Keras基础

2023-11-04 12:27:34

Keras基础

keras基于tensorflow库，在python中使用，暂时为安装GPU，训练速度可能有些慢。

步骤：

1.导入相应库

2.收集数据

3.处理数据，划分测试集和训练集

4.构建model，模块化构建

5.设置loss，optimizer 损失函数，优化器

6.进行训练

7.进行测试

8.结果的可视化

1.tensorflow基本内容

构建图->运行图

使用tensorlfow2.x

1.忽略waring以下的问题

import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

2.运行tensorflow1.x的代码

import tensorflow as tf
tf.compat.v1.disable_eager_execution()
#或
#import tensorflow.compat.v1 as tf
#tf.disable_v2_behavior()
#使用时
tf.compat.v1...............

3.构件图

常量 tf.constant(2)

变量 a=tf.Varibale(initial_value=2) #初始化

init=tf.compat.v1.global_variables_initializer()

展位符 x=tf.compat.v1.placeholder(tf.float32)

4.运行图

with tf.compat.v1.Session() as sess:
    sess.run(init)
    print(sess.run(c,feed_dict={a:3,b:4}))

5.tensorflow API

tf.app 为tensorflow脚本提供main函数
tf.image 图像处理，颜色变化，图像编码解码
tf.gfile 文件操作函数
summary 生成tensorboard可用统计日志
tf.python_io 读写TFRecord文件
tf.train 提供一些训练器
tf.nn 构建神经网络底层函数，卷积池化等

6.线性回归实例

import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
tf.compat.v1.disable_eager_execution()

#data
x=tf.compat.v1.random_normal(shape=[100,1])
y=tf.matmul(x,[[0.8]])+0.7

w=tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1,1]))
b=tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1,1]))
y_pre=tf.matmul(x,w)+b

#loss
loss=tf.reduce_mean(tf.square(y_pre-y))
#optimizer
optimizer=tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.05).minimize((loss))
#收集变量
tf.compat.v1.summary.scalar("loss",loss)
tf.compat.v1.summary.histogram("weight",w)
tf.compat.v1.summary.histogram("bias",b)

merged=tf.compat.v1.summary.merge_all()
#变量初始化
init=tf.compat.v1.global_variables_initializer()

with tf.compat.v1.Session() as sess:
    sess.run(init)

    filt_writer=tf.compat.v1.summary.FileWriter("F:\PyCharm Community Edition 2020.3.2\DeepLearning")

    print("训练前的模型参数为：权重：%f，偏置：%f，损失为：%f" % (w.eval(), b.eval(),loss.eval()))

    for i in range(201):
        sess.run(optimizer)
        if(i%20==0):
            print("第%d次训练后模型参数，权重:%f,偏置:%f，损失:%f" %(i+1,w.eval(),b.eval(),loss.eval()))

        summary=sess.run(merged)

        filt_writer.add_summary(summary,i)

7.模型保存

tf.train.Saver(var_list=None,max_to_keep=5)

# 创建saver对象
    saver = tf.compat.v1.train.Saver(max_to_keep=5)
if os.path.exists("D:/AliyunEDU/04 model/lr_model.ckpt"):
            saver.restore(sess,"D:/AliyunEDU/04 model/checkpoint")

8.tensorboard可视化

保存的logs的path

在cmd下 tensorboard --logdir=“path”

9.图片数据的输入

数据cifar 使用队列读入二进制文件,把样本特征通过example写入文件

再通过文件名队列，解码读取TFRecord文件并解析

图片数据读入和解码
import os
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
def picture_read():
    filename=os.listdir("E:/Picture/animation")
    #拼接路径名和文件名,路径使用/
    file_list=[os.path.join("E:/Picture/animation/",file) for file in filename]
    #加入文件队列
    file_queue=tf.compat.v1.train.string_input_producer(file_list)
    #读取文件
    reader=tf.compat.v1.WholeFileReader()
    key,value=reader.read(file_queue)
    #照片解码
    img=tf.compat.v1.image.decode_jpeg(value)
    #重新设置大小，和shape
    img=tf.compat.v1.image.resize(img,[200,200])
    img.set_shape(shape=[200,200,3])
    #设置batch
    img_batch=tf.compat.v1.train.batch([img],batch_size=100,num_threads=1,capacity=100)

    with tf.compat.v1.Session() as sess:
        #创建线程
        coord=tf.compat.v1.train.Coordinator()
        thread=tf.compat.v1.train.start_queue_runners(sess=sess)

        key_n,value_n,img_resized_n,img_batch_n=sess.run([key,value,img,img_batch])

        print(img_resized_n)
        #回收线程
        coord.request_stop()
        coord.join(thread)
picture_read()

10.TFRecords文件

import tensorflow as tf
tf.compat.v1.disable_eager_execution()
import os


class Cifar(object):
    def __init__(self):
        # 初始化操作
        self.height = 32
        self.width = 32
        self.channels = 3
        
        # 字节数
        self.image_bytes = self.height * self.width * self.channels
        self.label_bytes = 1
        self.all_bytes = self.label_bytes + self.image_bytes
        
    def read_binary(self):
        # 1.构造文件名队列
        file_name = os.listdir("D:/Project/Data/cifar-10-bin")
        # 构建文件名路径列表
        file_list = [os.path.join("D:/Project/Data/cifar-10-bin/", file) for file in file_name if file[-3:] == "bin"]
        file_queue = tf.compat.v1.train.string_input_producer(file_list)
        
        # 2.读取与解码
        ## 读取阶段
        reader = tf.compat.v1.FixedLengthRecordReader(self.all_bytes)
        key,value = reader.read(file_queue)
        print("key:\n{}\n value:\n{}\n".format(key,value))
        
