torch.zeros(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)
gener_loss = F.binary_cross_entropy(discr_pred, valid)
optim_gener.zero_grad()
gener_loss.backward()
optim_gener.step()
# --------------------------
# Train Discriminator
# --------------------------
discr_pred_real = model.discriminator_forward(features.view(-1, IMG_SIZE))
real_loss = F.binary_cross_entropy(discr_pred_real, valid)
discr_pred_fake = model.discriminator_forward(generated_features.detach())
fake_loss = F.binary_cross_entropy(discr_pred_fake, fake)
discr_loss = 0.5*(real_loss + fake_loss)
optim_discr.zero_grad()
discr_loss.backward()
optim_discr.step()
discr_costs.append(discr_loss)
gener_costs.append(gener_loss)
### 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: 0.6576/0.7134 Epoch: 001/100 | Batch 100/469 | Gen/Dis Loss: 5.1797/0.0280 Epoch: 001/100 | Batch 200/469 | Gen/Dis Loss: 1.8944/0.0933 Epoch: 001/100 | Batch 300/469 | Gen/Dis Loss: 1.5018/0.1451 Epoch: 001/100 | Batch 400/469 | Gen/Dis Loss: 2.0884/0.1026 Time elapsed: 0.27 min Epoch: 002/100 | Batch 000/469 | Gen/Dis Loss: 2.8803/0.0496 Epoch: 002/100 | Batch 100/469 | Gen/Dis Loss: 3.4923/0.0483 Epoch: 002/100 | Batch 200/469 | Gen/Dis Loss: 2.9812/0.1615 Epoch: 002/100 | Batch 300/469 | Gen/Dis Loss: 2.2371/0.1658 Epoch: 002/100 | Batch 400/469 | Gen/Dis Loss: 1.7027/0.2905 Time elapsed: 0.51 min Epoch: 003/100 | Batch 000/469 | Gen/Dis Loss: 1.2188/0.3533 Epoch: 003/100 | Batch 100/469 | Gen/Dis Loss: 1.8254/0.2083 Epoch: 003/100 | Batch 200/469 | Gen/Dis Loss: 1.9774/0.2238 Epoch: 003/100 | Batch 300/469 | Gen/Dis Loss: 1.9323/0.2806 Epoch: 003/100 | Batch 400/469 | Gen/Dis Loss: 1.9518/0.2712 Time elapsed: 0.77 min Epoch: 004/100 | Batch 000/469 | Gen/Dis Loss: 1.2785/0.3455 Epoch: 004/100 | Batch 100/469 | Gen/Dis Loss: 1.3979/0.3208 Epoch: 004/100 | Batch 200/469 | Gen/Dis Loss: 1.4295/0.3638 Epoch: 004/100 | Batch 300/469 | Gen/Dis Loss: 1.2798/0.3620 Epoch: 004/100 | Batch 400/469 | Gen/Dis Loss: 1.1321/0.4751 Time elapsed: 1.04 min Epoch: 005/100 | Batch 000/469 | Gen/Dis Loss: 1.1786/0.3932 Epoch: 005/100 | Batch 100/469 | Gen/Dis Loss: 1.1437/0.4343 Epoch: 005/100 | Batch 200/469 | Gen/Dis Loss: 1.0105/0.4453 Epoch: 005/100 | Batch 300/469 | Gen/Dis Loss: 1.3987/0.4194 Epoch: 005/100 | Batch 400/469 | Gen/Dis Loss: 1.3960/0.4005 Time elapsed: 