##此處x，y為資料集的tensor
torch_dataset = data.tensordataset(data_tensor=x, target_tensor=y)
loader = data.dataloader(
dataset=torch_dataset,
batch_size=batch_size,
shuffle=true, ##是否打亂次序
num_workers=2 ##執行緒
)for epoch in range(3):
for step, (batch_x, batch_y) in enumerate(loader):
## train .....
class cnn(nn.module):
def __init__(self):
super(cnn, self).__init__()
self.conv1 = nn.sequential(#(1, 28, 28)
nn.conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2),#(16, 28, 28)
nn.relu(),
nn.maxpool2d(kernel_size=2),#(16, 14, 14)
)self.conv2 = nn.sequential(
nn.conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2),#(32, 14, 14)
nn.relu(),
nn.maxpool2d(kernel_size=2),#(32, 7, 7)
)self.out = nn.linear(32*7*7, 10)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x) #(batch, 32, 7, 7)
x = x.view(x.size(0), -1) #(batch, 32*7*7)
output = self.out(x)
return output
要從tensor取資料出來，應該用tensor.data.numpy()
要把邏輯陣列先astype為int 1/0，然後除的時候要轉化為float
for epoch in range(epoch):
for step, (x, y) in enumerate(train_loader):
output = cnn(x)
loss = loss_func(output, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % 50 == 0:
test_output = cnn(test_x)
pred_y = torch.max(test_output, 1)[1].data.numpy()
accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0))
print('epoch: ', epoch,
'| train loss: %.4f' % loss.data.numpy(),
'| test accuracy: %.4f' % accuracy)
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