六、AlexNet实现中文字体识别——隶书和行楷

• 前文
• 中文字体识别——隶书和行楷
  • 数据生成器
  • 图像显示
  • AlexNet模型构建
  • AlexNet模型编译与拟合
  • 注意：
• GitHub下载地址：

前文

中文字体识别——隶书和行楷

数据生成器

from keras.preprocessing.image import ImageDataGenerator

IMSIZE = 227
validation_generator = ImageDataGenerator(rescale=1. / 255).flow_from_directory('../../data/ChineseStyle/test',
                                                                                target_size=(IMSIZE, IMSIZE),
                                                                                batch_size=200,
                                                                                class_mode='categorical'
                                                                                )

train_generator = ImageDataGenerator(rescale=1. / 255).flow_from_directory('../../data/ChineseStyle/train',
                                                                           target_size=(IMSIZE, IMSIZE),
                                                                           batch_size=200,
                                                                           class_mode='categorical'
                                                                           )

图像显示

from matplotlib import pyplot as plt

plt.figure()
fig, ax = plt.subplots(3, 5)
fig.set_figheight(7)
fig.set_figwidth(15)
ax = ax.flatten()
X, Y = next(validation_generator)
for i in range(15): ax[i].imshow(X[i, :, :, :, ])

AlexNet模型构建

from keras.layers import Activation, Conv2D, Dense
from keras.layers import Dropout, Flatten, Input, MaxPooling2D
from keras import Model

input_layer = Input([IMSIZE, IMSIZE, 3])
x = input_layer
x = Conv2D(96, [11, 11], strides=[4, 4], activation='relu')(x)
x = MaxPooling2D([3, 3], strides=[2, 2])(x)
x = Conv2D(256, [5, 5], padding="same", activation='relu')(x)
x = MaxPooling2D([3, 3], strides=[2, 2])(x)
x = Conv2D(384, [3, 3], padding="same", activation='relu')(x)
x = Conv2D(384, [3, 3], padding="same", activation='relu')(x)
x = Conv2D(256, [3, 3], padding="same", activation='relu')(x)
x = MaxPooling2D([3, 3], strides=[2, 2])(x)

x = Flatten()(x)
x = Dense(4096, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(4096, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(2, activation='softmax')(x)
output_layer = x
model = Model(input_layer, output_layer)
model.summary()

AlexNet模型编译与拟合

from keras.optimizers import Adam

model.compile(loss='categorical_crossentropy',
              optimizer=Adam(lr=0.001), metrics=['accuracy'])
model.fit_generator(train_generator, epochs=20, validation_data=validation_generator)

注意：

因为自己是使用tensorflow-GPU版本，自己电脑是1050Ti，4G显存。实际运行时候batch_size设置为了不到15大小，太大了就显存资源不足。但是batch_size太小，总的数据集较大较多最后消耗时间就较长。

所以为了效率和烧显卡，请酌情考虑

GitHub下载地址：

Tensorflow1.15深度学习