train_nets.py 文件源码

def model_lstm(input_shape):

    inp = Input(shape=input_shape)
    model = inp

    if input_shape[0] > 2: model = Conv1D(filters=24, kernel_size=(3), activation='relu')(model)
#    if input_shape[0] > 0: model = TimeDistributed(Conv1D(filters=24, kernel_size=3, activation='relu'))(model)
    model = LSTM(16)(model)
    model = Activation('relu')(model)
    model = Dropout(0.2)(model)
    model = Dense(16)(model)
    model = Activation('relu')(model)
    model = BatchNormalization()(model)

    model = Dense(1)(model)
    model = Activation('sigmoid')(model)

    model = Model(inp, model)
    return model

# %% 

# Conv-1D architecture. Just one sample as input