我已经打印出了我的模型中的层数，代码工作正常，我只是想了解输出的实际含义，输出如下。
Number of layers: 30
Layer types:
input_1 - InputLayer
conv2d - Conv2D
batch_normalization - BatchNormalization
activation - Activation
conv2d_1 - Conv2D
batch_normalization_1 - BatchNormalization
activation_1 - Activation
conv2d_2 - Conv2D
batch_normalization_2 - BatchNormalization
add - Add
activation_2 - Activation
conv2d_3 - Conv2D
batch_normalization_3 - BatchNormalization
activation_3 - Activation
conv2d_4 - Conv2D
conv2d_5 - Conv2D
batch_normalization_4 - BatchNormalization
add_1 - Add
activation_4 - Activation
conv2d_6 - Conv2D
batch_normalization_5 - BatchNormalization
activation_5 - Activation
conv2d_7 - Conv2D
conv2d_8 - Conv2D
batch_normalization_6 - BatchNormalization
add_2 - Add
activation_6 - Activation
average_pooling2d - AveragePooling2D
flatten - Flatten
dense - Dense这是否意味着我的模型有6层，每层有5个隐藏层？权重矩阵是否仅针对作为整体的六个层构造？或者当我运行模型的时候，权重矩阵会被构造为30层吗？我应该如何解释上面的输出。
我使用了keras的model.summary()-这是我的输出：
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 32, 32, 3)] 0
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 32, 32, 16) 448 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 32, 32, 16) 64 conv2d[0][0]
__________________________________________________________________________________________________
activation (Activation) (None, 32, 32, 16) 0 batch_normalization[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 32, 32, 16) 2320 activation[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 32, 32, 16) 64 conv2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 32, 32, 16) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 32, 32, 16) 2320 activation_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 32, 32, 16) 64 conv2d_2[0][0]
__________________________________________________________________________________________________
add (Add) (None, 32, 32, 16) 0 activation[0][0]
batch_normalization_2[0][0]
__________________________________________________________________________________________________
activation_2 (Activation) (None, 32, 32, 16) 0 add[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 16, 16, 32) 4640 activation_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 16, 16, 32) 128 conv2d_3[0][0]
__________________________________________________________________________________________________
activation_3 (Activation) (None, 16, 16, 32) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 16, 16, 32) 9248 activation_3[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 16, 16, 32) 544 activation_2[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 16, 16, 32) 128 conv2d_4[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 16, 16, 32) 0 conv2d_5[0][0]
batch_normalization_4[0][0]
__________________________________________________________________________________________________
activation_4 (Activation) (None, 16, 16, 32) 0 add_1[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 8, 8, 64) 18496 activation_4[0][0]
__________________________________________________________________________________________________
1条答案
按热度按时间to94eoyn1#
在tensorflow.keras中，层的定义与您可能从文献中期望的不同。层是tensorflow. keras. layers. Layer的子类。
当构建模型时，你从一个没有参数（权重）的输入层开始。然后你有一个Conv 2d层，它有参数，然后是BatchNormalization层，它只是执行标准化-它没有任何参数，类似地激活层也没有任何参数。
在你构建的模型中，只有Dense和Conv 2D层有参数，其余的只是执行不可训练的数学运算。
你可以使用model.summary（）来打印层，它会显示哪些层对参数计数有贡献，并显示网络连接-这是一种非常有用的调试模型的方法。