人工智能实践课作业
感谢组内调参大神肖同学
感谢课上讲作业的同学
cifar-10数据集可达到90%以上(使用了数据增强）
为了高成绩，不停地实验，
直接使用vgg19,70个epoch可以达到89%的准确率。
发现vgg19(似乎vgg16也可以），只要200个epoch以上，用Adam优化器方法，后面用sgd优化器方法，也可以达到90~91的准确率。
如果只用Adam方法，准确率会在89~90之间
以下是vgg19，可以跑到90~91 150Adam 100SGD
实际上按照上述方法，vgg16/19都可以达到同样效果
可能的改进方法
1.使用数据增强还有一点比较有趣的是，它训练的数据是数据增强后的数据（其实好像没有原来的数据），数据增强的数据就会差一点。可以先跑数据增强的数据，再跑原本的数据（或者先跑原来的数据再跑数据增强数据），效果也会提高（这个我们有在其它数据集上试验过）
如果可以shuffle一下应该会更好（但是这一点没有做探索。）
2.使用weight_decay
#!/usr/bin/env python
# coding:utf-8
# 利用class结构训练并测试fashion_mnist
#统一、规范命名
#如果电脑跑不动 改batch_size 可以只存最优
import tensorflow as tf
import os
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Dense
from tensorflow.keras import Model
from matplotlib import pyplot as plt
import numpy as np
from tensorflow.keras.preprocessing.image import ImageDataGenerator
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
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
x_train = x_train.reshape(x_train.shape[0], 32, 32, 3)
x_test = x_test.reshape(x_test.shape[0], 32, 32, 3)
x_train=tf.convert_to_tensor(x_train)
x_test=tf.convert_to_tensor(x_test)
y_train=tf.squeeze(y_train, axis=1)
y_test=tf.squeeze(y_test, axis=1)
image_gen_train = ImageDataGenerator(#数据增强
rescale=1,#归至0～1
rotation_range=0,#随机0度旋转
width_shift_range=0,#宽度偏移
height_shift_range=0,#高度偏移
horizontal_flip=True,#水平翻转
zoom_range=1#将图像随机缩放到100％
)
image_gen_train.fit(x_train)
#网络搭建 很重要！！
class vgg(Model):# 类 建网络
def __init__(self):#
super(LeNet5, self).__init__()
self.c1=Conv2D(filters=64, kernel_size=(3, 3), strides=1, padding='same', input_shape=(32, 32, 3))
self.b1=BatchNormalization()
self.a1=Activation('relu')
self.c2=Conv2D(filters=64, kernel_size=(3, 3), strides=1, padding='same', input_shape=(32, 32, 3))
self.b2 =BatchNormalization()
self.a2 =Activation('relu')
self.p1=MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
self.d1=Dropout(0.2)
self.c3 =Conv2D(128, kernel_size=(3, 3), strides=1, padding='same')
self.b3 =BatchNormalization()
self.a3 =Activation('relu')
self.c4 =Conv2D(128, kernel_size=(3, 3), strides=1, padding='same')
self.b4 =BatchNormalization()
self.a4 =Activation('relu')
self.p2 =MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
self.d2 =Dropout(0.2)
self.c5 =Conv2D(256, kernel_size=(3, 3), strides=1, padding='same')
self.b5 =BatchNormalization()
self.a5 =Activation('relu')
self.c6 =Conv2D(256, kernel_size=(3, 3), strides=1, padding='same')
self.b6 =BatchNormalization()
self.a6 =Activation('relu')
self.c7 =Conv2D(256, kernel_size=(3, 3), padding='same')
self.b7 =BatchNormalization()
self.a7 =Activation('relu')
self.ck =Conv2D(256, kernel_size=(3, 3), padding='same')
self.bk =BatchNormalization()
self.ak =Activation('relu')
self.p3 =MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
self.d3 =Dropout(0.2)
self.c8 =Conv2D(512, kernel_size=(3, 3), strides=1, padding='same')
self.b8 =BatchNormalization()
self.a8 =Activation('relu')
self.c9 =Conv2D(512, kernel_size=(3, 3), strides=1, padding='same')
self.b9 =BatchNormalization()
self.a9 =Activation('relu')
self.c10 =Conv2D(512, kernel_size=(3, 3), padding='same')
self.b10 =BatchNormalization()
self.a10 =Activation('relu')
self.cl =Conv2D(512, kernel_size=(3, 3), padding='same')
self.bl =BatchNormalization()
self.al =Activation('relu')
self.p4 =MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
self.d4 =Dropout(0.2)
self.c11 =Conv2D(512, kernel_size=(3, 3), strides=1, padding='same')
self.b11 =BatchNormalization()
self.a11 =Activation('relu')
self.c12 =Conv2D(512, kernel_size=(3, 3), strides=1, padding='same')