1.28 min Epoch: 006/100 | Batch 000/469 | Gen/Dis Loss: 1.3119/0.4792 Epoch: 006/100 | Batch 100/469 | Gen/Dis Loss: 1.6029/0.4045 Epoch: 006/100 | Batch 200/469 | Gen/Dis Loss: 1.6302/0.3768 Epoch: 006/100 | Batch 300/469 | Gen/Dis Loss: 0.9141/0.4838 Epoch: 006/100 | Batch 400/469 | Gen/Dis Loss: 0.9891/0.4810 Time elapsed: 1.56 min Epoch: 007/100 | Batch 000/469 | Gen/Dis Loss: 1.3198/0.4820 Epoch: 007/100 | Batch 100/469 | Gen/Dis Loss: 1.1527/0.4620 Epoch: 007/100 | Batch 200/469 | Gen/Dis Loss: 1.3668/0.3967 Epoch: 007/100 | Batch 300/469 | Gen/Dis Loss: 1.6183/0.4676 Epoch: 007/100 | Batch 400/469 | Gen/Dis Loss: 1.0077/0.4841 Time elapsed: 1.85 min Epoch: 008/100 | Batch 000/469 | Gen/Dis Loss: 1.2245/0.5437 Epoch: 008/100 | Batch 100/469 | Gen/Dis Loss: 1.0142/0.4928 Epoch: 008/100 | Batch 200/469 | Gen/Dis Loss: 0.8817/0.4939 Epoch: 008/100 | Batch 300/469 | Gen/Dis Loss: 1.0748/0.4967 Epoch: 008/100 | Batch 400/469 | Gen/Dis Loss: 2.1265/0.4329 Time elapsed: 2.11 min Epoch: 009/100 | Batch 000/469 | Gen/Dis Loss: 0.9277/0.4871 Epoch: 009/100 | Batch 100/469 | Gen/Dis Loss: 1.1624/0.4473 Epoch: 009/100 | Batch 200/469 | Gen/Dis Loss: 1.1869/0.4800 Epoch: 009/100 | Batch 300/469 | Gen/Dis Loss: 1.9998/0.4295 Epoch: 009/100 | Batch 400/469 | Gen/Dis Loss: 1.6921/0.5037 Time elapsed: 2.34 min Epoch: 010/100 | Batch 000/469 | Gen/Dis Loss: 1.3091/0.4358 Epoch: 010/100 | Batch 100/469 | Gen/Dis Loss: 1.2604/0.5375 Epoch: 010/100 | Batch 200/469 | Gen/Dis Loss: 1.1491/0.4537 Epoch: 010/100 | Batch 300/469 | Gen/Dis Loss: 1.3843/0.5068 Epoch: 010/100 | Batch 400/469 | Gen/Dis Loss: 1.3413/0.5051 Time elapsed: 2.60 min Epoch: 011/100 | Batch 000/469 | Gen/Dis Loss: 1.2368/0.5161 Epoch: 011/100 | Batch 100/469 | Gen/Dis Loss: 1.3715/0.4692 Epoch: 011/100 | Batch 200/469 | Gen/Dis Loss: 1.1182/0.5274 Epoch: 011/100 | Batch 300/469 | Gen/Dis Loss: 1.2770/0.4649 Epoch: 011/100 | Batch 400/469 | Gen/Dis Loss: 1.1847/0.5504 Time elapsed: 2.84 min Epoch: 012/100 | Batch 000/469 | Gen/Dis Loss: 0.9930/0.5509 Epoch: 012/100 | Batch 100/469 | Gen/Dis Loss: 1.1921/0.5310 Epoch: 012/100 | Batch 200/469 | Gen/Dis Loss: 0.9925/0.6062 Epoch: 012/100 | Batch 300/469 | Gen/Dis Loss: 1.1246/0.5170 Epoch: 012/100 | Batch 400/469 | Gen/Dis Loss: 1.0432/0.4437 Time elapsed: 3.07 min Epoch: 013/100 | Batch 000/469 | Gen/Dis