前文
- 一、Windows系统下安装Tensorflow2.x（2.6）
- 二、深度学习-读取数据
- 三、Tensorflow图像处理预算
- 四、线性回归模型的tensorflow实现
- 五、深度学习-逻辑回归模型
- 六、AlexNet实现中文字体识别——隶书和行楷
加利福尼亚理工学院鸟类数据库分类
数据生成器
from keras.preprocessing.image import ImageDataGenerator
IMSIZE = 224
train_generator = ImageDataGenerator(rescale=1. / 255).flow_from_directory('../../data/data_vgg/train',
target_size=(IMSIZE, IMSIZE),
batch_size=20,
class_mode='categorical'
)
validation_generator = ImageDataGenerator(rescale=1. / 255).flow_from_directory('../../data/data_vgg/test',
target_size=(IMSIZE, IMSIZE),
batch_size=20,
class_mode='categorical'
)
)
图像显示
from matplotlib import pyplot as plt
plt.figure()
fig, ax = plt.subplots(2, 5)
fig.set_figheight(6)
fig.set_figwidth(15)
ax = ax.flatten()
X, Y = next(validation_generator)
for i in range(15): ax[i].imshow(X[i, :, :, ])
VGG模型构建
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Flatten, Dense, Input, Activation
from keras import Model
from keras.layers import GlobalAveragePooling2D
input_shape = (IMSIZE, IMSIZE, 3)
input_layer = Input(input_shape)
x = input_layer
x = Conv2D(64, [3, 3], padding="same", activation='relu')(x)
x = Conv2D(64, [3, 3], padding="same", activation='relu')(x)
x = MaxPooling2D((2, 2))(x)
x = Conv2D(128, [3, 3], padding="same", activation='relu')(x)
x = Conv2D(128, [3, 3], padding="same", activation='relu')(x)
x = Conv2D(128, [3, 3], padding="same", activation='relu')(x)
x = MaxPooling2D((2, 2))(x)
x = Conv2D(256, [3, 3], padding="same", activation='relu')(x)
x = Conv2D(256, [3, 3], padding="same", activation='relu')(x)
x = Conv2D(256, [3, 3], padding="same", activation='relu')(x)
x = MaxPooling2D((2, 2))(x)
x = Conv2D(512, [3, 3], padding="same", activation='relu')(x)
x = Conv2D(512, [3, 3], padding="same", activation='relu')(x)
x = Conv2D(512, [3, 3], padding="same", activation='relu')(x)
x = MaxPooling2D((2, 2))(x)
x = Conv2D(512, [3, 3], padding="same", activation='relu')(x)
x = Conv2D(512, [3, 3], padding="same", activation='relu')(x)
x = Conv2D(512, [3, 3], padding="same", activation='relu')(x)
x = MaxPooling2D((2, 2))(x)
x = GlobalAveragePooling2D()(x)
x = Dense(315)(x)
x = Activation('softmax')(x)
output_layer = x
model_vgg16 = Model(input_layer, output_layer)
model_vgg16.summary()
VGG模型编译与拟合
from keras.optimizers import Adam
model_vgg16.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0.001),
metrics=['accuracy'])
model_vgg16.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，所以最后消耗时间就较长。
[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:7576b1d0-7718-434f-977c-32af2c49595c[En]
[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:7b787ff2-2365-4902-a514-a19686bed26a
数据集来源：kaggle平台315种鸟类：315 Bird Species – Classification | Kaggle
GitHub下载地址：
Original: https://www.cnblogs.com/lehoso/p/15614793.html
Author: 李好秀
Title: 七、VGG16实现鸟类数据库分类
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