K.set_learning_phase(0) base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3), ) for layer in base_model.layers: layer.trainable=False K.set_learning_phase(1) x = base_model.output x = layers.GlobalMaxPooling2D()(x) x = layers.Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.0001))(x) x = layers.Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.0001))(x) predictions = layers.Dense(4, activation='softmax')(x) model = Model(inputs=base_model.input, outputs=predictions) model.summary()怎么在这段代码中加入inception模块

你可以在 `base_model` 中添加 Inception 模块。以下是示例代码：

from keras.applications.inception_v3 import InceptionV3

# 加载 InceptionV3
inception_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# 在 base_model 中添加 Inception 模块
x = inception_model.output
x = layers.GlobalMaxPooling2D()(x)
x = layers.Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.0001))(x)
x = layers.Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.0001))(x)
predictions = layers.Dense(4, activation='softmax')(x)
model = Model(inputs=inception_model.input, outputs=predictions)
model.summary()

在这个例子中，我们首先加载了 InceptionV3 模型，并将其作为一个新的层添加到 `base_model` 中。注意，我们使用了 InceptionV3 的输出作为 `x`。最后，我们根据需要添加额外的全连接层和输出层。