keras 搭建网络模型（lstm 和 convlstm）一些参数理解

1 所有网络模型的输入， input_shape 都不包含samples。训练数据和测试数据的shape才是（samples, input_shape）。

  eg: 有一批100张32*32的RGB图片，若要处理这批图片（分类，目标识别），搭建网络模型的input_shape 应为（3， 32， 32）（假设dataformat格式为channels_first）

2 搭建网络模型时既可选择指定batch_input_shape，也可选择指定input_shape。
batch_input_shape 和input_shape的关系是：batch_input_shape =(batch_size, input_shape)

3 keras 有Sequential 和Model两种搭建网络的方式，Sequential只能顺序搭建无分支结构，Model则可以搭建多分支结构。

以Sequential 和Model两种方式搭建同样的网络结构

from keras.layers import (
Input,
ConvLSTM2D,
)

from keras.models import Model
from keras.models import Sequential


def Seq():
    '''
     input_shape 为(time_steps, map_height, map_width, channels)
     time_steps 就是将一个样例分为多少个时间点读入，x1,x2...,xt，的t
     return_sequences为True时每一个时间点都有输出
     return_sequences为False时，只有最后一个时间点有输出
    '''
    seq = Sequential()
    seq.add(ConvLSTM2D(filters=30, kernel_size=(3, 3),
                       input_shape=(15, 40, 40, 3),
                       padding='same', return_sequences=True, data_format='channels_last'))

    seq.add(ConvLSTM2D(filters=50, kernel_size=(3, 3),
                       padding='same', return_sequences=True, data_format='channels_last'))

    seq.add(ConvLSTM2D(filters=60, kernel_size=(3, 3),
                       padding='same', return_sequences=True, data_format='channels_last'))

    seq.add(ConvLSTM2D(filters=70, kernel_size=(3, 3),
                       padding='same', return_sequences=False, data_format='channels_last'))

    seq.summary()

def main():
    Inputs=[]
    Outputs=[]
    input = Input(shape=(15, 40, 40, 3))
    Inputs.append(input)
    convlstm1= ConvLSTM2D(filters=30, kernel_size=(3,3),
                          padding='same', return_sequences=True, data_format='channels_last')(input)
    convlstm2 = ConvLSTM2D(filters=50, kernel_size=(3, 3),
                           padding='same', return_sequences=True, data_format='channels_last')(convlstm1)
    convlstm3 = ConvLSTM2D(filters=60, kernel_size=(3, 3),
                           padding='same', return_sequences=True, data_format='channels_last')(convlstm2)
    convlstm4 = ConvLSTM2D(filters=70, kernel_size=(3, 3),
                           padding='same', return_sequences=False, data_format='channels_last')(convlstm3)
    Outputs.append(convlstm4)
    model =Model(inputs=input, outputs=convlstm4)
    model.summary()

if __name__ == '__main__':
    Seq()
    main()

4 LSTM 、ConvLSTM和SimpleRNN等RNN layer input_shape格式应该加上time_steps，格式为(time_steps, input_size)