全连接神经网络创建模型代码

#初始化模型
model = tf.keras.Sequential()
model.add(Flatten(input_shape=(32,32,3)))   #将二维扁平化为一维
model.add(Dropout(0.1))
model.add(Dense(2048,activation='relu')) 
model.add(Dropout(0.1))
model.add(Dense(1024,activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(512,activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(256,activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(10,activation='softmax'))

全连接神经网络的模型结构计算详解

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten (Flatten)            (None, 3072)              0         
_________________________________________________________________
dropout (Dropout)            (None, 3072)              0         
_________________________________________________________________
dense (Dense)                (None, 2048)              6293504   
_________________________________________________________________
dropout_1 (Dropout)          (None, 2048)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 1024)              2098176   
_________________________________________________________________
dropout_2 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 512)               524800    
_________________________________________________________________
dropout_3 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 256)               131328    
_________________________________________________________________
dropout_4 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_4 (Dense)              (None, 10)                2570      
=================================================================
Total params: 9,050,378
Trainable params: 9,050,378
Non-trainable params: 0

全连接层神经网络的Param，说明的是每层神经元权重的个数，所以它的计算如下：

Param = （输入数据维度+1）* 神经元个数
之所以要加1，是考虑到每个神经元都有一个Bias。

输入的图片是(32,32,3)需要进行Flatten操作即 Output Shape = 32*32*3=3072
第一个Dense层，因为Flatten后有3072个神经元。所以，Param=(3072+1)*2048= 6293504
第二个Dense层，有1024个神经元。所以，Param=(2048+1)*1024=2098176.
第三个Dense层，有512个神经元。所以，Param=(1024+1)*512=524800.
第四个Dense层，有256个神经元。所以，Param=(512+1)*256=131328.
第五个Dense层，有10个神经元。所以，Param=(256+1)*10=2570.

卷积神经网络创建模型代码

#初始化模型
model = Sequential()

#1st
model.add(Convolution2D(
	input_shape=(32,32,3),  #输入图片的维度
    filters=32,             #32个卷积核 (28,28,1) ---> (28,28,32)
    kernel_size=(5,5),      #卷积核设置(5,5)
    padding='same',         #步长
))
model.add(Activation('relu'))
model.add(MaxPooling2D(
    pool_size=(2,2),    #设置2*2的核	(28,28,32) ---> (14,14,32)
    strides=(2,2),      #跳过2*2执行
    padding='same',     #步长
))
#2nd
model.add(Convolution2D(
    filters=64,             #64个卷积核 (28,28,32) ---> (28,28,64)
    kernel_size=(5,5),      #卷积核设置(5,5)
    padding='same',         #步长
    # input_shape=(32,32,3)   #输入图片的维度
))
model.add(Activation('relu'))
model.add(MaxPooling2D(
    pool_size=(2,2),    #设置2*2的核 (14,14,32) ---> (7,7,64)
    strides=(2,2),      #跳过2*2执行
    padding='same',     #步长
))
#1st Dense
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
#2nd Dense
model.add(Dense(10)) 
model.add(Activation('softmax'))

卷积神经网络的模型结构计算详解

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 32, 32, 32)        2432      
_________________________________________________________________
activation (Activation)      (None, 32, 32, 32)        0         
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 16, 16, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 16, 16, 64)        51264     
_________________________________________________________________
activation_1 (Activation)    (None, 16, 16, 64)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 8, 8, 64)          0         
_________________________________________________________________
flatten (Flatten)            (None, 4096)              0         
_________________________________________________________________
dense (Dense)                (None, 64)                262208    
_________________________________________________________________
activation_2 (Activation)    (None, 64)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                650       
_________________________________________________________________
activation_3 (Activation)    (None, 10)                0         
=================================================================
Total params: 316,554
Trainable params: 316,554
Non-trainable params: 0

Param的计算方法如下：

（卷积核长度卷积核宽度通道数+1）* 卷积核个数

第一个CONV层，Convolution2D(32, kernel_size=(5, 5),
input_shape=(32,32,3))，所以计算得Param=(5*5*3+1)*32 = 2432.
第二个CONV层，Convolution2D(64, kernel_size=(5, 5),activation=‘relu’)，经过第一个层32个卷积核的作用，第二层输入数据通道数为32，Param=(5*5*32+1)*64 = 51264.
Flatten扁平：由池化后的(8,8,64)打成一维，即(8*8*64) = 4096
第一个Dense层，由于经过Flatten操作后，输出变成了4096，而第一个Dense中有64个卷积核，所以计算得Param =
64*(4096+1)= 262208
第二个Dense层，有10个卷积核，所以计算得Param =
10*(64+1)= 650