Loss: 1.1419/0.5287 Epoch: 013/100 | Batch 100/469 | Gen/Dis Loss: 1.0053/0.5152 Epoch: 013/100 | Batch 200/469 | Gen/Dis Loss: 1.1308/0.5384 Epoch: 013/100 | Batch 300/469 | Gen/Dis Loss: 1.1822/0.5124 Epoch: 013/100 | Batch 400/469 | Gen/Dis Loss: 1.4501/0.5495 Time elapsed: 3.32 min Epoch: 014/100 | Batch 000/469 | Gen/Dis Loss: 1.1417/0.5364 Epoch: 014/100 | Batch 100/469 | Gen/Dis Loss: 0.9595/0.5884 Epoch: 014/100 | Batch 200/469 | Gen/Dis Loss: 0.9887/0.5216 Epoch: 014/100 | Batch 300/469 | Gen/Dis Loss: 1.0332/0.5686 Epoch: 014/100 | Batch 400/469 | Gen/Dis Loss: 1.5268/0.4554 Time elapsed: 3.60 min Epoch: 015/100 | Batch 000/469 | Gen/Dis Loss: 1.1181/0.4960 Epoch: 015/100 | Batch 100/469 | Gen/Dis Loss: 1.2722/0.4632 Epoch: 015/100 | Batch 200/469 | Gen/Dis Loss: 0.9523/0.6012 Epoch: 015/100 | Batch 300/469 | Gen/Dis Loss: 0.9905/0.5274 Epoch: 015/100 | Batch 400/469 | Gen/Dis Loss: 1.0448/0.5855 Time elapsed: 3.82 min Epoch: 016/100 | Batch 000/469 | Gen/Dis Loss: 1.0641/0.5432 Epoch: 016/100 | Batch 100/469 | Gen/Dis Loss: 0.9587/0.5636 Epoch: 016/100 | Batch 200/469 | Gen/Dis Loss: 1.3602/0.5691 Epoch: 016/100 | Batch 300/469 | Gen/Dis Loss: 1.1294/0.5564 Epoch: 016/100 | Batch 400/469 | Gen/Dis Loss: 1.0727/0.5042 Time elapsed: 4.04 min Epoch: 017/100 | Batch 000/469 | Gen/Dis Loss: 0.9285/0.6045 Epoch: 017/100 | Batch 100/469 | Gen/Dis Loss: 1.0024/0.6384 Epoch: 017/100 | Batch 200/469 | Gen/Dis Loss: 1.5662/0.4652 Epoch: 017/100 | Batch 300/469 | Gen/Dis Loss: 1.3644/0.4632 Epoch: 017/100 | Batch 400/469 | Gen/Dis Loss: 1.2681/0.5238 Time elapsed: 4.22 min Epoch: 018/100 | Batch 000/469 | Gen/Dis Loss: 1.2578/0.5151 Epoch: 018/100 | Batch 100/469 | Gen/Dis Loss: 1.6475/0.4929 Epoch: 018/100 | Batch 200/469 | Gen/Dis Loss: 1.0610/0.5496 Epoch: 018/100 | Batch 300/469 | Gen/Dis Loss: 1.0613/0.5634 Epoch: 018/100 | Batch 400/469 | Gen/Dis Loss: 1.4675/0.4589 Time elapsed: 4.40 min Epoch: 019/100 | Batch 000/469 | Gen/Dis Loss: 1.1211/0.5027 Epoch: 019/100 | Batch 100/469 | Gen/Dis Loss: 1.1444/0.5655 Epoch: 019/100 | Batch 200/469 | Gen/Dis Loss: 1.2471/0.5716 Epoch: 019/100 | Batch 300/469 | Gen/Dis Loss: 1.0223/0.5106 Epoch: 019/100 | Batch 400/469 | Gen/Dis Loss: 1.0361/0.5805 Time elapsed: 4.58 min Epoch: 020/100 | Batch 000/469 | Gen/Dis Loss: 0.9195/0.5428 Epoch: 020/100 | Batch 100/469 | Gen/Dis Loss: 1.3110/0.4955 Epoch: 020/100 | Batch 200/469 | Gen/Dis Loss: 1.2449/0.4973 Epoch: 020/100 | Batch 300/469 | Gen/Dis Loss: 1.3258/0.4992 Epoch: 020/100 | Batch 400/469 | Gen/Dis Loss: 1.2196/0.5279 Time elapsed: 4.77 min Epoch: 021/100 | Batch 000/469 | Gen/Dis Loss: 1.5621/0.5584 Epoch: 021/100 | Batch 100/469 | Gen/Dis Loss: 1.1148/0.5888 Epoch: 021/100 | Batch 200/469 | Gen/Dis Loss: 1.5108/0.4636 Epoch: 021/100 | Batch 300/469 | Gen/Dis Loss: 1.0957/0.4912 Epoch: 021/100 | Batch 400/469 | Gen/Dis Loss: 1.0342/0.5184 Time elapsed: 4.92 min Epoch: 022/100 | Batch 000/469 | Gen/Dis Loss: 1.9312/0.4366 Epoch: 022/100 | Batch 100/469 | Gen/Dis Loss: 1.2312/0.5260 Epoch: 022/100 | Batch 200/469 | Gen/Dis Loss: 1.1939/0.5075 Epoch: 022/100 | Batch 300/469 | Gen/Dis Loss: 1.1393/0.5692 Epoch: 022/100 | Batch 400/469 | Gen/Dis Loss: 1.0390/0.5261 Time elapsed: 5.05 min Epoch: 023/100 | Batch 000/469 | Gen/Dis Loss: 1.3148/0.4902 Epoch: 023/100 | Batch 100/469 | Gen/Dis Loss: 1.2077/0.6129 Epoch: 023/100 | Batch 200/469 | Gen/Dis Loss: 1.0886/0.5545 Epoch: 023/100 | Batch 300/469 | Gen/Dis Loss: 1.0762/0.4948 Epoch: 023/100 | Batch 400/469 | Gen/Dis Loss: 1.5361/0.5476 Time elapsed: 5.17 min Epoch: 024/100 | Batch 000/469 | Gen/Dis Loss: 1.1752/0.5881 Epoch: 024/100 | Batch 100/469 | Gen/Dis Loss: 1.3408/0.5339 Epoch: 024/100 | Batch 200/469 | Gen/Dis Loss: 1.2613/0.4555 Epoch: 024/100 | Batch 300/469 | Gen/Dis Loss: 1.0707/0.5099 Epoch: 024/100 | Batch 400/469 | Gen/Dis Loss: 1.1063/0.5695 Time elapsed: 5.32 min Epoch: 025/100 | Batch 000/469 | Gen/Dis Loss: 1.2911/0.5084 Epoch: 025/100 | Batch 100/469 | Gen/Dis Loss: 1.1280/0.5151 Epoch: 025/100 | Batch 200/469 | Gen/Dis Loss: 1.3799/0.5784 Epoch: 025/100 | Batch 300/469 | Gen/Dis Loss: 1.1675/0.6001 Epoch: 025/100 | Batch 400/469 | Gen/Dis Loss: 0.9834/0.6158 Time elapsed: 5.48 min Epoch: 026/100 | Batch 000/469 | Gen/Dis Loss: 1.2713/0.5475 Epoch: 026/100 | Batch 100/469 | Gen/Dis Loss: 1.3814/0.5652 Epoch: 026/100 | Batch 200/469 | Gen/Dis Loss: 1.1782/0.4850 Epoch: 026/100 | Batch 300/469 | Gen/Dis Loss: 0.9917/0.5888 Epoch: 026/100 | Batch 400/469 | Gen/Dis Loss: 1.0909/0.5825 Time elapsed: 5.64 min Epoch: 027/100 | Batch 000/469 | Gen/Dis Loss: 1.0873/0.5579 Epoch: 027/100 | Batch 100/469 | Gen/Dis Loss: 0.9639/0.5860 Epoch: 027/100 | Batch 