TensorFlow2 构建模型-境界3 | Python技术交流与分享

文章目录

1.导入CIFAR-10数据集
1.1 导入需要的库
1.2 定义批量导入数据的函数
1.3 定义加载数据函数
1.4 加载数据
1.5 可视化加载数据
2 .数据预处理并设置超参数
3.使用tf,data构建数据管道
4.定义卷积层及池化层
5.构建模型
6.定义训练模型函数
7.可视化运行结果
8.测试模型

有三种计算图的构建方式：静态计算图，动态计算图，以及Autograph。 TensorFlow 2.0主要使用的是动态计算图和Autograph。动态计算图易于调试，编码效率较高，但执行效率偏低。静态计算图执行效率很高，但较难调试。而Autograph机制可以将动态图转换成静态计算图，兼收执行效率和编码效率之利。当然Autograph机制能够转换的代码并不是没有任何约束的，有一些编码规范需要遵循，否则可能会转换失败或者不符合预期。我们将着重介绍Autograph的编码规范和Autograph转换成静态图的原理。
Autograph编码规范:
• 1.被@tf.function修饰的函数应尽可能使用TensorFlow中的函数而不是Python中的其他函数。例如使用tf.print而不是print，使用tf.range而不是range，使用tf.constant(True)而不是True.
• 2.避免在@tf.function修饰的函数内部定义tf.Variable.
• 3.被@tf.function修饰的函数不可修改该函数外部的Python列表或字典等数据结构变量。

1.导入CIFAR-10数据集

CIFAR-10是由 Hinton 的学生 Alex Krizhevsky 和 Ilya Sutskever 整理的一个用于识别普适物体的小型数据集。一共包含 10 个类别的 RGB 彩色图片：飞机（ a叩lane ）、汽车（ automobile ）、鸟类（ bird ）、猫（ cat ）、鹿（ deer ）、狗（ dog ）、蛙类（ frog ）、马（ horse ）、船（ ship ）和卡车（ truck ）。图片的尺寸为 32×32，3个通道，数据集中一共有 50000 张训练圄片和 10000 张测试图片。 CIFAR-10数据集有3个版本，这里使用python版本。

1.1 导入需要的库

import os
import math
import numpy as np
import pickle as p
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline

import os

import math

import numpy as np

import pickle as p

import tensorflow as tf

import matplotlib.pyplot as plt

%matplotlib inline

1.2 定义批量导入数据的函数

def load_CIFAR_batch(filename):
    """ load single batch of cifar """  
    with open(filename, 'rb')as f:
        # 一个样本由标签和图像数据组成
        #  (3072=32x32x3)
        # ...
        # 
        data_dict = p.load(f, encoding='bytes')
        images= data_dict[b'data']
        labels = data_dict[b'labels']
                
        # 把原始数据结构调整为: BCWH
        images = images.reshape(10000, 3, 32, 32)
        # tensorflow处理图像数据的结构：BWHC
        # 把通道数据C移动到最后一个维度
        images = images.transpose (0,2,3,1)
     
        labels = np.array(labels)
        
        return images, labels

def load_CIFAR_batch(filename):

""" load single batch of cifar """

with open(filename, 'rb')as f:

# 一个样本由标签和图像数据组成

# (3072=32x32x3)

# ...

data_dict = p.load(f, encoding='bytes')

images= data_dict[b'data']

labels = data_dict[b'labels']

# 把原始数据结构调整为: BCWH

images = images.reshape(10000, 3, 32, 32)

# tensorflow处理图像数据的结构：BWHC

# 把通道数据C移动到最后一个维度

images = images.transpose (0,2,3,1)

labels = np.array(labels)

return images, labels

1.3 定义加载数据函数

def load_CIFAR_data(data_dir):
    """load CIFAR data"""
 
    images_train=[]
    labels_train=[]
    for i in range(5):
        f=os.path.join(data_dir,'data_batch_%d' % (i+1))
        print('loading ',f)
        # 调用 load_CIFAR_batch( )获得批量的图像及其对应的标签
        image_batch,label_batch=load_CIFAR_batch(f)
        images_train.append(image_batch)
        labels_train.append(label_batch)
        Xtrain=np.concatenate(images_train)
        Ytrain=np.concatenate(labels_train)
        del image_batch ,label_batch
    
