TensorFlow 2.0 笔记（四）功能扩展
第四章 神经网络优化
1 回顾
1.1 tf.keras 搭建神经网络八股——六步法
-
import——导入所需的各种库和包
-
x_train, y_train——导入数据集、自制数据集、数据增强
-
model=tf.keras.models.Sequential
/class MyModel(Model) model=MyModel——定义模型
-
model.compile——配置模型
-
model.fit——训练模型、断点续训
-
model.summary——参数提取、acc/loss 可视化、前向推理实现应用
import tensorflow as tf
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'])
model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
model.summary()
2 自制数据集，应对特定应用
2.1 观察数据集数据结构，配成特征标签对
mnist_image_label文件夹：
四个文件分别对应为训练集图片、训练集标签、测试集图片、测试集标签
图片文件夹：
标签文件：
代码mnist_train_ex1.py：
import tensorflow as tf
from PIL import Image
import numpy as np
import os
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'
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'
def generateds(path, txt):
f = open(txt, 'r') # 以只读形式打开txt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，贴到列表x
y_.append(value[1]) # 标签贴到列表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()
2.2 数据增强，增大数据量
1.数据增强（增大数据量）
image_gen_train=tf.keras.preprocessing.image.ImageDataGenerator( 增强方法)
image_gen_train.fit(x_train)
常用增强方法：
- 缩放系数：rescale=所有数据将乘以提供的值
- 随机旋转：rotation_range=随机旋转角度数范围宽度偏移：width_shift_range=随机宽度偏移量
- 高度偏移：height_shift_range=随机高度偏移量水平翻转：horizontal_flip=是否水平随机翻转
- 随机缩放：zoom_range=随机缩放的范围 [1-n，1+n]
例：
image_gen_train = ImageDataGenerator(
rescale=1./255, # 原像素值 0～255 归至 0～1
rotation_range=45, #随机 45 度旋转
width_shift_range=.15, #随机宽度偏移 [-0.15,0.15)
height_shift_range=.15, #随机高度偏移 [-0.15,0.15)
horizontal_flip=True, #随机水平翻转
zoom_range=0.5 # 随机缩放到 [1-50％，1+50%]
代码 mnist_train_ex2.py：
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) # 给数据增加一个维度,从(60000, 28, 28)reshape为(60000, 28, 28, 1)
image_gen_train = ImageDataGenerator(
rescale=1. / 1., # 如为图像，分母为255时，可归至0～1
rotation_range=45, # 随机45度旋转
width_shift_range=.15, # 宽度偏移
height_shift_range=.15, # 高度偏移
horizontal_flip=False, # 水平翻转
zoom_range=0.5 # 将图像随机缩放阈量50％
)
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()
注：
1、model.fit(x_train,y_train,batch_size=32,……)变为 model.fit(image_gen_train.flow(x_train,y_train,batch_size=32), ……)；
2、数据增强函数的输入要求是 4 维，通过 reshape 调整；
3、如果报错：缺少scipy 库，pip install scipy 即可。
2.3 数据增强可视
代码show_augmented _images.py
# 显示原始图像和增强后的图像
import tensorflow as tf
from matplotlib import pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
image_gen_train = ImageDataGenerator(
rescale=1. / 255,# 原像素值 0～255 归至 0～1
rotation_range=45,# 随机45度旋转
width_shift_range=.15,# 宽度偏移
height_shift_range=.15, # 高度偏移
horizontal_flip=False,# 水平翻转
zoom_range=0.5# 将图像随机缩放阈量50％
)
image_gen_train.fit(x_train)
print("xtrain",x_train.shape)
x_train_subset1 = np.squeeze(x_train[:12])
print("xtrain_subset1",x_train_subset1.shape)
print("xtrain",x_train.shape)
x_train_subset2 = x_train[:12] # 一次显示12张图片
print("xtrain_subset2",x_train_subset2.shape)
fig = plt.figure(figsize=(20, 2))
plt.set_cmap('gray')
# 显示原始图片
for i in range(0, len(x_train_subset1)):
ax = fig.add_subplot(1, 12, i + 1)
ax.imshow(x_train_subset1[i])
fig.suptitle('Subset of Original Training Images', fontsize=20)
plt.show()
# 显示增强后的图片
fig = plt.figure(figsize=(20, 2))
for x_batch in image_gen_train.flow(x_train_subset2, batch_size=12, shuffle=False):