200/469 | Gen/Dis Loss: 1.0458/0.5526 Epoch: 027/100 | Batch 300/469 | Gen/Dis Loss: 1.3373/0.5140 Epoch: 027/100 | Batch 400/469 | Gen/Dis Loss: 1.2790/0.5223 Time elapsed: 5.79 min Epoch: 028/100 | Batch 000/469 | Gen/Dis Loss: 0.9300/0.5869 Epoch: 028/100 | Batch 100/469 | Gen/Dis Loss: 1.0022/0.6056 Epoch: 028/100 | Batch 200/469 | Gen/Dis Loss: 1.0688/0.5447 Epoch: 028/100 | Batch 300/469 | Gen/Dis Loss: 1.0161/0.5702 Epoch: 028/100 | Batch 400/469 | Gen/Dis Loss: 0.8731/0.5543 Time elapsed: 5.92 min Epoch: 029/100 | Batch 000/469 | Gen/Dis Loss: 0.8719/0.5524 Epoch: 029/100 | Batch 100/469 | Gen/Dis Loss: 1.3005/0.5179 Epoch: 029/100 | Batch 200/469 | Gen/Dis Loss: 1.2986/0.5312 Epoch: 029/100 | Batch 300/469 | Gen/Dis Loss: 1.1084/0.5207 Epoch: 029/100 | Batch 400/469 | Gen/Dis Loss: 1.0591/0.5577 Time elapsed: 6.07 min Epoch: 030/100 | Batch 000/469 | Gen/Dis Loss: 1.0231/0.6170 Epoch: 030/100 | Batch 100/469 | Gen/Dis Loss: 0.9142/0.6046 Epoch: 030/100 | Batch 200/469 | Gen/Dis Loss: 1.2140/0.5290 Epoch: 030/100 | Batch 300/469 | Gen/Dis Loss: 0.8784/0.5804 Epoch: 030/100 | Batch 400/469 | Gen/Dis Loss: 1.1178/0.5165 Time elapsed: 6.20 min Epoch: 031/100 | Batch 000/469 | Gen/Dis Loss: 0.9555/0.5921 Epoch: 031/100 | Batch 100/469 | Gen/Dis Loss: 0.9644/0.5432 Epoch: 031/100 | Batch 200/469 | Gen/Dis Loss: 0.9531/0.5465 Epoch: 031/100 | Batch 300/469 | Gen/Dis Loss: 1.3496/0.5550 Epoch: 031/100 | Batch 400/469 | Gen/Dis Loss: 1.2137/0.5672 Time elapsed: 6.32 min Epoch: 032/100 | Batch 000/469 | Gen/Dis Loss: 1.0849/0.5020 Epoch: 032/100 | Batch 100/469 | Gen/Dis Loss: 0.9098/0.5481 Epoch: 032/100 | Batch 200/469 | Gen/Dis Loss: 1.2349/0.5024 Epoch: 032/100 | Batch 300/469 | Gen/Dis Loss: 0.9468/0.5599 Epoch: 032/100 | Batch 400/469 | Gen/Dis Loss: 1.4531/0.4928 Time elapsed: 6.45 min Epoch: 033/100 | Batch 000/469 | Gen/Dis Loss: 1.3397/0.5521 Epoch: 033/100 | Batch 100/469 | Gen/Dis Loss: 1.0106/0.5472 Epoch: 033/100 | Batch 200/469 | Gen/Dis Loss: 0.9787/0.5606 Epoch: 033/100 | Batch 300/469 | Gen/Dis Loss: 1.1434/0.5388 Epoch: 033/100 | Batch 400/469 | Gen/Dis Loss: 1.0476/0.5259 Time elapsed: 6.57 min Epoch: 034/100 | Batch 000/469 | Gen/Dis Loss: 1.3847/0.5294 Epoch: 034/100 | Batch 100/469 | Gen/Dis Loss: 0.8550/0.5800 Epoch: 034/100 | Batch 200/469 | Gen/Dis Loss: 1.0220/0.5527 Epoch: 