[Deep Learning 21 Days Learning Challenge] Memo: What does our neural network model look like? - detailed explanation of model.summary()

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活动地址：CSDN21天学习挑战赛

学完手写识别和服装分类,Want to stop for a while to digest what I've learned,也总结一下,今天就从keras的model.summary()Let's start the output！

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1、model.summary()是什么

构建深度学习模型,我们会通过model.summary()输出模型各层的参数状况,Have we had just learned model as an example：


这里可以看出,model.summary()打印出的内容,is the same as the hierarchical relationship in which we build the model,Clothing classification model as an example：

#Build model code
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)), #卷积层1,卷积核3*3
    layers.MaxPooling2D((2, 2)),                   #池化层1,2*2采样
    layers.Conv2D(64, (3, 3), activation='relu'),  #卷积层2,卷积核3*3
    layers.MaxPooling2D((2, 2)),                   #池化层2,2*2采样
    layers.Conv2D(64, (3, 3), activation='relu'),  #卷积层3,卷积核3*3
    layers.Flatten(),                      #Flatten层,连接卷积层与全连接层
    layers.Dense(64, activation='relu'),   #全连接层,特征进一步提取
    layers.Dense(10)                       #输出层,输出预期结果
])

2、model.summary()输出含义

Still take the clothing classification model as an example：

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param # 
=================================================================
conv2d_6 (Conv2D)            (None, 26, 26, 32)        320   
#创建： layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)), #卷积层1,卷积核3*3
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 13, 13, 32)        0       
#创建：layers.MaxPooling2D((2, 2)), #池化层1,2*2采样 
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 11, 11, 64)        18496    
#创建：layers.Conv2D(64, (3, 3), activation='relu'), #卷积层2,卷积核3*3 
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 5, 5, 64)          0     
#创建：layers.MaxPooling2D((2, 2)), #池化层2,2*2采样 
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 3, 3, 64)          36928     
#创建：layers.Conv2D(64, (3, 3), activation='relu'), #卷积层3,卷积核3*3
_________________________________________________________________
flatten_2 (Flatten)          (None, 576)               0   
#创建：layers.Flatten(), #Flatten层,连接卷积层与全连接层 
_________________________________________________________________
dense_4 (Dense)              (None, 64)                36928  
#创建：layers.Dense(64, activation='relu'), #全连接层,特征进一步提取 
_________________________________________________________________
dense_5 (Dense)              (None, 10)                650       
#创建：layers.Dense(10) #输出层,输出预期结果
=================================================================
Total params: 93,322
Trainable params: 93,322
Non-trainable params: 0
_________________________________________________________________

• Param：该层输入参数个数, So how did this number come about?？
  a、The formula for calculating the number of parameters of the convolution layer：（卷积核长度*卷积核宽度*通道数+1）*卷积核个数
  例：
  第一个卷积层：(3*3*1+1)*32 = 320
  第二个卷积层：(3*3*32+1)*64 = 18496
  第三个卷积层：(3*3*64+1)*64 = 36928

  b、The formula for calculating the number of parameters of the fully connected layer： （输入数据维度+1）* 神经元个数
  例：
  Fully connected layer before the output layer：（64+1）*10=650
  这里之所以要加1,Because every neuron has a偏置（Bias）.

• Output Shape ：The output data shape of this layer

• Total params: Total number of model parameters,The parameters of each layer are accumulated

• Trainable params: 模型可训练参数

• Non-trainable params:Model untrainable parameters

3、Understand the shape model process

通过model.summary(),Let's look at this picture again,就清楚多了