老七的神床（3 / 8）

    t = torch.tensor(t).view(6, 6)

    return t, self.data_dict_y[index]

    def __len__(self):

    return len(self.data_dict_y)

    def cnn_classification():

    batch_size = 256

    trainDataLoader = DataLoader(TrainingDataSet(), batch_size=batch_size, shuffle=False)

    testDataLoader = DataLoader(TestDataSet(), batch_size=batch_size, shuffle=False)

    epoch_num = 200

    #lr = 0.001

    lr = 0.001

    net = VGGBaseSimpleS2().to(device)

    print(net)

    # loss

    loss_func = nn.CrossEntropyLoss()

    # optimizer

    optimizer = torch.optim.Adam(net.parameters(), lr=lr)

    # optimizer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)

    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.9)

    if not os.path.exists(“logCNN“):

    os.mkdir(“logCNN“)

    writer = tensorboardX.SummaryWriter(“logCNN“)

    for epoch in range(epoch_num):

    train_sum_loss = 0

    train_sum_correct = 0

    train_sum_fp = 0

    train_sum_fn = 0

    train_sum_tp = 0

    train_sum_tn = 0

    for i, data in enumerate(trainDataLoader):