84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
"""
|
|
View more, visit my tutorial page: https://mofanpy.com/tutorials/
|
|
My Youtube Channel: https://www.youtube.com/user/MorvanZhou
|
|
|
|
Dependencies:
|
|
torch: 0.4
|
|
torchvision
|
|
"""
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.utils.data as Data
|
|
import torchvision
|
|
|
|
# torch.manual_seed(1)
|
|
|
|
EPOCH = 1
|
|
BATCH_SIZE = 50
|
|
LR = 0.001
|
|
DOWNLOAD_MNIST = False
|
|
|
|
train_data = torchvision.datasets.MNIST(root='./mnist/', train=True, transform=torchvision.transforms.ToTensor(), download=DOWNLOAD_MNIST,)
|
|
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
|
|
|
|
test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
|
|
|
|
# !!!!!!!! Change in here !!!!!!!!! #
|
|
test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000].cuda()/255. # Tensor on GPU
|
|
test_y = test_data.test_labels[:2000].cuda()
|
|
|
|
|
|
class CNN(nn.Module):
|
|
def __init__(self):
|
|
super(CNN, self).__init__()
|
|
self.conv1 = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2,),
|
|
nn.ReLU(), nn.MaxPool2d(kernel_size=2),)
|
|
self.conv2 = nn.Sequential(nn.Conv2d(16, 32, 5, 1, 2), nn.ReLU(), nn.MaxPool2d(2),)
|
|
self.out = nn.Linear(32 * 7 * 7, 10)
|
|
|
|
def forward(self, x):
|
|
x = self.conv1(x)
|
|
x = self.conv2(x)
|
|
x = x.view(x.size(0), -1)
|
|
output = self.out(x)
|
|
return output
|
|
|
|
cnn = CNN()
|
|
|
|
# !!!!!!!! Change in here !!!!!!!!! #
|
|
cnn.cuda() # Moves all model parameters and buffers to the GPU.
|
|
|
|
optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)
|
|
loss_func = nn.CrossEntropyLoss()
|
|
|
|
for epoch in range(EPOCH):
|
|
for step, (x, y) in enumerate(train_loader):
|
|
|
|
# !!!!!!!! Change in here !!!!!!!!! #
|
|
b_x = x.cuda() # Tensor on GPU
|
|
b_y = y.cuda() # Tensor on GPU
|
|
|
|
output = cnn(b_x)
|
|
loss = loss_func(output, b_y)
|
|
optimizer.zero_grad()
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
if step % 50 == 0:
|
|
test_output = cnn(test_x)
|
|
|
|
# !!!!!!!! Change in here !!!!!!!!! #
|
|
pred_y = torch.max(test_output, 1)[1].cuda().data # move the computation in GPU
|
|
|
|
accuracy = torch.sum(pred_y == test_y).type(torch.FloatTensor) / test_y.size(0)
|
|
print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.cpu().numpy(), '| test accuracy: %.2f' % accuracy)
|
|
|
|
|
|
test_output = cnn(test_x[:10])
|
|
|
|
# !!!!!!!! Change in here !!!!!!!!! #
|
|
pred_y = torch.max(test_output, 1)[1].cuda().data # move the computation in GPU
|
|
|
|
print(pred_y, 'prediction number')
|
|
print(test_y[:10], 'real number')
|