for i in range(0, 12):
ax = fig.add_subplot(1, 12, i + 1)
ax.imshow(np.squeeze(x_batch[i]))
fig.suptitle('Augmented Images', fontsize=20)
plt.show()
break;
3 断点续训，存取模型
3.1 读取模型
load_weights(路径文件名)
checkpoint_save_path = "./checkpoint/mnist.ckpt"
if os.path.exists(checkpoint_save_path + '.index'):
print('-------------load the model-----------------')
model.load_weights(checkpoint_save_path)
3.2 保存模型
借助 tensorflow 给出的回调函数，直接保存参数和网络
tf.keras.callbacks.ModelCheckpoint(
filepath= 路径文件名,
save_weights_only=True,
monitor='val_loss', # val_loss or loss
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])
注：monitor 配合 save_best_only 可以保存最优模型，包括：训练损失最小模型、测试损失最小模型、训练准确率最高模型、测试准确率最高模型等。
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，写至文本
4.1 提取可训练参数
model.trainable_variables 模型中可训练的参数
4.2 设置print输出格式
np.set_printoptions(precision=小数点后按四舍五入保留几位,threshold=数组元素数量少于或等于门槛值，打印全部元素；否则打印门槛值+1 个元素，中间用省略号补充)
>>> np.set_printoptions(precision=5)
>>> print(np.array([1.123456789]))
[1.12346]
>>> np.set_printoptions(threshold=5)
>>> print(np.arange(10))
[0 1 2 … , 7 8 9]
注：precision=np.inf 打印完整小数位；threshold=np.nan 打印全部数组元素。
代码mnist_train_ex4.py：
import tensorflow as tf
import os
import numpy as np
# 设置显示全部内容 np.inf 表示无穷大
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()
模型参数打印结果：
weights.txt：
5 acc/loss 可视化，查看效果
5.1 acc曲线和loss曲线
history=model.fit(训练集数据, 训练集标签, batch_size=, epochs=, validation_split=用作测试数据的比例,validation_data=测试集, validation_freq=测试频率)
history：
- loss：训练集
- loss val_loss：测试集 loss
- sparse_categorical_accuracy：训练集准确率v
- al_sparse_categorical_accuracy：测试集准确率
# 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']
############################################### 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()
acc和loss曲线：
代码mnist_train_ex5.py：
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(256, 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,
# monitor='val_loss',
save_best_only=True)
history = model.fit(x_train, y_train, batch_size=32, epochs=10, 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.figure(figsize=(8, 8))
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()
6 应用程序，给图识物
6.1 给图识物
输入一张手写数字图片：
神经网络自动识别出值：
6
手写十个数，正确率90%以上合格。
6.2 前向传播执行应用
predict(输入数据, batch_size=整数) 返回前向传播计算结果
注：predict 参数详解。
(1)x：输入数据，Numpy 数组（或者 Numpy 数组的列表，如果模型有多个输出）；
(2)batch_size：整数，由于 GPU 的特性，batch_size最好选用 8，16，32，64……，如果未指定，默认为 32；
(3)verbose: 日志显示模式，0 或 1；
(4)steps: 声明预测结束之前的总步数（批次样本），默认值 None；
(5)返回：预测的 Numpy 数组（或数组列表）。
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(256, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')])
model.load_weights(model_save_path) # 加载模型
preNum = int(input("input the number of test pictures:")) # 预测图片数量
for i in range(preNum):
image_path = input("the path of test picture:") # 预测图片路径
img = Image.open(image_path) # 打开图片
img = img.resize((28, 28), Image.ANTIALIAS) # 调整尺寸和类型
img_arr = np.array(img.convert('L'))
for i in range(28): # 二值化
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) # 预测
pred = tf.argmax(result, axis=1)
print('\n')
tf.print(pred) # 输出结果
注：
1、输出结果 pred 是张量，需要用 tf.print，print 打印出来是 tf.Tensor([1], shape=(1,), dtype=int64)；
2、去掉二值化，出现无法收敛问题，需要对数据集进行归一化。