034/100 | Batch 300/469 | Gen/Dis Loss: 0.9255/0.5751 Epoch: 034/100 | Batch 400/469 | Gen/Dis Loss: 1.0400/0.5554 Time elapsed: 6.72 min Epoch: 035/100 | Batch 000/469 | Gen/Dis Loss: 0.9723/0.5789 Epoch: 035/100 | Batch 100/469 | Gen/Dis Loss: 1.4414/0.4769 Epoch: 035/100 | Batch 200/469 | Gen/Dis Loss: 0.9431/0.5898 Epoch: 035/100 | Batch 300/469 | Gen/Dis Loss: 0.8252/0.6573 Epoch: 035/100 | Batch 400/469 | Gen/Dis Loss: 0.9694/0.5427 Time elapsed: 6.84 min Epoch: 036/100 | Batch 000/469 | Gen/Dis Loss: 1.3664/0.5839 Epoch: 036/100 | Batch 100/469 | Gen/Dis Loss: 1.0854/0.5739 Epoch: 036/100 | Batch 200/469 | Gen/Dis Loss: 1.0429/0.5457 Epoch: 036/100 | Batch 300/469 | Gen/Dis Loss: 0.8601/0.6151 Epoch: 036/100 | Batch 400/469 | Gen/Dis Loss: 1.2785/0.5850 Time elapsed: 6.97 min Epoch: 037/100 | Batch 000/469 | Gen/Dis Loss: 1.0251/0.5933 Epoch: 037/100 | Batch 100/469 | Gen/Dis Loss: 1.2177/0.5053 Epoch: 037/100 | Batch 200/469 | Gen/Dis Loss: 0.8804/0.5925 Epoch: 037/100 | Batch 300/469 | Gen/Dis Loss: 1.2797/0.6173 Epoch: 037/100 | Batch 400/469 | Gen/Dis Loss: 0.9189/0.6238 Time elapsed: 7.10 min Epoch: 038/100 | Batch 000/469 | Gen/Dis Loss: 1.3463/0.5419 Epoch: 038/100 | Batch 100/469 | Gen/Dis Loss: 1.0166/0.6045 Epoch: 038/100 | Batch 200/469 | Gen/Dis Loss: 0.9895/0.6320 Epoch: 038/100 | Batch 300/469 | Gen/Dis Loss: 0.9749/0.5621 Epoch: 038/100 | Batch 400/469 | Gen/Dis Loss: 1.0448/0.5945 Time elapsed: 7.24 min Epoch: 039/100 | Batch 000/469 | Gen/Dis Loss: 0.9662/0.5669 Epoch: 039/100 | Batch 100/469 | Gen/Dis Loss: 1.1476/0.5462 Epoch: 039/100 | Batch 200/469 | Gen/Dis Loss: 0.9662/0.5554 Epoch: 039/100 | Batch 300/469 | Gen/Dis Loss: 1.0850/0.6031 Epoch: 039/100 | Batch 400/469 | Gen/Dis Loss: 1.1491/0.6014 Time elapsed: 7.41 min Epoch: 040/100 | Batch 000/469 | Gen/Dis Loss: 0.9942/0.5999 Epoch: 040/100 | Batch 100/469 | Gen/Dis Loss: 0.9034/0.5979 Epoch: 040/100 | Batch 200/469 | Gen/Dis Loss: 1.1880/0.5693 Epoch: 040/100 | Batch 300/469 | Gen/Dis Loss: 1.0893/0.5933 Epoch: 040/100 | Batch 400/469 | Gen/Dis Loss: 1.0711/0.5501 Time elapsed: 7.59 min Epoch: 041/100 | Batch 000/469 | Gen/Dis Loss: 0.9100/0.5957 Epoch: 041/100 | Batch 100/469 | Gen/Dis Loss: 0.7538/0.5947 Epoch: 041/100 | Batch 200/469 | Gen/Dis Loss: 0.9743/0.5999 Epoch: 041/100 | Batch 300/469 | Gen/Dis Loss: 0.8305/0.6395 