        ## 解码阶段
        decoded = tf.compat.v1.decode_raw(value, tf.uint8)
        
        ## 1_将目标值和特征值切片切开
        label = tf.slice(decoded, [0], [self.label_bytes])
        image = tf.slice(decoded, [1], [self.image_bytes])
        
        ## 2_调整图片形状，以符合tensor的输入要求
        image_reshaped = tf.reshape(image, shape=[self.channels,self.height,self.width])
        
        ## 3_转置 将图片调整为 HWC
        image_transposed = tf.transpose(image_reshaped,[1,2,0])
        print("image_reshaped:{}\n image_tansposed:{}\n".format(image_reshaped,image_transposed))
        
        ## 4_调整图像类型 uint8->float32
        image_cast = tf.cast(image_transposed, tf.float32)
        
        # 3.批处理
        label_batch,image_batch = tf.compat.v1.train.batch([label,image_cast],batch_size=100,num_threads=1,capacity=100)
        print(label_batch)
        
        # 开启会话
        with tf.compat.v1.Session() as sess:
            # 开启线程
            coord = tf.compat.v1.train.Coordinator()
            threads = tf.compat.v1.train.start_queue_runners(sess=sess, coord=coord)
            
            key_new, value_new, decoded_new, label_new, image_new,image_reshaped_new,image_transposed_new = sess.run(
                [key, value, decoded, label, image, image_reshaped, image_transposed]
            )
            label_value, image_value = sess.run([label_batch, image_batch])
            print("decoded_new:\n",decoded_new)
            
            # 回收线程
            coord.request_stop()
            coord.join(threads)
            
        return image_value, label_value
    
    def write_to_tfrecords(self, image_batch, label_batch):
        """
        将样本的特征值写入TFRecords文件
        """
        with tf.compat.v1.python_io.TFRecordWriter("cifar10.tfrecords") as writer:
            # 循环构造example对象，并序列化写入文件
            for i in range(100):
                image = image_batch[i].tostring() #bytes类型
                label = label_batch[i][0] #取出整型单值
                #print("label:{}\n image:{}".format(label,image))
                example = tf.compat.v1.train.Example(features=tf.compat.v1.train.Features(feature={
                    "image":tf.compat.v1.train.Feature(bytes_list=tf.compat.v1.train.BytesList(value=[image])),
                    "label":tf.compat.v1.train.Feature(int64_list=tf.compat.v1.train.Int64List(value=[label])),
                }))
                #example.SerializeToString()
                # 将序列化后的example下入example文件
                writer.write(example.SerializeToString())
                                                                      
        return None

    
    def read_tfrecords(self):
        # 1.构造文件名队列
        file_queue = tf.compat.v1.train.string_input_producer(["cifar10.tfrecords"])
        
        # 2.读取与解码
        reader = tf.compat.v1.TFRecordReader()
        key,value = reader.read(file_queue)
        
        # 解析 example
        feature = tf.compat.v1.parse_single_example(value, features={
            "image":tf.compat.v1.FixedLenFeature([], tf.string),
            "label":tf.compat.v1.FixedLenFeature([], tf.int64)
        })
        image = feature["image"]
        label = feature["label"]
        
        ## 解码
        image_decoded = tf.compat.v1.decode_raw(image, tf.uint8)
        print("image_decoded:",image_decoded)
        
        ## 图像形状调整
        image_reshaped = tf.compat.v1.reshape(image_decoded, [self.height, self.width, self.channels])
        
        # 3.构造批处理队列
        image_batch,label_batch = tf.compat.v1.train.batch([image_reshaped, label], batch_size=100, num_threads=2, capacity=100)
        
        
        with tf.compat.v1.Session() as sess:
            
            # 开启线程
            coord = tf.compat.v1.train.Coordinator()
            threads = tf.compat.v1.train.start_queue_runners(sess=sess, coord=coord)
            
            image_value, label_value = sess.run([image_batch, label_batch])
            print("image_value:\n",image_value)
            
            # 回收线程
            coord.requset_stop()
            coord.join(threads)
            
        return None

# 实例化Cifar
cifar = Cifar()
image_value, label_value = cifar.read_binary()
cifar.write_to_tfrecords(image_value, label_value)

2.单层线性模型

#Regression  单层全连接网络,线性回归模型
import numpy as np
import matplotlib.pylab as plt
np.random.seed(3)
from  keras.models import Sequential
from  keras.layers import Dense

#数据
X=np.linspace(-1,1,200)
np.random.shuffle(X) #将数据随机化
Y=0.5 *X +2+np.random.normal(0,0.05,(200,))


X_train,Y_train=X[:160],Y[:160]
X_test,Y_test=X[160:],Y[160:]

#创建模型
model=Sequential()
model.add(Dense(units=1))#输入维度 1，输出维度 1

#选择损失函数,均方差loss，随机梯度下降优化
model.compile(loss='mse',optimizer='sgd')

print("training------------------------")

for step in range(301):
     cost =model.train_on_batch(X_train,Y_train)  #训练一轮
     if step %1000==0:
         print("train cost",cost)

print("Testing------------------------")
cost=model.evaluate(X_test,Y_test,batch_size=4)
W,b=model.layers[0].get_weights()

#plt
Y_pred=model.predict(X_test)
plt.scatter(X_test,Y_test)
plt.plot(X_test,Y_pred)
plt.show()

码农公寓

Keras基础

1.tensorflow基本内容

1.忽略waring以下的问题

2.运行tensorflow1.x的代码

3.构件图

4.运行图

5.tensorflow API

6.线性回归实例

7.模型保存

8.tensorboard可视化

9.图片数据的输入

10.TFRecords文件

2.单层线性模型

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