self.b12 =BatchNormalization()
self.a12 =Activation('relu')
self.c13 =Conv2D(512, kernel_size=(3, 3), strides=1, padding='same')
self.b13 =BatchNormalization()
self.a13 =Activation('relu')
self.cm =Conv2D(512, kernel_size=(3, 3), padding='same')
self.bm =BatchNormalization()
self.am =Activation('relu')
self.p5 =MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
self.d5 =Dropout(0.2)
self.flatten =Flatten()
self.f1 =Dense(512, activation='relu')
self.d6 =Dropout(0.2)
self.f2 =Dense(512, activation='relu')
self.d7 =Dropout(0.2)
self.f3 =Dense(10, activation='softmax')
def call(self, x):#网络结构的表达
x = self.c1(x)
x = self.b1(x)
x = self.a1(x)
x = self.c2(x)
x = self.b2(x)
x = self.a2(x)
x = self.p1(x)
x = self.d1(x)
x = self.c3(x)
x = self.b3(x)
x = self.a3(x)
x = self.c4(x)
x = self.b4(x)
x = self.a4(x)
x = self.p2(x)
x = self.d2(x)
x = self.c5(x)
x = self.b5(x)
x = self.a5(x)
x = self.c6(x)
x = self.b6(x)
x = self.a6(x)
x = self.c7(x)
x = self.b7(x)
x = self.a7(x)
x = self.ck(x)
x = self.bk(x)
x = self.ak(x)
x = self.p3(x)
x = self.d3(x)
x = self.c8(x)
x = self.b8(x)
x = self.a8(x)
x = self.c9(x)
x = self.b9(x)
x = self.a9(x)
x = self.c10(x)
x = self.b10(x)
x = self.a10(x)
x = self.cl(x)
x = self.bl(x)
x = self.al(x)
x = self.p4(x)
x = self.d4(x)
x = self.c11(x)
x = self.b11(x)
x = self.a11(x)
x = self.c12(x)
x = self.b12(x)
x = self.a12(x)
x = self.c13(x)
x = self.b13(x)
x = self.a13(x)
x = self.cm(x)
x = self.bm(x)
x = self.am(x)
x = self.p5(x)
x = self.d5(x)
x = self.flatten(x)
x = self.f1(x)
x = self.d6(x)
x = self.f2(x)
x = self.d7(x)
y = self.f3(x)
return y
model = vgg()
#sgd = optimizers.SGD(lr=.1, momentum=0.9, nesterov=True)
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['sparse_categorical_accuracy'])#配置好参数，程序在fit时才会使用运行
checkpoint_save_path = "./vgg192/mnist.ckpt"#断点存续
print(os.path)
if os.path.exists(checkpoint_save_path + '.index'):
print('-------------load the model-----------------')
model.load_weights(checkpoint_save_path)
'''
verbose：日志显示
verbose = 0 为不在标准输出流输出日志信息
verbose = 1 为输出进度条记录
verbose = 2 为每个epoch输出一行记录
注意： 默认为 1
'''
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
save_weights_only=True,
# monitor='loss',
save_best_only=True,
verbose=2)
#训练过程
# model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
# loss='sparse_categorical_crossentropy',
# metrics=['sparse_categorical_accuracy'])
# history = model.fit(image_gen_train.flow(x_train, y_train, batch_size=64), epochs=150,
# validation_data=(x_test, y_test),
# validation_freq=1, callbacks=[cp_callback], verbose=1,shuffle=True)
#
# model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=1e-3),
# loss='sparse_categorical_crossentropy',
# metrics=['sparse_categorical_accuracy'])
# history = model.fit(image_gen_train.flow(x_train, y_train, batch_size=64), epochs=100,
# validation_data=(x_test, y_test),
# validation_freq=1, callbacks=[cp_callback], verbose=1,shuffle=True)
#测试是否达到这个准确率的实验过程
model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=1e-3),
loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'])
history = model.fit(image_gen_train.flow(x_train, y_train, batch_size=64), epochs=12,
validation_data=(x_test, y_test),
validation_freq=1, callbacks=[cp_callback], verbose=1,shuffle=True)
# model.evaluate(x=x_test,y=y_test)
model.summary()
file = open('./weights.txt', 'w') # 参数提取
for v in model.trainable_variables:#存网络结构
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.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
实验结果
sjwl.png
