卷积神经网络-LenNet

import tensorflow as tf
import numpy as np
import os
from matplotlib import pyplot as plt
from tensorflow.keras.layers import Conv2D,BatchNormalization,Activation,MaxPool2D,Dropout,Flatten,Dense
from tensorflow.keras import 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

class LeNet5(Model):
    def __init__(self):
        super(LeNet5,self).__init__()
        
        self.c1=Conv2D(filters= 6,kernel_size=(5,5),activation="sigmoid",padding="valid")
        self.p1=MaxPool2D(pool_size=(2,2),strides=2)
        
        self.c2=Conv2D(filters=16,kernel_size=(5,5),activation="sigmoid",padding="same")
        self.p2=MaxPool2D(pool_size=(2,2),strides=2)
        
        self.flatten=Flatten()
        self.f1=Dense( 120,activation="sigmoid")
        self.f2=Dense(  84,activation="sigmoid")
        self.f3=Dense(  10,activation="softmax")

    def call(self,x):
        x=self.c1(x)
        x=self.p1(x)

        x=self.c2(x)
        x=self.p2(x)

        x=self.flatten(x)
        x=self.f1(x)
        x=self.f2(x)
        y=self.f3(x)
        return y

model=LeNet5()
model.compile(optimizer="adam",
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])
checkpoint_save_path = "./checkpoint.LeNet/LeNet.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)
history=model.fit(x_train,y_train,batch_size=32,epochs=1,validation_data=(x_test,y_test),validation_freq=1,
                  callbacks=[cp_callback])
model.summary()
print(model.trainable_variables)
file = open('./weights.LeNet.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()

###############################################    show   ###############################################

# 显示训练集和验证集的acc和loss曲线
acc     =history.history['sparse_categorical_accuracy']
val_acc =history.history['val_sparse_categorical_accuracy']
loss    =history.history['loss']
val_loss=history.history['val_loss']

plt.subplot(1, 2, 1)
plt.plot(acc,    label='Training Accuracy')
plt.plot(val_acc,label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(loss,    label='Training Loss')
plt.plot(val_loss,label='Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.show()