tensorflow2.0+数据增强+VGG-Net
引言：上篇文章《VGG-net训练过程总结》详细介绍了VGG网络结构和训练过程，主要是使用1.x训练。今天在学习Tensorflow2.0，也从新使用2.0来实现VGG网络。
1、导入相关包
import tensorflow as tf
import numpy as np
import pickle as p
import os
from tensorflow.keras import models, optimizers, regularizers
from tensorflow.keras.callbacks import LearningRateScheduler
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
from tensorflow import keras
#os.environ['CUDA_VISIBLE_DEVICES'] = '0'2、设置训练参数
weight_decay = 5e-4
batch_size = 128
learning_rate = 1e-2
dropout_rate = 0.5
epoch_num = 2003、定义读取数据集函数
def load_CIFAR_batch(filename):
""" load single batch of cifar """
with open(filename, 'rb')as f:
datadict = p.load(f, encoding='iso-8859-1')
X = datadict['data']
Y = datadict['labels']
X = X.reshape(10000, 3, 32, 32)
Y = np.array(Y)
return X, Y
def load_CIFAR(Foldername):
train_data = np.zeros([50000, 32, 32, 3], dtype=np.float32)
train_label = np.zeros([50000, 10], dtype=np.float32)
test_data = np.zeros([10000, 32, 32, 3], dtype=np.float32)
test_label = np.zeros([10000, 10], dtype=np.float32)
for sample in range(5):
X, Y = load_CIFAR_batch(Foldername + "/data_batch_" + str(sample + 1))
for i in range(3):
train_data[10000 * sample:10000 * (sample + 1), :, :, i] = X[:, i, :, :]
for i in range(10000):
train_label[i + 10000 * sample][Y[i]] = 1
X, Y = load_CIFAR_batch(Foldername + "/test_batch")
for i in range(3):
test_data[:, :, :, i] = X[:, i, :, :]
for i in range(10000):
test_label[i][Y[i]] = 1
return train_data, train_label, test_data, test_label4、定义网络模型
def VGG16():
model = models.Sequential()
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3),
kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Flatten()) # 2*2*512
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
return model5、定义学习率调整函数，加快网络收敛
def scheduler(epoch):
if epoch < epoch_num * 0.4:
return learning_rate
if epoch < epoch_num * 0.8:
return learning_rate * 0.1
return learning_rate * 0.01
6、主函数
if __name__ == '__main__':
# gpu config
#physical_devices = tf.config.experimental.list_physical_devices('GPU')
#tf.config.experimental.set_memory_growth(device=physical_devices[0], enable=True)
# load data
train_images, train_labels, test_images, test_labels = load_CIFAR(
'/home/user/PycharmProjects/ImageClassification/VGG-16-19/VGG-16-TF2/cifar-10-batches-py')
# 不需要转换为one-hot,labels已经是one-hot
#(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()
#train_labels = tf.keras.utils.to_categorical(train_labels, 10)
#test_labels = tf.keras.utils.to_categorical(test_labels, 10)
print('train_images.shape', train_images.shape) # (50000, 32, 32, 3)
print('train_labels.shape', train_labels.shape) # (50000, 10)
print('test_images.shape', test_images.shape) # (10000, 32, 32, 3)
print('test_labels.shape', test_labels.shape) # (10000, 10)
# get model
model = VGG16()
# show
model.summary()
# train
sgd = optimizers.SGD(lr=learning_rate, momentum=0.9, nesterov=True)
change_lr = LearningRateScheduler(scheduler)
# Tensorboard
import time
from tensorflow.keras.callbacks import TensorBoard
# 设置格式化模型名称，以时间为标记
model_name = "mode-{}".format(int(time.time()))
# 设定存储位置，每个模型不一样的路径
tensorboard = TensorBoard(log_dir='vgg16_from_tensorflow20/{}'.format(model_name))
data_augmentation = False # 改为False，Ture有bug，后续再改改看20200421
if not data_augmentation:
print('Not using data augmentation')
# 不进行数据扩充
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.fit(train_images, train_labels,
batch_size=batch_size,
epochs=epoch_num,
callbacks=[change_lr],
validation_data=(test_images, test_labels))
else:
print('Using real-time data augmentation') # 实时数据增强
## tensorflow2.0 数据增强
train_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255,
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=False,
fill_mode='nearest')
train_datagen.fit(train_images)
# 测试集不需要进行数据处理
test_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255) # 不增强验证数据
#test_datagen.fit(test_images)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.fit(train_datagen.flow(train_images, train_labels, batch_size=128), # flows是实时
steps_per_epoch=train_images.shape[0] // batch_size, # steps_per_epoch: 每个epoch所需的steps,不能少
epochs=200,
callbacks=[change_lr],
validation_data=test_datagen.flow(test_images, test_labels, batch_size=128),
#validation_data = (test_images, test_labels),
