深度学习之路=====4=====＞＞(GoogleNet)InceptionNet(tensorflow2)

Inception module

InceptionNet 结构

code

import tensorflow as tf
import os
import numpy as np
import datetime
from tensorflow.keras.layers import Conv2D,BatchNormalization,Activation,MaxPool2D,GlobalAveragePooling2D,Dropout,Flatten,Dense
from tensorflow.keras import layers,Model
np.set_printoptions(threshold=np.inf
                   )
cifar10=tf.keras.datasets.cifar10
(x_train,y_train),(x_test,y_test)=cifar10.load_data()
x_train,x_test=x_train/255.0,x_test/255.0

# Inception block 组件C-B-A
class ConvBNA(Model):
    def __init__(self,ch,kernelsize=3,strides=1,padding='same'):
        super().__init__()
        self.model=tf.keras.models.Sequential([
            layers.Conv2D(ch,kernelsize,strides=strides,padding=padding,activation='relu'),
            layers.BatchNormalization()
        ])
    def call(self,x):
        x=self.model(x)
        return x
##Inception block 由    Inception block 组件C-B-A 和MaxPool2D 组成
class InceptionBlock(Model):
    def __init__(self,ch,strides=1):
        super().__init__()
        self.ch=ch
        self.strides=strides
        self.c1=ConvBNA(ch,kernelsize=1,strides=strides)
        self.c2_1=ConvBNA(ch,kernelsize=1,strides=strides)
        self.c2_2=ConvBNA(ch,strides=strides)
        self.c3_1=ConvBNA(ch,kernelsize=1,strides=strides)
        self.c3_2=ConvBNA(ch,kernelsize=5,strides=strides)
        self.p4_1=MaxPool2D(pool_size=(3,3))
        self.c4_2=ConvBNA(ch,kernelsize=1,strides=strides)
    def call(self,x):
        x1=self.c1(x)
        x2_1=self.c2_1(x)
        x2_2=self.c2_2(x2_1)
        x3_1=self.c3_1(x)
        x3_2=self.c3_2(x3_1)
        x4_1=self.p4_1(x)
        x4_2=self.c4_2(x4_1)
        x=tf.concat([x1,x2_2,x3_2,x4_2],axis=3)
        return x
## Inception 由1个ConvBNA module 、4个InceptionBlock module、一个均值池化、一个全连接网络层构成
class InceptionNet(Model):
    def __init__(self,num_classes,init_ch=16,**kwargs):
        super().__init__()
        self.in_channels=init_ch
        self.input_layer=ConvBNA(init_ch)
        self.middle_layers=[
            InceptionBlock(init_ch,strides=2),
            InceptionBlock(init_ch,strides=1),
            InceptionBlock(init_ch*2,strides=2),
            InceptionBlock(init_ch*2,strides=1),
            GlobalAveragePooling2D()
        ]
        self.output_layer=Dense(num_classes,activation='softmax')
    def call(self,x):
        x=self.input_layer(x)
        for layer in self.middle_layers:
            x=layer(x)
        y=self.output_layer(x)
        return y
model=InceptionNet(num_classes=10)

##断点续训
checkpoint_save_path="./checkpoint/mnist.ckpt"
if os.path.exists(checkpoint_save_path+'.index'):
    print('--------------load the model----------')
    model.load_weights(checkpoint_save_path)
cp_callback=tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
                                             save_weights_only=True,
                                             save_best_only=True)
model.compile(optimizer="adam",loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),metrics=['sparse_categorical_accuracy'])
history=model.fit(x_train,y_train,batch_size=32,epochs=1,validation_data=(x_test,y_test),validation_freq=1,callbacks=[cp_callback])

##打印所有可训练参数
print(model.trainable_variables)
file=open('./weights.txt','w')
for v in model.trainable_variables:
    file.write(str(v.name)+'\n')
    file.write(str(v.shape)+'\n')
    file.write(str(v.numpy)+'\n')
file.close()