Tensorflow2.1基础知识---丰富神经网络八股的内容

其他 2020-03-27 10:24:38 阅读次数: 0

本篇文章的主要目的是对上一篇文章已经搭建好的网络进一步丰富其内容，具体体现在6个方面：

	1. 加载自己制作的训练集
	2. 数据增强
	3. 断点续训
	4. 参数提取
	5. acc/loss可视化
	6. 给图识物

  1. 加载自己制作的训练集

    代码：

    import tensorflow as tf
from PIL import Image
import numpy as np
import os

#加载训练集的路径以及要保存的路径
train_path = './mnist_image_label/mnist_train_jpg_60000/'
train_txt = './mnist_image_label/mnist_train_jpg_60000.txt'
x_train_savepath = './mnist_image_label/mnist_x_train.npy'
y_train_savepath = './mnist_image_label/mnist_y_train.npy'
#加载测试集的路径以要保存的路径
test_path = './mnist_image_label/mnist_test_jpg_10000/'
test_txt = './mnist_image_label/mnist_test_jpg_10000.txt'
x_test_savepath = './mnist_image_label/mnist_x_test.npy'
y_test_savepath = './mnist_image_label/mnist_y_test.npy'


#--------------------------------------------------------------------------
#---------------------------------自制数据集-------------------------------
#--------------------------------------------------------------------------
def generateds(path,txt):
    f = open(txt,'r')  # 以只读形式打开文件
    contents = f.readlines()  # 读取文件中所有行
    f.close()  # 关闭txt文件
    x,y_ = [],[]  # 建立空列表
    for content in contents:  # 逐行取出
        value = content.split()  # 以空格分开，图片路径为value[0],标签为value[1],存入列表
        img_path = path + value[0]  # 拼出图片路径和文件名
        img = Image.open(img_path)  # 读入图片
        img = np.array(img.convert('L'))  # 图片变为8位宽灰度值的np.array格式
        img = img / 255  # 数据归一化（实现预处理）
        x.append(img)  #归一化后的数据，添加到列表x
        y_.append(value[1])  #标签添加到列表y
        print('loading:' + content)  #打印状态提示
    x = np.array(x)  #变为np.array格式
    y_ = np.array(y_)  #变为np.array格式
    y_ = y_.astype(np.int64)  #变为64位整型
    return x,y_  #返回输入特征x，返回标签y_

if os.path.exists(x_train_savepath) and os.path.exists(y_train_savepath) and os.path.exists(x_test_savepath) and os.path.exists(y_test_savepath):
    print('----------------Load Datasets---------------------')
    x_train_save = np.load(x_train_savepath)
    y_train = np.load(y_train_savepath)
    x_test_save = np.load(x_test_savepath)
    y_test = np.load(y_test_savepath)
    x_train = np.reshape(x_train_save,(len(x_train_save),28,28))
    x_test = np.reshape(x_test_save,(len(x_test_save),28,28))
else:
    print('----------------Generate Datasets---------------------')
    x_train,y_train = generateds(train_path,train_txt)
    x_test,y_test = generateds(test_path,test_txt)
    
    print('----------------Save Datasets---------------------')
    x_train_save = np.reshape(x_train,(len(x_train), -1))
    x_test_save = np.reshape(x_test,(len(x_test), -1))
    np.save(x_train_savepath,x_train_save)
    np.save(y_train_savepath,y_train)
    np.save(x_test_savepath,x_test_save)
    np.save(y_test_savepath,y_test)
    

    

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128,activation = 'relu'),
    tf.keras.layers.Dense(10,activation = 'softmax')
])

model.compile(optimizer = 'adam',
             loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = False),
             metrics = ['sparse_categorical_accuracy'])

model.fit(x_train,y_train,batch_size = 32,epochs = 5,validation_data = (x_test,y_test),validation_freq=1)
model.summary()

  2. 数据增强
    函数介绍：

     Image_gen_train = tf.keras.preprocessing.image.ImageDataGenerator(
 	Rescale = 所有数据将乘以该数值
 	Rotation_range = 随机旋转角度数范围
 	Width_shift_range = 随机宽度偏移量
 	Height_shift_range = 随机高度偏移量
 	水平翻转：horizontal_flip = 是否随机水平翻转
 	随机缩放：zoom_range = 随机缩放的范围[1-n,1+n]
 )
 Image_gen_train.fit(x_train)

    代码：

    import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

mnist = tf.keras.datasets.mnist
(x_train,y_train),(x_test,y_test) = mnist.load_data()
x_train,x_test = x_train / 255.0,x_test / 255.0
x_train = x_train.reshape(x_train.shape[0],28,28,1)  #给数据增减一个维度，从（60000，28，28）reshape为（60000，28，28，1）

image_gen_train = ImageDataGenerator(
    rescale = 1. / 1.,
    rotation_range = 45,
    width_shift_range = .15,
    height_shift_range = .15,
    horizontal_flip = False,
    zoom_range = 0.5
)
image_gen_train.fit(x_train)

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128,activation = 'relu'),
    tf.keras.layers.Dense(10,activation = 'softmax')
])

model.compile(optimizer = 'adam',
             loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = False),
             metrics = ['sparse_categorical_accuracy'])

model.fit(image_gen_train.flow(x_train,y_train,batch_size = 32),epochs = 5,validation_data = (x_test,y_test),validation_freq=1)
model.summary()

