卷积神经网络-2-LeNet 模型应用

Posted 2021-04-26 德鹏研究

LeNet是1998年被提出应用，是卷积神经网络的开篇之作，当时还没有BN操作，也没有dropout操作，而且主流的激活函数是sigmoid函数；

基于之前demo，代码如下：

import tensorflow as tfimport osimport numpy as npfrom matplotlib import pyplot as pltfrom tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Densefrom 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') self.p1 = MaxPool2D(pool_size=(2, 2),strides=2) self.c2 = Conv2D(filters=16,kernel_size=(5,5), activation='sigmoid') 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/LeNet5.ckpt"if os.path.exists(checkpoint_save_path +'.index'): print('·····加载模型·····') model.load_weights(checkpoint_save_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint( filepath= checkpoint_save_path, save_best_only=True, save_weights_only=True)history = model.fit(x_train,y_train,batch_size=32,epochs=5, validation_data=(x_test,y_test),validation_freq=1, callbacks=[cp_callback])model.summary()
# 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()
############################################### 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()

运行的打印效果：