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CNNTraining.py
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CNNTraining.py
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# -*- coding:utf-8 -*-
# keras imports for the dataset and building our neural network
from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Activation
from keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.utils import to_categorical
# from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
from readImage import readImage, returnImage
from chessInfo import docuChessInfo
from chessInfo import renameImages
from vertifyModel import readPreImage, returnPreImage, dispResult
import sys
sys.dont_write_bytecode = True
import matplotlib.pyplot as plt # plt 用于显示图片
import matplotlib.image as mp # mpimg 用于读取图片
from PIL import Image
import numpy as np
import math
import os
img_h = 620
img_w = 620
channel = 1
classes = 10
# 训练cnn模型
# 这里的x_train,x_test里面存放的不是图像路径，而是读取图像之后的数组，与之前的x_train, x_test不一样
def trainModel(x_train, y_train, x_test, y_test):
# 1. 数据预处理
# 归一化处理（0~1）
x_train = x_train.astype('float32') # 类型转化或者类型声明
x_test = x_test.astype('float32')
# x_test /= 255
# x_train /= 255
# 将类别向量转换为二进制（只有0和1）的矩阵类型表示
y_train = to_categorical(y_train, classes)
y_test = to_categorical(y_test, classes)
printShape = "x_train.shape is: " # (480, 128, 128, 1)
print(printShape, x_train.shape)
print("-*-*-*- Step -> 1 -> Normalization Done！ -*-*-*-")
# 2. 定义模型结构
spoch_number = 10 # spoch number
model = Sequential()
# New model
model.add(Conv2D(32, (3, 3),
activation='relu', padding='same',
data_format='channels_last', name='layer1_con1',
input_shape=(img_h, img_w, channel)))
model.add(Conv2D(32, (3, 3),
activation='relu', padding='same',
data_format='channels_last', name='layer1_con2'))
model.add(MaxPool2D(pool_size=(2, 2),
strides=(2,2), padding = 'same',
data_format='channels_last',name = 'layer1_pool'))
model.add(Dropout(0.25)) # dropout层可以防止过拟合，每次有25%的数据将被抛弃
model.add(Conv2D(64, (3, 3),
activation='relu', padding='same',
data_format='channels_last',name='layer2_con1'))
model.add(Conv2D(64, (3, 3),
activation='relu', padding='same',
data_format='channels_last',name='layer2_con2'))
model.add(MaxPool2D(pool_size=(2, 2),
strides=(2,2), padding = 'same',
data_format='channels_last',name = 'layer2_pool'))
# Old model
# model.add(Conv2D(16, kernel_size=(5, 5), input_shape=(img_h, img_w, channel))) # 卷积
# model.add(Activation('tanh'))
# model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2))) # 池化
# model.add(Conv2D(16, kernel_size=(5, 5)))
# model.add(Activation('tanh'))
# model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(Conv2D(32, kernel_size=(5, 5)))
# model.add(Activation('tanh'))
# model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(Conv2D(32, kernel_size=(5, 5)))
# model.add(Activation('tanh'))
# model.add(MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu')) # 该全连接层共128个神经元
model.add(Dense(10, activation='softmax')) # 一共分为10类，所以最后一层有10个神经元，并且采用softmax输出
# model.add(Flatten())
# model.add(Dense(1000, activation='relu')) # 全连接
# model.add(Dropout(0.5))
# model.add(Dense(classes, activation='softmax'))
print("-*-*-*- Step -> 2 -> Model Building Done！ -*-*-*-")
# 3. 编译
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
print("-*-*-*- Step -> 3 -> Model Compile Done！ -*-*-*-")
# 4. 训练
# history = model.fit(x_train, y_train, batch_size = 64, epochs = i, validation_data = (x_test, y_test))
# print(history.history.keys())
# 定义数据生成器用于数据提升，其返回一个生成器对象datagen，datagen每被调用一
# 次其生成一组数据（顺序生成），节省内存，其实就是python的数据生成器
datagen = ImageDataGenerator(
featurewise_center=False, # 是否使输入数据去中心化（均值为0），
samplewise_center=False, # 是否使输入数据的每个样本均值为0
featurewise_std_normalization=False, # 是否数据标准化（输入数据除以数据集的标准差）
samplewise_std_normalization=False, # 是否将每个样本数据除以自身的标准差
zca_whitening=False, # 是否对输入数据施以ZCA白化
rotation_range=15, # 数据提升时图片随机转动的角度(范围为0～180)
width_shift_range=0.15, # 数据提升时图片水平偏移的幅度（单位为图片宽度的占比，0~1之间的浮点数）
height_shift_range=0.15, # 同上，只不过这里是垂直
horizontal_flip=False, # 是否进行随机水平翻转
vertical_flip=False) # 是否进行随机垂直翻转
# 计算整个训练样本集的数量以用于特征值归一化、ZCA白化等处理
datagen.fit(x_train)
# 利用生成器开始训练模型
generator = datagen.flow(x_train, y_train, batch_size = 128)
history = model.fit_generator(generator,
steps_per_epoch = len(x_train)/128,
epochs = spoch_number,
validation_data = (x_test, y_test),
verbose = 1.0)
# Error occurred when finalizing GeneratorDataset iterator: Failed precondition:
# Python interpreter state is not initialized. The process may be terminated.
# 内存不足，将bach_size改小
# 绘制训练 & 验证的准确率值
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
# 绘制训练 & 验证的损失值
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
print("-*-*-*- Step -> 4 -> Training Done！ -*-*-*-")
# 5. 评估模型
score = model.evaluate(x_test, y_test)
print('acc', score[1])
# saving the model
save_dir = "./model_save"
model_name = "model" + "_" + str(spoch_number) + "_" + str(score[1]) + '.h5'
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
if not os.path.exists(save_dir): #判断是否存在
os.makedirs(save_dir) #不存在则创建
print("-*-*-*- Step -> 5 -> Model Evaluation Done！ -*-*-*-")
print('Saved Trained Model at %s ' % save_dir + "/" + model_name)
print("-*-*-*- CNN Training Model Done! -*-*-*-")
# 6. 测试集显示
def main(): # 主函数
docuChessInfo() # 记录信息
x_train, y_train, x_test, y_test = readImage() # 测试集与训练集的分类整理（路径与对应人名编号）
x_train_img, x_test_img = returnImage(x_train, x_test) # 图片读取
trainModel(x_train_img, y_train, x_test_img, y_test) # CNN训练
# 以下三个函数属于检验模块，详见 vertify.py
test = readPreImage()
Test = returnPreImage(test) # 检验模型的图片读取
dispResult(Test, test) # 输出检验结果
# 调用函数
if __name__ == '__main__':
main()
'''
Data: https://www.heywhale.com/mw/dataset/5fa3f55fca48e0003014d7b0/file
Date: 2021/04/08
'''