    Xtest,Ytest=load_CIFAR_batch(os.path.join(data_dir,'test_batch'))
    print('finished loadding CIFAR-10 data')
    
    # 返回训练集的图像和标签，测试集的图像和标签
    return (Xtrain,Ytrain),(Xtest,Ytest)

def load_CIFAR_data(data_dir):

"""load CIFAR data"""

images_train=[]

labels_train=[]

for i in range(5):

f=os.path.join(data_dir,'data_batch_%d' % (i+1))

print('loading ',f)

# 调用 load_CIFAR_batch( )获得批量的图像及其对应的标签

image_batch,label_batch=load_CIFAR_batch(f)

images_train.append(image_batch)

labels_train.append(label_batch)

Xtrain=np.concatenate(images_train)

Ytrain=np.concatenate(labels_train)

del image_batch ,label_batch

Xtest,Ytest=load_CIFAR_batch(os.path.join(data_dir,'test_batch'))

print('finished loadding CIFAR-10 data')

# 返回训练集的图像和标签，测试集的图像和标签

return (Xtrain,Ytrain),(Xtest,Ytest)

1.4 加载数据

data_dir = r'data\cifar-10-batches-py'
(x_train,y_train),(x_test,y_test) = load_CIFAR_data(data_dir)

1 2	data_dir = r'data\cifar-10-batches-py' (x_train,y_train),(x_test,y_test) = load_CIFAR_data(data_dir)

运行结果
loading data\cifar-10-batches-py\data_batch_1
loading data\cifar-10-batches-py\data_batch_2
loading data\cifar-10-batches-py\data_batch_3
loading data\cifar-10-batches-py\data_batch_4
loading data\cifar-10-batches-py\data_batch_5
finished loadding CIFAR-10 data

1.5 可视化加载数据

label_dict = {0:"airplane", 1:"
automobile", 2:"bird", 3:"cat", 4:"deer",
              5:"dog", 6:"frog", 7:"horse", 8:"ship", 9:"truck"}


def plot_images_labels(images, labels, num):
    total = len(images)
    fig = plt.gcf()
    fig.set_size_inches(15, math.ceil(num / 10) * 7)
    for i in range(0, num):
        choose_n = np.random.randint(0, total)
        
        ax = plt.subplot(math.ceil(num / 5), 5, 1 + i)
        ax.imshow(images[choose_n], cmap='binary')
        title = label_dict[labels[choose_n]]
        ax.set_title(title, fontsize=10)        
    plt.show()
	
plot_images_labels(x_train, y_train, 10)

label_dict = {0:"airplane", 1:"

automobile", 2:"bird", 3:"cat", 4:"deer",

5:"dog", 6:"frog", 7:"horse", 8:"ship", 9:"truck"}

def plot_images_labels(images, labels, num):

total = len(images)

fig = plt.gcf()

fig.set_size_inches(15, math.ceil(num / 10) * 7)

for i in range(0, num):

choose_n = np.random.randint(0, total)

ax = plt.subplot(math.ceil(num / 5), 5, 1 + i)

ax.imshow(images[choose_n], cmap='binary')

title = label_dict[labels[choose_n]]

ax.set_title(title, fontsize=10)

plt.show()

plot_images_labels(x_train, y_train, 10)

