031392ff2cbb5703558d17a676a456521f754190,mnist/main.py,,,#,54

Before Change

        x = self.relu(self.fc2(x))
        return self.softmax(x)

model = Net()
if cuda == True:
    model.cuda()

criterion = nn.NLLLoss()

// Training settings
BATCH_SIZE = 150
TEST_BATCH_SIZE = 1000
NUM_EPOCHS = 2

optimizer = optim.SGD(model.parameters(), lr=1e-2, momentum=0.9)

def train(epoch):
    batch_data_t = torch.FloatTensor(BATCH_SIZE, 1, 28, 28)
    batch_targets_t = torch.LongTensor(BATCH_SIZE)

After Change

        test_loss, correct, test_data.size(0),
        float(correct)/test_data.size(0)*100))

for epoch in range(1, opt.nEpochs+1):
    train(epoch)
    test(epoch)

Instances