标签：real nn scratch self error GAN fake Implement data
GANs from Scratch 1: A deep introduction. With code in PyTorch and TensorFlow
修改文章代码中的错误后的代码如下：
import torch
from torch import nn, optim
from torch.autograd.variable import Variable
from torchvision import transforms, datasets
import matplotlib.pyplot as plt
DATA_FOLDER = 'D:/WorkSpace/Data/torchvision_data'
def mnist_data():
compose = transforms.Compose(
[transforms.ToTensor(),
# transforms.Normalize((.5, .5, .5), (.5, .5, .5))
transforms.Normalize([0.5], [0.5]) # MNIST只有一个通道
])
return datasets.MNIST(root=DATA_FOLDER, train=True, transform=compose)
# Load data
data = mnist_data()
# Create loader with data, so that we can iterate over it
data_loader = torch.utils.data.DataLoader(data, batch_size=64, shuffle=True)
# Num batches
num_batches = len(data_loader)
class DiscriminatorNet(torch.nn.Module):
"""
A three hidden-layer discriminative neural network
"""
def __init__(self):
super(DiscriminatorNet, self).__init__()
n_features = 784
n_out = 1
self.hidden0 = nn.Sequential(
nn.Linear(n_features, 1024),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.hidden1 = nn.Sequential(
nn.Linear(1024, 512),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.hidden2 = nn.Sequential(
nn.Linear(512, 256),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.out = nn.Sequential(
torch.nn.Linear(256, n_out),
torch.nn.Sigmoid()
)
def forward(self, x):
x = self.hidden0(x)
x = self.hidden1(x)
x = self.hidden2(x)
x = self.out(x)
return x
def images_to_vectors(images):
return images.view(images.size(0), 784)
def vectors_to_images(vectors):
return vectors.view(vectors.size(0), 1, 28, 28)
class GeneratorNet(torch.nn.Module):
"""
A three hidden-layer generative neural network
"""
def __init__(self):
super(GeneratorNet, self).__init__()
n_features = 100
n_out = 784
self.hidden0 = nn.Sequential(
nn.Linear(n_features, 256),
nn.LeakyReLU(0.2)
)
self.hidden1 = nn.Sequential(
nn.Linear(256, 512),
nn.LeakyReLU(0.2)
)
self.hidden2 = nn.Sequential(
nn.Linear(512, 1024),
nn.LeakyReLU(0.2)
)
self.out = nn.Sequential(
nn.Linear(1024, n_out),
nn.Tanh()
)
def forward(self, x):
x = self.hidden0(x)
x = self.hidden1(x)
x = self.hidden2(x)
x = self.out(x)
return x
# Noise
def noise(size):
n = Variable(torch.randn(size, 100))
if torch.cuda.is_available(): return n.cuda()
return n
discriminator = DiscriminatorNet()
generator = GeneratorNet()
if torch.cuda.is_available():
discriminator.cuda()
generator.cuda()
# Optimizers
d_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002)
g_optimizer = optim.Adam(generator.parameters(), lr=0.0002)
# Loss function
loss = nn.BCELoss()
# Number of steps to apply to the discriminator
d_steps = 1 # In Goodfellow et. al 2014 this variable is assigned to 1
# Number of epochs
num_epochs = 200
def real_data_target(size):
'''
Tensor containing ones, with shape = size
'''
data = Variable(torch.ones(size, 1))
if torch.cuda.is_available(): return data.cuda()
return data
def fake_data_target(size):
'''
Tensor containing zeros, with shape = size
'''
data = Variable(torch.zeros(size, 1))
if torch.cuda.is_available(): return data.cuda()
return data
def train_discriminator(optimizer, real_data, fake_data):
# Reset gradients
optimizer.zero_grad()
# 1.1 Train on Real Data
prediction_real = discriminator(real_data)
# Calculate error and backpropagate
error_real = loss(prediction_real, real_data_target(real_data.size(0)))
error_real.backward()
# 1.2 Train on Fake Data
prediction_fake = discriminator(fake_data)
# Calculate error and backpropagate
error_fake = loss(prediction_fake, fake_data_target(real_data.size(0)))
error_fake.backward()
# 1.3 Update weights with gradients
optimizer.step()
# Return error
return error_real + error_fake, prediction_real, prediction_fake
def train_generator(optimizer, fake_data):
# 2. Train Generator
# Reset gradients
optimizer.zero_grad()
# Sample noise and generate fake data
prediction = discriminator(fake_data)
# Calculate error and backpropagate
error = loss(prediction, real_data_target(prediction.size(0)))
error.backward()
# Update weights with gradients
optimizer.step()
# Return error
return error
num_test_samples = 16
test_noise = noise(num_test_samples)
for epoch in range(num_epochs):
for n_batch, (real_batch,_) in enumerate(data_loader):
# 1. Train Discriminator
real_data = Variable(images_to_vectors(real_batch))
if torch.cuda.is_available(): real_data = real_data.cuda()
# Generate fake data
fake_data = generator(noise(real_data.size(0))).detach()
# Train D
d_error, d_pred_real, d_pred_fake = train_discriminator(d_optimizer,
real_data, fake_data)
# 2. Train Generator
# Generate fake data
fake_data = generator(noise(real_batch.size(0)))
# Train G
g_error = train_generator(g_optimizer, fake_data)
# Display Progress
print('epoch ', epoch, ': ','d_error is ', d_error, 'g_error is ', g_error)
if (epoch) % 20 == 0:
test_images = vectors_to_images(generator(test_noise)).data.cpu()
fig = plt.figure()
for i in range(len(test_images)):
ax = fig.add_subplot(4, 4, i+1)
ax.imshow(test_images[i][0], cmap=plt.cm.gray)
plt.show()标签：real,nn,scratch,self,error,GAN,fake,Implement,data 来源： https://www.cnblogs.com/ZeroTensor/p/10851877.html
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