运行结果

2 .数据预处理并设置超参数

x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

train_num = len(x_train)
num_classes = 10

learning_rate = 0.0002
batch_size = 64
training_steps = 20000
display_step = 1000

conv1_filters = 32
conv2_filters = 64
fc1_units = 256

x_train = x_train.astype('float32') / 255.0

x_test = x_test.astype('float32') / 255.0

train_num = len(x_train)

num_classes = 10

learning_rate = 0.0002

batch_size = 64

training_steps = 20000

display_step = 1000

conv1_filters = 32

conv2_filters = 64

fc1_units = 256

3.使用tf,data构建数据管道

AUTOTUNE = tf.data.experimental.AUTOTUNE
train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_data = train_data.shuffle(5000).repeat(training_steps).batch(batch_size).prefetch(buffer_size=AUTOTUNE)

AUTOTUNE = tf.data.experimental.AUTOTUNE

train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))

train_data = train_data.shuffle(5000).repeat(training_steps).batch(batch_size).prefetch(buffer_size=AUTOTUNE)

4.定义卷积层及池化层

@tf.function
def conv2d(x, W, b, strides=1):
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
    x = tf.nn.bias_add(x, b)
    return tf.nn.relu(x)

@tf.function
def maxpool2d(x, k=2):
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')

@tf.function

def conv2d(x, W, b, strides=1):

x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')

x = tf.nn.bias_add(x, b)

return tf.nn.relu(x)

@tf.function

def maxpool2d(x, k=2):

return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')

5.构建模型

class CNNModel(tf.Module):
    def __init__(self,name = None):
        super(CNNModel, self).__init__(name=name)
        self.w1 = tf.Variable(random_normal([3, 3, 3, conv1_filters]))#[k_width, k_height, input_chn, output_chn]
        self.b1 = tf.Variable(tf.zeros([conv1_filters]))
        #输入通道：32，输出通道：64，卷积后图像尺寸不变，依然是16x16
        self.w2 = tf.Variable(random_normal([3, 3, conv1_filters, conv2_filters]))
        self.b2 = tf.Variable(tf.zeros([conv2_filters]))
        #将池第2个池化层的64个8x8的图像转换为一维的向量，长度是 64*8*8=4096
        self.w3 = tf.Variable(random_normal([4096, fc1_units]))
        self.b3 = tf.Variable(tf.zeros([fc1_units]))
        self.wout = tf.Variable(random_normal([fc1_units, num_classes]))
        self.bout = tf.Variable(tf.zeros([num_classes]))

     
    # 正向传播
    @tf.function
    def __call__(self,x):
        conv1 = conv2d(x, self.w1, self.b1)
        pool1 = maxpool2d(conv1, k=2)  #将32x32图像缩小为16x16，池化不改变通道数量，因此依然是32个
        conv2 = conv2d(pool1, self.w2, self.b2)
        pool2 = maxpool2d(conv2, k=2)
        flat = tf.reshape(pool2, [-1, self.w3.get_shape().as_list()[0]])
        fc1 = tf.add(tf.matmul(flat, self.w3), self.b3)
        fc1 = tf.nn.relu(fc1)
        out = tf.add(tf.matmul(fc1, self.wout), self.bout)
    
        return tf.nn.softmax(out) 
    
    # 损失函数(二元交叉熵)
    @tf.function
    def cross_entropy(self,y_pred, y_true):
        y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)
        loss_ = tf.keras.losses.sparse_categorical_crossentropy(y_true=y_true, y_pred=y_pred)
    
        return tf.reduce_mean(loss_)
    
    # 评估指标(准确率)
    @tf.function
    def accuracy(self,y_pred, y_true):
        correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.reshape(tf.cast(y_true, tf.int64), [-1]))
        return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
   
    
model = CNNModel()
#通过添加属性的方法进行封装
model.optimizer = tf.optimizers.Adam(learning_rate)

class CNNModel(tf.Module):

def __init__(self,name = None):

super(CNNModel, self).__init__(name=name)

self.w1 = tf.Variable(random_normal([3, 3, 3, conv1_filters]))#[k_width, k_height, input_chn, output_chn]

self.b1 = tf.Variable(tf.zeros([conv1_filters]))

