input1 = Input(shape=(look_back, n_inp1)) conv11 = Conv1D(filters=48, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_uniform')(input1) pool11 = MaxPool1D(pool_size=2, padding='valid')(conv11) conv12 = Conv1D(filters=48, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_uniform')(pool11) pool12 = MaxPool1D(pool_size=2, padding='valid')(conv12) gru11 = GRU(32, return_sequences=True)(pool12) # drop1 = Dropout(0.2)(gru11) gru12 = GRU(32, return_sequences=True)(gru11) time_last = tf.transpose(gru12, [0, 2, 1]) att1 = Dense(look_back, activation='tanh')(time_last) att2 = Dense(look_back, activation='softmax', use_bias=False)(att1) time_att = Multiply()([time_last, att2]) out1 = tf.reduce_sum(time_att, axis=-1)打印模型

要打印上述代码中的模型结构，可以使用Keras的`Model`类的`summary()`方法。以下是打印模型的示例代码：

from keras.layers import Input, Conv1D, MaxPool1D, GRU, Dense, Multiply
from keras.models import Model

# 输入层
input1 = Input(shape=(look_back, n_inp1))

# 卷积层和池化层
conv11 = Conv1D(filters=48, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_uniform')(input1)
pool11 = MaxPool1D(pool_size=2, padding='valid')(conv11)
conv12 = Conv1D(filters=48, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_uniform')(pool11)
pool12 = MaxPool1D(pool_size=2, padding='valid')(conv12)

# GRU层
gru11 = GRU(32, return_sequences=True)(pool12)
gru12 = GRU(32, return_sequences=True)(gru11)

# 时间注意力机制
time_last = tf.transpose(gru12, [0, 2, 1])
att1 = Dense(look_back, activation='tanh')(time_last)
att2 = Dense(look_back, activation='softmax', use_bias=False)(att1)
time_att = Multiply()([time_last, att2])
out1 = tf.reduce_sum(time_att, axis=-1)

# 创建模型
model = Model(inputs=input1, outputs=out1)

# 打印模型结构
model.summary()

请确保在运行代码之前导入所需的库，并根据需要调整输入形状和其他层的参数。运行上述代码后，将打印出模型的详细结构信息，包括每个层的名称、输出形状和总参数数量等。