def cnn_lstm_attention_model(step, input_dim):
"""
step: 步长
input_dim：输入维度
"""
inputs = Input(shape=(step, input_dim))
x = Conv1D(filters=64, kernel_size=1,
activation='elu')(inputs) # , padding = 'same'
# x = Conv1D(filters=32, kernel_size=1, activation='elu')(x) # , padding = 'same'
print("Conv1D 111", K.int_shape(x))
x = Dropout(0.3)(x)
# x = Conv1D(filters=64, kernel_size=2, activation='elu')(x) # , padding = 'same'
# x = MaxPooling1D(2)(x)
#lstm_out = Bidirectional(LSTM(lstm_units, activation='relu'), name='bilstm')(x)
# 对于GPU可以使用CuDNNLSTM
#lstm_out = Bidirectional(LSTM(lstm_units, return_sequences=True))(x)
lstm_out = LSTM(64, return_sequences=True)(x)
# lstm_out = LSTM(16, return_sequences=True)(x)
# lstm_out = LSTM(50, activation='elu', return_sequences=True, dropout=0.2)(lstm_out)
lstm_out = Dropout(0.2)(lstm_out)
print("lstm out", K.int_shape(lstm_out))
# CNN和LSTM已经对特征进行提取了，这时候再用注意力机制效果可能会更好
attention_mul = attention_3d_block2(lstm_out)
attention_mul = Flatten()(attention_mul)
output = Dense(1, activation='sigmoid')(attention_mul)
model = Model(inputs=[inputs], outputs=output)
return model
def cnn_lstm_step4(step, input_dim):
"""
step: 步长
input_dim：输入维度
"""
inputs = Input(shape=(step, input_dim))
# x = Conv1D(filters=32, kernel_size=1, activation='relu')(inputs) # , padding = 'same'
x = Conv1D(filters=64, kernel_size=1,
activation='elu')(inputs) # , padding = 'same'
x = Conv1D(filters=32, kernel_size=1,
activation='elu')(x) # , padding = 'same'
print("Conv1D 111", K.int_shape(x))
x = Dropout(0.3)(x)
lstm_out = LSTM(64, return_sequences=True, activation='elu')(x)
lstm_out = Dropout(0.2)(lstm_out)
print("lstm out", K.int_shape(lstm_out))
# CNN和LSTM已经对特征进行提取了，这时候再用注意力机制效果可能会更好
attention_mul = attention_3d_block2(lstm_out)
attention_mul = Flatten()(attention_mul)
output = Dense(1, activation='sigmoid')(attention_mul)
model = Model(inputs=[inputs], outputs=output)
return model