77a6ec73c53c5cc62a2ae451694537144afa5644,mnist/main.py,,test,#Any#,74

Before Change

    test_loss = test_loss.data[0]
    test_loss /= (test_data.size(0) / TEST_BATCH_SIZE) // criterion averages over batch size
    print("TEST SET RESULTS:" + " " * 20)
    print("Average loss: {:.4f}".format(test_loss))

for epoch in range(1, NUM_EPOCHS+1):
    train(epoch)

After Change

        batch_data.data[:] = test_data[i:i+TEST_BATCH_SIZE]
        batch_targets.data[:] = test_labels[i:i+TEST_BATCH_SIZE]
        output = model(batch_data)
        test_loss += criterion(output, batch_targets)
        pred = output.data.max(1)[1]
        correct += pred.long().eq(batch_targets.data.long()).sum()

    test_loss = test_loss.data[0]
    test_loss /= (test_data.size(0) / TEST_BATCH_SIZE) // criterion averages over batch size
    print("TEST SET RESULTS:" + " " * 20)
    print("Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
        test_loss, correct, test_data.size(0),
        float(correct)/test_data.size(0)*100))

for epoch in range(1, NUM_EPOCHS+1):
    train(epoch)

Instances