我是keras的新手，我正在尝试创建一个CNN模型。我创建了一个顺序模型如下 -
model = models.Sequential()
model.add(layers.Conv2D(32, (5, 5), activation='relu', input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
print(model.summary())
我得到如下摘要 -
Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 146, 146, 32) 2432 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 73, 73, 32) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 71, 71, 64) 18496 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 35, 35, 64) 0 _________________________________________________________________ conv2d_3 (Conv2D) (None, 33, 33, 64) 36928 _________________________________________________________________ max_pooling2d_3 (MaxPooling2 (None, 16, 16, 64) 0 ================================================================= Total params: 57,856 Trainable params: 57,856 Non-trainable params: 0 _________________________________________________________________
在此之后，我使用del model删除模型并再次使用上面的代码创建它，我得到如下摘要 -
Layer (type) Output Shape Param # ================================================================= conv2d_4 (Conv2D) (None, 146, 146, 32) 2432 _________________________________________________________________ max_pooling2d_4 (MaxPooling2 (None, 73, 73, 32) 0 _________________________________________________________________ conv2d_5 (Conv2D) (None, 71, 71, 64) 18496 _________________________________________________________________ max_pooling2d_5 (MaxPooling2 (None, 35, 35, 64) 0 _________________________________________________________________ conv2d_6 (Conv2D) (None, 33, 33, 64) 36928 _________________________________________________________________ max_pooling2d_6 (MaxPooling2 (None, 16, 16, 64) 0 ================================================================= Total params: 57,856 Trainable params: 57,856 Non-trainable params: 0 _________________________________________________________________
那么，为什么此摘要显示来自 conv2d_4 的图层编号，它应该来自 conv2d_1 ？
即使我创建了另一个模型 -
model_2 = models.Sequential()
model_2.add(layers.Conv2D(32, (5, 5), activation='relu', input_shape=(150, 150, 3)))
model_2.add(layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model_2.add(layers.Conv2D(64, (3, 3), activation='relu'))
model_2.add(layers.MaxPooling2D((2, 2)))
model_2.add(layers.Conv2D(64, (3, 3), activation='relu'))
model_2.add(layers.MaxPooling2D((2, 2)))
print(model_2.summary())
我在前一个模型的最后一个图层编号后开始编号 -
Layer (type) Output Shape Param # ================================================================= conv2d_7 (Conv2D) (None, 146, 146, 32) 2432 _________________________________________________________________ max_pooling2d_7 (MaxPooling2 (None, 73, 73, 32) 0 _________________________________________________________________ conv2d_8 (Conv2D) (None, 71, 71, 64) 18496 _________________________________________________________________ max_pooling2d_8 (MaxPooling2 (None, 35, 35, 64) 0 _________________________________________________________________ conv2d_9 (Conv2D) (None, 33, 33, 64) 36928 _________________________________________________________________ max_pooling2d_9 (MaxPooling2 (None, 16, 16, 64) 0 ================================================================= Total params: 57,856 Trainable params: 57,856 Non-trainable params: 0 _________________________________________________________________
答案 0 :(得分：0)
这取决于您使用的后端：
你可以看到（https://github.com/keras-team/keras/blob/master/keras/engine/base_layer.py#L132）：
name = _to_snake_case(prefix) + '_' + str(K.get_uid(prefix))
并且Keras没有重置del model上的uid。当使用张量流后端时，Keras会在clear_session()上重置uid。