#输入通道：32，输出通道：64，卷积后图像尺寸不变，依然是16x16

self.w2 = tf.Variable(random_normal([3, 3, conv1_filters, conv2_filters]))

self.b2 = tf.Variable(tf.zeros([conv2_filters]))

#将池第2个池化层的64个8x8的图像转换为一维的向量，长度是 64*8*8=4096

self.w3 = tf.Variable(random_normal([4096, fc1_units]))

self.b3 = tf.Variable(tf.zeros([fc1_units]))

self.wout = tf.Variable(random_normal([fc1_units, num_classes]))

self.bout = tf.Variable(tf.zeros([num_classes]))

# 正向传播

@tf.function

def __call__(self,x):

conv1 = conv2d(x, self.w1, self.b1)

pool1 = maxpool2d(conv1, k=2) #将32x32图像缩小为16x16，池化不改变通道数量，因此依然是32个

conv2 = conv2d(pool1, self.w2, self.b2)

pool2 = maxpool2d(conv2, k=2)

flat = tf.reshape(pool2, [-1, self.w3.get_shape().as_list()[0]])

fc1 = tf.add(tf.matmul(flat, self.w3), self.b3)

fc1 = tf.nn.relu(fc1)

out = tf.add(tf.matmul(fc1, self.wout), self.bout)

return tf.nn.softmax(out)

# 损失函数(二元交叉熵)

@tf.function

def cross_entropy(self,y_pred, y_true):

y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)

loss_ = tf.keras.losses.sparse_categorical_crossentropy(y_true=y_true, y_pred=y_pred)

return tf.reduce_mean(loss_)

# 评估指标(准确率)

@tf.function

def accuracy(self,y_pred, y_true):

correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.reshape(tf.cast(y_true, tf.int64), [-1]))

return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

model = CNNModel()

#通过添加属性的方法进行封装

model.optimizer = tf.optimizers.Adam(learning_rate)

6.定义训练模型函数

自定训练过程：
（1）打开一个遍历各epoch的for循环
（2）对于每个epoch，打开一个分批遍历数据集的 for 循环
（3）对于每个批次，打开一个 GradientTape() 作用域
（4）在此作用域内，调用模型（前向传递）并计算损失
（5）在作用域之外，检索模型权重相对于损失的梯度
（6）根据梯度使用优化器来更新模型的权重
（7）评估模型指标

@tf.function
def train_step(model, features, labels):
    
    # 正向传播求损失
    with tf.GradientTape() as tape:
        predictions = model(features)
        loss = model.cross_entropy(predictions,labels) 
        
    # 反向传播求梯度
    grads = tape.gradient(loss, model.trainable_variables)
    
    # 执行梯度下降   
    model.optimizer.apply_gradients(zip(grads, model.trainable_variables))        
    # 计算评估指标
    metric = model.accuracy(predictions,labels)
    
    return loss, metric

train_loss_list1 = []
train_acc_list1 = []
def train_model(model,train_data,training_steps,display_step):
    #for epoch in tf.range(1,epochs+1): 
    for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):
        starttime=time.time()
        loss,metric = train_step(model,batch_x,batch_y)
        
        if step % display_step == 0:
            #printbar()
            train_loss_list1.append(loss)
            train_acc_list1.append(metric)
            tf.print("step ={},loss = {:.4f},accuracy ={:.4f} ,times={:.4f}".format(step,loss,metric,(time.time() - starttime)))

@tf.function

def train_step(model, features, labels):

# 正向传播求损失

with tf.GradientTape() as tape:

predictions = model(features)

loss = model.cross_entropy(predictions,labels)

# 反向传播求梯度

grads = tape.gradient(loss, model.trainable_variables)

# 执行梯度下降

model.optimizer.apply_gradients(zip(grads, model.trainable_variables))

# 计算评估指标

metric = model.accuracy(predictions,labels)

return loss, metric

train_loss_list1 = []

train_acc_list1 = []

def train_model(model,train_data,training_steps,display_step):

