keras model.summary（）不显示所有模型层

Question

我建立了一个keras功能模型，当我绘制摘要时，第一个conv1d层没有出现...

from tensorflow.keras.layers import Input, Dense, LSTM, Dropout, TimeDistributed, Conv1D, 
     MaxPooling1D, Flatten
from tensorflow.keras import Model, regularizers, initializers

tensor_input = Input(shape=(Xn.shape[1], Xn.shape[2]), name='main_inputs')

xy = TimeDistributed(Conv1D(filters= 10, kernel_size= 3,
                            activation=params['activationCNN1']))
xy = TimeDistributed(MaxPooling1D(pool_size= 2))

xy = TimeDistributed(Conv1D(filters=5, kernel_size= 2,
                        activation=params['activationCNN1']), name='Cnn1d-2')
xy = TimeDistributed(MaxPooling1D(pool_size= 2), name='MaxPool')
xy = TimeDistributed(Flatten(), name='Flatten')

xy = LSTM(params['unitsLstm1'],activation=params['activationLSTM1'],
          return_sequences=False, stateful=params['stateful'], 
          name='Hlayer1')(tensor_input)
xy = Dropout(rate = params['dropout1'])(xy)

xy = Dense(params['unitsDense1'], activation=params['activationDense1'],
           kernel_initializer= initializers.he_uniform(), name='Dense1')(xy)
xy = Dropout(rate = params['dropout2'])(xy)

out = Dense(autres_param['timestepsOut'], activation=params['activationDenseOutput'],
            kernel_initializer= initializers.he_uniform(), name='DenseOutput')(xy) 
model = Model(inputs=tensor_input, outputs=out)

model.compile(optimizer=optimizer, loss=params['loss'])
# summarize layers
print(model.summary())

我得到的是：仅输入，lstm，辍学和密集层...

培训似乎有效，但是所有层都处于活动状态？我如何获得完整的摘要？

Answer 1

您需要调用前一个输出张量上的每一层：

tensor_input = Input(shape=(Xn.shape[1], Xn.shape[2]), name='main_inputs')

xy = TimeDistributed(Conv1D(filters= 10, kernel_size= 3,
                            activation=params['activationCNN1']))(tensor_input)
xy = TimeDistributed(MaxPooling1D(pool_size= 2))(xy)

xy = TimeDistributed(Conv1D(filters=5, kernel_size= 2,
                        activation=params['activationCNN1']), name='Cnn1d-2')(xy)
xy = TimeDistributed(MaxPooling1D(pool_size= 2), name='MaxPool')(xy)
xy = TimeDistributed(Flatten(), name='Flatten')(xy)

xy = LSTM(params['unitsLstm1'],activation=params['activationLSTM1'],
          return_sequences=False, stateful=params['stateful'], 
          name='Hlayer1')(xy)

通常，摘要擅长显示模型的所有部分。如果摘要中未显示某些内容，则可以安全地假定这表示它不在模型中。

Answer 2

在

xy = LSTM(params['unitsLstm1'],activation=params['activationLSTM1'],
          return_sequences=False, stateful=params['stateful'], 
          name='Hlayer1')(tensor_input)

您在LSTM上呼叫tensor_input，因此基本上所有这一切：

xy = TimeDistributed(Conv1D(filters= 10, kernel_size= 3,
                            activation=params['activationCNN1']))
xy = TimeDistributed(MaxPooling1D(pool_size= 2))

xy = TimeDistributed(Conv1D(filters=5, kernel_size= 2,
                        activation=params['activationCNN1']), name='Cnn1d-2')
xy = TimeDistributed(MaxPooling1D(pool_size= 2), name='MaxPool')
xy = TimeDistributed(Flatten(), name='Flatten')

被忽略。