CNN.py

import tensorflow as tf
import os
import numpy as np
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 Baseline(Model):
    def __init__(self):
        super(Baseline,self).__init__()
        self.c1 = Conv2D(filters=6, kernel_size=(5, 5), padding='same')#卷积层
        self.b1 = BatchNormalization()
        self.a1 = Activation('relu' )
        self.p1 = MaxPool2D(pool_size=(2,2), strides=2, padding='same')#池化层
        self.d1 = Dropout(0.2)
        self.flatten = Flatten()
        self.f1 = Dense(128,activation='relu')
        self.d2 = Dropout(0.2)
        self.f2 = Dense(10,activation='softmax')
    def call(self,x):
        x = self.c1(x)
        x = self.b1(x)
        x = self.a1(x)
        x = self.p1(x)
        x = self.d1(x)
        x = self.flatten(x)
        x = self.f1(x)
        x = self.d2(x)
        y = self.f2(x)
        return y

model = Baseline()
model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),metrics=['sparse_categorical_accuracy'])
checkpoint_save_path = "./checkpoint/Baseline.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=10,validation_data=(x_test,y_test),validation_freq=1,callbacks=[cp_callback])
model.summary()

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()



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()