Files
2017-05-28 20:06:40 +09:00

105 lines
3.0 KiB
Python

import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
from logger import Logger
# MNIST Dataset
dataset = dsets.MNIST(root='./data',
train=True,
transform=transforms.ToTensor(),
download=True)
# Data Loader (Input Pipeline)
data_loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=100,
shuffle=True)
def to_np(x):
return x.data.cpu().numpy()
def to_var(x):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x)
# Neural Network Model (1 hidden layer)
class Net(nn.Module):
def __init__(self, input_size=784, hidden_size=500, num_classes=10):
super(Net, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
net = Net()
if torch.cuda.is_available():
net.cuda()
# Set the logger
logger = Logger('./logs')
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.00001)
data_iter = iter(data_loader)
iter_per_epoch = len(data_loader)
total_step = 50000
# Start training
for step in range(total_step):
# Reset the data_iter
if (step+1) % iter_per_epoch == 0:
data_iter = iter(data_loader)
# Fetch the images and labels and convert them to variables
images, labels = next(data_iter)
images, labels = to_var(images.view(images.size(0), -1)), to_var(labels)
# Forward, backward and optimize
optimizer.zero_grad() # zero the gradient buffer
outputs = net(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# Compute accuracy
_, argmax = torch.max(outputs, 1)
accuracy = (labels == argmax.squeeze()).float().mean()
if (step+1) % 100 == 0:
print ('Step [%d/%d], Loss: %.4f, Acc: %.2f'
%(step+1, total_step, loss.data[0], accuracy.data[0]))
#============ TensorBoard logging ============#
# (1) Log the scalar values
info = {
'loss': loss.data[0],
'accuracy': accuracy.data[0]
}
for tag, value in info.items():
logger.scalar_summary(tag, value, step+1)
# (2) Log values and gradients of the parameters (histogram)
for tag, value in net.named_parameters():
tag = tag.replace('.', '/')
logger.histo_summary(tag, to_np(value), step+1)
logger.histo_summary(tag+'/grad', to_np(value.grad), step+1)
# (3) Log the images
info = {
'images': to_np(images.view(-1, 28, 28)[:10])
}
for tag, images in info.items():
logger.image_summary(tag, images, step+1)