如何在Keras模型上添加Pooling层?

我正在使用tensorflow和来自Google的colab notbook加载神经网络。我删除了输出层的完全连接层,并添加了仅与一个神经元完全连接的另一层,然后冻结了另一层。我正在使用tf.keras.application.MobileNetV2,而我正在使用mledu-datasets/cats_and_dogs。我只想训练这个增加的输出层,但是我遇到了“错误”。我想我必须使用

添加一个Pooling层

我的代码如下:

model = tf.keras.applications.mobilenet_v2.MobileNetV2(input_shape=(IMG_HEIGHT,IMG_WIDTH,3),alpha=1.0,include_top=False,weights='imagenet',input_tensor=None,pooling='max',classes=2)
model.summary()
penultimate_layer = model.layers[-2]  # layer that you want to connect your new FC layer to 
new_top_layer = tf.keras.layers.Dense(1)(penultimate_layer.output) # create new FC layer and connect it to the rest of the model
new_model = tf.keras.models.Model(model.input,new_top_layer)  # define your new model


ultima_layer = new_model.layers[-1]
new_new_top_layer = tf.keras.layers.AveragePooling2D(pool_size=(2,2),strides=None,padding='valid',data_format=None)


new_new_model = tf.keras.models.Model(new_model.input,new_new_top_layer)

最后,在最后一层做之前冻结所有图层的权重:

for layer in new_model.layers[:-2]:
    layer.trainable = False
new_model.layers[-1].trainable = True

对于培训:

new_model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])


history = new_model.fit_generator(
    train_data_gen,steps_per_epoch = total_train // batch_size,epochs = epochs,validation_data = val_data_gen,validation_steps = total_val // batch_size
)

我遇到以下错误

AttributeError                            Traceback (most recent call last)

<ipython-input-18-05a947aac1cd> in <module>()
      8 ultima_layer = new_model.layers[-1]
      9 new_new_top_layer = tf.keras.layers.AveragePooling2D(pool_size=(2,data_format=None)
---> 10 new_new_model = tf.keras.models.Model(new_model.input,new_new_top_layer)
     11 
     12 # tf.keras.layers.MaxPooling2D(pool_size=(2,data_format=None)

5 frames

/tensorflow-2.0.0/python3.6/tensorflow_core/python/keras/engine/base_layer_utils.py in _create_keras_history_helper(tensors,processed_ops,created_layers)
    208     if getattr(tensor,'_keras_history',None) is not None:
    209       continue
--> 210     op = tensor.op  # The Op that created this Tensor.
    211     if op not in processed_ops:
    212       # Recursively set `_keras_history`.

AttributeError: 'AveragePooling2D' object has no attribute 'op'

谢谢

vighi 回答:如何在Keras模型上添加Pooling层?

这可能会有所帮助。我在像这样组成新模型之前添加了PoolingLayer,但没有得到您看到的错误。我希望这会有所帮助:

new_top_layer = tf.keras.layers.Dense(1)(penultimate_layer.output) # create new FC layer and connect it to the rest of the model
new_new_top_layer = tf.keras.layers.AveragePooling2D(pool_size=(2,2),strides=None,padding='valid',data_format=None)(new_top_layer)
new_model = tf.keras.models.Model(inputs=model.input,outputs=new_new_top_layer)  # define your new model
,

您可以在实例化pooling='avg'时传递MobileNetV2参数,以便获得最后一层的全局平均池值(因为模型不包括顶层)。由于这是一个二进制分类问题,因此您的最后一个/输出层应该具有一个具有单节点和S形激活功能的密集层。因此,您可以添加具有单个节点的最后一个/输出Dense层,并按如下所示提供基本模型的输出。

model = tf.keras.applications.mobilenet_v2.MobileNetV2(input_shape=(IMG_HEIGHT,IMG_WIDTH,3),alpha=1.0,include_top=False,weights='imagenet',input_tensor=None,pooling='avg',classes=2)
# model.summary()
penultimate_layer = model.layers[-1]  # layer that you want to connect your new FC layer to
new_top_layer = tf.keras.layers.Dense(1,activation='sigmoid')(penultimate_layer.output) # create new FC layer and connect it to the rest of the model
new_model = tf.keras.models.Model(model.input,new_top_layer)  # define your new model
print(new_model.summary())

希望这会有所帮助。

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