validation_steps=test_images.shape[0] // batch_size, # 这个也是不能少
#callbacks=[tensorboard]
)
代码汇总：
import tensorflow as tf
import numpy as np
import pickle as p
import os
from tensorflow.keras import models, optimizers, regularizers
from tensorflow.keras.callbacks import LearningRateScheduler
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
from tensorflow import keras
#os.environ['CUDA_VISIBLE_DEVICES'] = '0'
weight_decay = 5e-4
batch_size = 128
learning_rate = 1e-2
dropout_rate = 0.5
epoch_num = 200
def load_CIFAR_batch(filename):
""" load single batch of cifar """
with open(filename, 'rb')as f:
datadict = p.load(f, encoding='iso-8859-1')
X = datadict['data']
Y = datadict['labels']
X = X.reshape(10000, 3, 32, 32)
Y = np.array(Y)
return X, Y
def load_CIFAR(Foldername):
train_data = np.zeros([50000, 32, 32, 3], dtype=np.float32)
train_label = np.zeros([50000, 10], dtype=np.float32)
test_data = np.zeros([10000, 32, 32, 3], dtype=np.float32)
test_label = np.zeros([10000, 10], dtype=np.float32)
for sample in range(5):
X, Y = load_CIFAR_batch(Foldername + "/data_batch_" + str(sample + 1))
for i in range(3):
train_data[10000 * sample:10000 * (sample + 1), :, :, i] = X[:, i, :, :]
for i in range(10000):
train_label[i + 10000 * sample][Y[i]] = 1
X, Y = load_CIFAR_batch(Foldername + "/test_batch")
for i in range(3):
test_data[:, :, :, i] = X[:, i, :, :]
for i in range(10000):
test_label[i][Y[i]] = 1
return train_data, train_label, test_data, test_label
def VGG16():
model = models.Sequential()
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3),
kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Flatten()) # 2*2*512
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
return model
def scheduler(epoch):
if epoch < epoch_num * 0.4:
return learning_rate
if epoch < epoch_num * 0.8:
return learning_rate * 0.1
return learning_rate * 0.01
if __name__ == '__main__':
# gpu config
#physical_devices = tf.config.experimental.list_physical_devices('GPU')
#tf.config.experimental.set_memory_growth(device=physical_devices[0], enable=True)
# load data
#train_images, train_labels, test_images, test_labels = load_CIFAR(
# '/home/user/PycharmProjects/ImageClassification/VGG-16-19/VGG-16-TF2/cifar-10-batches-py')
# 不需要转换为one-hot,labels已经是one-hot
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()
train_labels = tf.keras.utils.to_categorical(train_labels, 10)
test_labels = tf.keras.utils.to_categorical(test_labels, 10)
print('train_images.shape', train_images.shape) # (50000, 32, 32, 3)
print('train_labels.shape', train_labels.shape) # (50000, 10)
print('test_images.shape', test_images.shape) # (10000, 32, 32, 3)
print('test_labels.shape', test_labels.shape) # (10000, 10)
# get model
model = VGG16()
# show
model.summary()
# train
sgd = optimizers.SGD(lr=learning_rate, momentum=0.9, nesterov=True)
change_lr = LearningRateScheduler(scheduler)
# Tensorboard
import time
from tensorflow.keras.callbacks import TensorBoard
# 设置格式化模型名称，以时间为标记
model_name = "mode-{}".format(int(time.time()))
# 设定存储位置，每个模型不一样的路径
tensorboard = TensorBoard(log_dir='vgg16_from_tensorflow20/{}'.format(model_name))
data_augmentation = False # 改为False，Ture还有bug
if not data_augmentation:
print('Not using data augmentation')
# 不进行数据扩充
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.fit(train_images, train_labels,
batch_size=batch_size,
epochs=epoch_num,
callbacks=[change_lr],
validation_data=(test_images, test_labels))
else:
print('Using real-time data augmentation') # 实时数据增强
## tensorflow2.0 数据增强
train_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255,
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=False,
fill_mode='nearest')
train_datagen.fit(train_images)
# 测试集不需要进行数据处理
test_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=1./255) # 不增强验证数据
#test_datagen.fit(test_images)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.fit(train_datagen.flow(train_images, train_labels, batch_size=128), # flows是实时
steps_per_epoch=train_images.shape[0] // batch_size, # steps_per_epoch: 每个epoch所需的steps,不能少
epochs=200,
callbacks=[change_lr],
validation_data=test_datagen.flow(test_images, test_labels, batch_size=128),
#validation_data = (test_images, test_labels),
validation_steps=test_images.shape[0] // batch_size, # 这个也是不能少
#callbacks=[tensorboard]
)