Epoch: 041/100 | Batch 400/469 | Gen/Dis Loss: 1.1106/0.6419 Time elapsed: 7.73 min Epoch: 042/100 | Batch 000/469 | Gen/Dis Loss: 1.1241/0.5890 Epoch: 042/100 | Batch 100/469 | Gen/Dis Loss: 0.8509/0.6164 Epoch: 042/100 | Batch 200/469 | Gen/Dis Loss: 1.2024/0.5684 Epoch: 042/100 | Batch 300/469 | Gen/Dis Loss: 0.9708/0.6378 Epoch: 042/100 | Batch 400/469 | Gen/Dis Loss: 1.1171/0.5501 Time elapsed: 7.85 min Epoch: 043/100 | Batch 000/469 | Gen/Dis Loss: 1.0931/0.5653 Epoch: 043/100 | Batch 100/469 | Gen/Dis Loss: 1.0468/0.5782 Epoch: 043/100 | Batch 200/469 | Gen/Dis Loss: 1.0359/0.6329 Epoch: 043/100 | Batch 300/469 | 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Epoch: 086/100 | Batch 100/469 | Gen/Dis Loss: 1.0762/0.6450 Epoch: 086/100 | Batch 200/469 | Gen/Dis Loss: 1.0070/0.6302 Epoch: 086/100 | Batch 300/469 | Gen/Dis Loss: 0.8805/0.6313 Epoch: 086/100 | Batch 400/469 | Gen/Dis Loss: 0.8568/0.6320 Time elapsed: 17.47 min Epoch: 087/100 | Batch 000/469 | Gen/Dis Loss: 0.9597/0.6527 Epoch: 087/100 | Batch 100/469 | Gen/Dis Loss: 0.8664/0.6339 Epoch: 087/100 | Batch 200/469 | Gen/Dis Loss: 1.0466/0.6181 Epoch: 087/100 | Batch 300/469 | Gen/Dis Loss: 0.8645/0.6272 Epoch: 087/100 | Batch 400/469 | Gen/Dis Loss: 0.8296/0.6125 Time elapsed: 17.71 min Epoch: 088/100 | Batch 000/469 | Gen/Dis Loss: 0.8497/0.6134 Epoch: 088/100 | Batch 100/469 | Gen/Dis Loss: 0.7984/0.6551 Epoch: 088/100 | Batch 200/469 | Gen/Dis Loss: 0.7777/0.6737 Epoch: 088/100 | Batch 300/469 | Gen/Dis Loss: 0.8157/0.6250 Epoch: 088/100 | Batch 400/469 | Gen/Dis Loss: 0.7993/0.6446 Time elapsed: 17.96 min Epoch: 089/100 | Batch 000/469 | Gen/Dis Loss: 0.8526/0.6219 Epoch: 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Batch 100/469 | Gen/Dis Loss: 0.8876/0.6303 Epoch: 092/100 | Batch 200/469 | Gen/Dis Loss: 0.9333/0.6201 Epoch: 092/100 | Batch 300/469 | Gen/Dis Loss: 0.8813/0.5981 Epoch: 092/100 | Batch 400/469 | Gen/Dis Loss: 0.9026/0.6128 Time elapsed: 18.94 min Epoch: 093/100 | Batch 000/469 | Gen/Dis Loss: 0.8874/0.6373 Epoch: 093/100 | Batch 100/469 | Gen/Dis Loss: 0.8537/0.6204 Epoch: 093/100 | Batch 200/469 | Gen/Dis Loss: 0.7982/0.6342 Epoch: 093/100 | Batch 300/469 | Gen/Dis Loss: 0.9005/0.6010 Epoch: 093/100 | Batch 400/469 | Gen/Dis Loss: 1.0532/0.6091 Time elapsed: 19.20 min Epoch: 094/100 | Batch 000/469 | Gen/Dis