#for epoch in tf.range(1,epochs+1):

for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):

starttime=time.time()

loss,metric = train_step(model,batch_x,batch_y)

if step % display_step == 0:

#printbar()

train_loss_list1.append(loss)

train_acc_list1.append(metric)

tf.print("step ={},loss = {:.4f},accuracy ={:.4f} ,times={:.4f}".format(step,loss,metric,(time.time() - starttime)))

train_model(model,train_data,training_steps,display_step)
运行结果
step =1000,loss = 1.3011,accuracy =0.5781 ,times=0.0000
step =2000,loss = 1.2720,accuracy =0.6094 ,times=0.0000
step =3000,loss = 1.2153,accuracy =0.5469 ,times=0.0000
step =4000,loss = 0.8636,accuracy =0.7500 ,times=0.0000
step =5000,loss = 0.7936,accuracy =0.7500 ,times=0.0000
step =6000,loss = 0.9527,accuracy =0.6875 ,times=0.0156
step =7000,loss = 0.9352,accuracy =0.7344 ,times=0.0000
step =8000,loss = 0.6892,accuracy =0.7969 ,times=0.0000
step =9000,loss = 0.7949,accuracy =0.7031 ,times=0.0000
step =10000,loss = 0.4768,accuracy =0.8438 ,times=0.0000
step =11000,loss = 0.7983,accuracy =0.7188 ,times=0.0000
step =12000,loss = 0.5601,accuracy =0.8281 ,times=0.0000
step =13000,loss = 0.7934,accuracy =0.7031 ,times=0.0000
step =14000,loss = 0.6450,accuracy =0.8438 ,times=0.0000
step =15000,loss = 0.5681,accuracy =0.7656 ,times=0.0000
step =16000,loss = 0.5413,accuracy =0.8125 ,times=0.0000
step =17000,loss = 0.3914,accuracy =0.8438 ,times=0.0000
step =18000,loss = 0.3687,accuracy =0.8906 ,times=0.0000
step =19000,loss = 0.4534,accuracy =0.8750 ,times=0.0000
step =20000,loss = 0.3855,accuracy =0.8438 ,times=0.0000

从运行结果来看，运行时间快了很多！

7.可视化运行结果

plt.title('the train and validate')
plt.xlabel('Times')
plt.ylabel('Loss value')
plt.plot(train_loss_list, color=(1, 0, 0), label='Loss train')
plt.plot(train_acc_list, color=(0, 0, 1), label='Accuracy train')
plt.legend(loc='best')
plt.show()

plt.title('the train and validate')

plt.xlabel('Times')

plt.ylabel('Loss value')

plt.plot(train_loss_list, color=(1, 0, 0), label='Loss train')

plt.plot(train_acc_list, color=(0, 0, 1), label='Accuracy train')

plt.legend(loc='best')

plt.show()

8.测试模型

test_total_batch = int(len(x_test) / batch_size)
test_acc_sum = 0.0
for i in range(test_total_batch):
    test_image_batch = x_test[i*batch_size:(i+1)*batch_size]
    test_label_batch = y_test[i*batch_size:(i+1)*batch_size]
    pred = conv_net(test_image_batch)
    test_batch_acc = accuracy(pred,test_label_batch)
    test_acc_sum += test_batch_acc
test_acc = float(test_acc_sum / test_total_batch)
print("Test accuracy:{:.6f}".format(test_acc))

test_total_batch = int(len(x_test) / batch_size)

test_acc_sum = 0.0

for i in range(test_total_batch):

test_image_batch = x_test[i*batch_size:(i+1)*batch_size]

test_label_batch = y_test[i*batch_size:(i+1)*batch_size]

pred = conv_net(test_image_batch)

test_batch_acc = accuracy(pred,test_label_batch)

test_acc_sum += test_batch_acc

test_acc = float(test_acc_sum / test_total_batch)

print("Test accuracy:{:.6f}".format(test_acc))

运行结果
Test accuracy:0.720653
性能也有一定提升！