keras搭建的双通道卷积神经网络，添加的混淆矩阵不能够运行出错，python代码

以下是一个使用Keras搭建的双通道卷积神经网络，并添加混淆矩阵的Python代码示例：

from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, concatenate
from sklearn.metrics import confusion_matrix

# 定义输入形状
input_shape = (28, 28, 1)

# 定义左通道输入
left_input = Input(input_shape)
left_conv1 = Conv2D(32, kernel_size=(3, 3), activation='relu')(left_input)
left_pool1 = MaxPooling2D(pool_size=(2, 2))(left_conv1)
left_conv2 = Conv2D(64, kernel_size=(3, 3), activation='relu')(left_pool1)
left_pool2 = MaxPooling2D(pool_size=(2, 2))(left_conv2)
left_flat = Flatten()(left_pool2)

# 定义右通道输入
right_input = Input(input_shape)
right_conv1 = Conv2D(32, kernel_size=(3, 3), activation='relu')(right_input)
right_pool1 = MaxPooling2D(pool_size=(2, 2))(right_conv1)
right_conv2 = Conv2D(64, kernel_size=(3, 3), activation='relu')(right_pool1)
right_pool2 = MaxPooling2D(pool_size=(2, 2))(right_conv2)
right_flat = Flatten()(right_pool2)

# 合并左右通道
merged = concatenate([left_flat, right_flat])

# 添加全连接层
dense1 = Dense(128, activation='relu')(merged)
predictions = Dense(10, activation='softmax')(dense1)

# 定义模型
model = Model(inputs=[left_input, right_input], outputs=predictions)

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型

# 添加混淆矩阵
def get_confusion_matrix(y_true, y_pred):
    cm = confusion_matrix(y_true, y_pred)
    print("Confusion Matrix:\n", cm)

# 训练模型，并在每个epoch结束时计算混淆矩阵
model.fit([X_train_left, X_train_right], y_train, epochs=10, batch_size=32, 
          validation_data=([X_test_left, X_test_right], y_test), 
          callbacks=[LambdaCallback(on_epoch_end=lambda epoch, logs: get_confusion_matrix(y_test.argmax(axis=1), 
                                                                                            model.predict([X_test_left, 
                                                                                                           X_test_right]).argmax(axis=1)))])

这里使用了LambdaCallback回调函数，在每个epoch结束时计算混淆矩阵。其中，`X_train_left`和`X_train_right`分别代表左通道和右通道的训练数据，`y_train`代表训练数据的标签；`X_test_left`和`X_test_right`分别代表左通道和右通道的测试数据，`y_test`代表测试数据的标签。请根据你的数据进行相应的修改。