Loss: 0.9877/0.6426 Epoch: 094/100 | Batch 100/469 | Gen/Dis Loss: 0.8308/0.6501 Epoch: 094/100 | Batch 200/469 | Gen/Dis Loss: 0.9217/0.6269 Epoch: 094/100 | Batch 300/469 | Gen/Dis Loss: 0.9183/0.6632 Epoch: 094/100 | Batch 400/469 | Gen/Dis Loss: 0.8859/0.6128 Time elapsed: 19.46 min Epoch: 095/100 | Batch 000/469 | Gen/Dis Loss: 0.9032/0.6331 Epoch: 095/100 | Batch 100/469 | Gen/Dis Loss: 0.8298/0.6976 Epoch: 095/100 | Batch 200/469 | Gen/Dis Loss: 1.0004/0.6347 Epoch: 095/100 | Batch 300/469 | Gen/Dis Loss: 0.9161/0.6169 Epoch: 095/100 | Batch 400/469 | Gen/Dis Loss: 0.7622/0.6884 Time elapsed: 19.71 min Epoch: 096/100 | Batch 000/469 | Gen/Dis Loss: 0.8816/0.5997 Epoch: 096/100 | Batch 100/469 | Gen/Dis Loss: 0.9499/0.5969 Epoch: 096/100 | Batch 200/469 | Gen/Dis Loss: 0.8974/0.6214 Epoch: 096/100 | Batch 300/469 | Gen/Dis Loss: 0.8853/0.6259 Epoch: 096/100 | Batch 400/469 | Gen/Dis Loss: 0.8107/0.6027 Time elapsed: 19.95 min Epoch: 097/100 | Batch 000/469 | Gen/Dis Loss: 0.9242/0.6189 Epoch: 097/100 | Batch 100/469 | Gen/Dis Loss: 0.8917/0.6491 Epoch: 097/100 | Batch 200/469 | Gen/Dis Loss: 0.8729/0.6375 Epoch: 097/100 | Batch 300/469 | Gen/Dis Loss: 0.8848/0.5950 Epoch: 097/100 | Batch 400/469 | Gen/Dis Loss: 0.8502/0.6296 Time elapsed: 20.21 min Epoch: 098/100 | Batch 000/469 | Gen/Dis Loss: 0.9020/0.6453 Epoch: 098/100 | Batch 100/469 | Gen/Dis Loss: 1.1077/0.5882 Epoch: 098/100 | Batch 200/469 | Gen/Dis Loss: 0.9468/0.6364 Epoch: 098/100 | Batch 300/469 | Gen/Dis Loss: 0.8636/0.6313 Epoch: 098/100 | Batch 400/469 | Gen/Dis Loss: 0.9089/0.6911 Time elapsed: 20.45 min Epoch: 099/100 | Batch 000/469 | Gen/Dis Loss: 0.9101/0.6386 Epoch: 099/100 | Batch 100/469 | Gen/Dis Loss: 0.8036/0.6396 Epoch: 099/100 | Batch 200/469 | Gen/Dis Loss: 0.9393/0.6060 Epoch: 099/100 | Batch 300/469 | Gen/Dis Loss: 0.8776/0.6242 Epoch: 099/100 | Batch 400/469 | Gen/Dis Loss: 0.8244/0.6278 Time elapsed: 20.68 min Epoch: 100/100 | Batch 000/469 | Gen/Dis Loss: 0.8623/0.6496 Epoch: 100/100 | Batch 100/469 | Gen/Dis Loss: 0.9965/0.5964 Epoch: 100/100 | Batch 200/469 | Gen/Dis Loss: 0.8666/0.6306 Epoch: 100/100 | Batch 300/469 | Gen/Dis Loss: 1.1555/0.6634 Epoch: 100/100 | Batch 400/469 | Gen/Dis Loss: 0.9071/0.6545 Time elapsed: 20.94 min Total Training Time: 20.94 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')