  3. 断点续训
    a. 读取模型：

     函数：load_weights(路径文件名)
 例子：
 	checkpoint_save_path = "./checkpoint/mnist.ckpt"
 	if os.path.exists(checkpoint_save_path + '.index'):
 		print("--------------load the model---------------")
 		model.load_weights(checkpoint_save_path)

    b. 保存模型：

     函数：
 	tf.keras.callbacks.ModelCheckpoint(
 		filepath = 路径文件名,
 		save_weights_only = True/False,
 		save_best_only = True/False
 	)
 	history = mlodel.fit(callbacks = [cp_callback])
 例子：
 	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=5,validation_data=(x_test,y_test),validation_freq=1,callbacks = [cp_callback])

    代码：

    import tensorflow as tf
import os


mnist = tf.keras.datasets.mnist
(x_train,y_train),(x_test,y_test) = mnist.load_data()
x_train,x_test = x_train / 255.0,x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128,activation = 'relu'),
    tf.keras.layers.Dense(10,activation = 'softmax')
])

model.compile(optimizer = 'adam',
             loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = False),
             metrics = ['sparse_categorical_accuracy'])

checkpoint_save_path = "./checkpoint/mnist.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=5,validation_data=(x_test,y_test),validation_freq=1,callbacks = [cp_callback])
model.summary()

  4. 参数提取
    代码：

    import tensorflow as tf
import os
import numpy as np
np.set_printoptions(threshold = np.inf)

mnist = tf.keras.datasets.mnist
(x_train,y_train),(x_test,y_test) = mnist.load_data()
x_train,x_test = x_train / 255.0,x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128,activation = 'relu'),
    tf.keras.layers.Dense(10,activation = 'softmax')
])

model.compile(optimizer = 'adam',
             loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = False),
             metrics = ['sparse_categorical_accuracy'])

checkpoint_save_path = "./checkpoint/mnist.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=5,validation_data=(x_test,y_test),validation_freq=1,callbacks = [cp_callback])
model.summary()

#打印参数
print(model.trainable_variables)
#将参数保存到文件weights.txt中
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()

  5. acc/loss可视化
    history包含得出的结果：

    1. 训练集loss：loss
    2. 测试集loss：val_loss
    3. 训练集准确率：sparse_categorical_accuracy
    4. 测试集准确率：val_sparse_cateforical_accuracy

    代码：

    import tensorflow as tf
import os
import numpy as np
from matplotlib import pyplot as plt
np.set_printoptions(threshold = np.inf)

mnist = tf.keras.datasets.mnist
(x_train,y_train),(x_test,y_test) = mnist.load_data()
x_train,x_test = x_train / 255.0,x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128,activation = 'relu'),
    tf.keras.layers.Dense(10,activation = 'softmax')
])

model.compile(optimizer = 'adam',
             loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = False),
             metrics = ['sparse_categorical_accuracy'])

checkpoint_save_path = "./checkpoint/mnist.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=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()

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 abd 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 abd Validation Loss")
plt.legend()
plt.show()

  6. 给图识物（也就是前向传播的应用）

    1. 调用函数：predict(输入特征，batch_size=整数) 返回前向传播的计算结果

    2. 步骤：

       a. 浮现模型
 	model = tf.keras.models.Sequential([
 	    tf.keras.layers.Flatten(),
 	    tf.keras.layers.Dense(128, activation='relu'),
 	    tf.keras.layers.Dense(10, activation='softmax')])
 b. 加载参数
 	model.load_weights(model_save_path)
 c. 预测结果
 	result = model.predict(x_predict)

    代码：

    from PIL import Image
import numpy as np
import tensorflow as tf

model_save_path = './checkpoint/mnist.ckpt'

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')])

model.load_weights(model_save_path)

preNum = int(input("input the number of test pictures:"))

for i in range(preNum):
    image_path = input("the path of test picture:")
    img = Image.open(image_path)
    img = img.resize((28, 28), Image.ANTIALIAS)
    img_arr = np.array(img.convert('L'))

    for i in range(28):
        for j in range(28):
            if img_arr[i][j] < 200:
                img_arr[i][j] = 255
            else:
                img_arr[i][j] = 0

    img_arr = img_arr / 255.0
    x_predict = img_arr[tf.newaxis, ...]
    result = model.predict(x_predict)

    pred = tf.argmax(result, axis=1)

    print('\n')
    tf.print(pred)
下面的是笔者的微信公众号，欢迎关注，会持续更新c++、python、tensorflow、机器学习、深度学习等系列文章

                      在这里插入图片描述

Xiao_Jie123
发布了38 篇原创文章 · 获赞 49 · 访问量 6932
私信 关注

猜你喜欢

转载自blog.csdn.net/Xiao_Jie123/article/details/104993125
今日推荐
《美国对全球网络空间安全与发展的威胁和破坏》报告发布
火速冲上 GitHub 热榜 —— 开源编程语言、框架哪有这么可爱？
北京人形机器人创新中心发布全球首个纯电驱拟人奔跑的全尺寸人形机器人“天工”
LFOSSA 源来如此公开课 | 掌握云原生未来：CNCF 认证全面攻略与备考秘籍
周排行
循环神经网络（rnn）讲解
Tigao教程四：单独的关节运动
金蝶K3WISE15.0-注册套打教程
如何在Mac上配置Kubernetes
Android应用结束自身进程的方法
SpringMVC学习 十三 拦截器栈
中国驻洛杉矶总领馆举行新春招待会
HttpClient get post 发送
11 - three.js 笔记 - 绘制三维字体模型
Mysql递归获取某个父节点下面的所有子节点和子节点上的所有父节点
每日归档
更多
2024-05-01(4)
2024-04-30(1)
2024-04-29(40)
2024-04-28(0)
2024-04-27(56)
2024-04-26(39)
2024-04-25(22)
2024-04-24(36)
2024-04-23(26)
2024-04-22(39)

代码天地 © 2018 codetd.com

境外服务器   最新电视剧2022