在Keras中跨两个平行层的反向传播

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Add
from tensorflow.keras.models import Model

# 定义输入层
input_layer = Input(shape=(input_dim,))

# 定义平行层1
hidden_layer1 = Dense(64, activation='relu')(input_layer)

# 定义平行层2
hidden_layer2 = Dense(64, activation='relu')(input_layer)

# 将两个平行层的输出相加
merged_layer = Add()([hidden_layer1, hidden_layer2])

# 定义输出层
output_layer = Dense(output_dim, activation='softmax')(merged_layer)

# 构建模型
model = Model(inputs=input_layer, outputs=output_layer)

# 定义损失函数和优化器
model.compile(loss='categorical_crossentropy', optimizer='adam')

# 训练模型
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs)

# 使用模型进行预测
predictions = model.predict(x_test)