请问为什么train_label是20张图，train_data是5张？这样的话如何计算loss? about precipitation-nowcasting HOT 2 CLOSED

`# build model
convlstm_encoder_params = [
    [
        OrderedDict({'conv1_leaky_1': [1, 8, 7, 5, 1]}),
        OrderedDict({'conv2_leaky_1': [64, 192, 5, 3, 1]}),
        OrderedDict({'conv3_leaky_1': [192, 192, 3, 2, 1]}),
    ],

    [
        ConvLSTM(input_channel=8, num_filter=64, b_h_w=(batch_size, 96, 96),
                 kernel_size=3, stride=1, padding=1),
        ConvLSTM(input_channel=192, num_filter=192, b_h_w=(batch_size, 32, 32),
                 kernel_size=3, stride=1, padding=1),
        ConvLSTM(input_channel=192, num_filter=192, b_h_w=(batch_size, 16, 16),
                 kernel_size=3, stride=1, padding=1),
    ]
]

convlstm_forecaster_params = [
    [
        OrderedDict({'deconv1_leaky_1': [192, 192, 4, 2, 1]}),
        OrderedDict({'deconv2_leaky_1': [192, 64, 5, 3, 1]}),
        OrderedDict({
            'deconv3_leaky_1': [64, 8, 7, 5, 1],
            'conv3_leaky_2': [8, 8, 3, 1, 1],
            'conv3_3': [8, 1, 1, 1, 0]
        }),
    ],

    [
        ConvLSTM(input_channel=192, num_filter=192, b_h_w=(batch_size, 16, 16),
                 kernel_size=3, stride=1, padding=1),
        ConvLSTM(input_channel=192, num_filter=192, b_h_w=(batch_size, 32, 32),
                 kernel_size=3, stride=1, padding=1),
        ConvLSTM(input_channel=64, num_filter=64, b_h_w=(batch_size, 96, 96),
                 kernel_size=3, stride=1, padding=1),
    ]
]