Tensorflow学习笔记：CNN篇（5）——CIFAR-10数据集VGG19实现（Keras版）

Tensorflow学习笔记：CNN篇（5）——CIFAR-10数据集VGG19实现（Keras版）

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完整代码

import keras
import numpy as np
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, AveragePooling2D
from keras.initializers import he_normal
from keras import optimizers
from keras.callbacks import LearningRateScheduler, TensorBoard
from keras.layers.normalization import BatchNormalization
from keras.utils.data_utils import get_file
import matplotlib.pyplot as plt

num_classes = 10
batch_size = 128
epochs = 50
iterations = 391
dropout = 0.5
weight_decay = 0.0001
log_filepath = r'./vgg19_retrain_logs/'


def scheduler(epoch):
    if epoch < 80:
        return 0.1
    if epoch < 160:
        return 0.01
    return 0.001


# WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5'
# filepath = get_file('vgg19_weights_tf_dim_ordering_tf_kernels.h5', WEIGHTS_PATH, cache_subdir='models')


# data loading
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')

# data preprocessing
x_train[:, :, :, 0] = (x_train[:, :, :, 0]-123.680)
x_train[:, :, :, 1] = (x_train[:, :, :, 1]-116.779)
x_train[:, :, :, 2] = (x_train[:, :, :, 2]-103.939)
x_test[:, :, :, 0] = (x_test[:, :, :, 0]-123.680)
x_test[:, :, :, 1] = (x_test[:, :, :, 1]-116.779)
x_test[:, :, :, 2] = (x_test[:, :, :, 2]-103.939)

# build model
model = Sequential()

# Block 1
model.add(Conv2D(64, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block1_conv1', input_shape=x_train.shape[1:]))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block1_conv2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool'))

# Block 2
model.add(Conv2D(128, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block2_conv1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block2_conv2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool'))

# Block 3
model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block3_conv1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block3_conv2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block3_conv3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(256, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block3_conv4'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool'))

# Block 4
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block4_conv1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block4_conv2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block4_conv3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block4_conv4'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool'))

# Block 5
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block5_conv1'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block5_conv2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block5_conv3'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Conv2D(512, (3, 3), padding='same', kernel_regularizer=keras.regularizers.l2(weight_decay),
                 kernel_initializer=he_normal(), name='block5_conv4'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool'))

# model modification for cifar-10
model.add(Flatten(name='flatten'))
model.add(Dense(4096, use_bias=True, kernel_regularizer=keras.regularizers.l2(weight_decay),
                kernel_initializer=he_normal(), name='fc_cifa10'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dense(4096, kernel_regularizer=keras.regularizers.l2(weight_decay),
                kernel_initializer=he_normal(), name='fc2'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dense(10, kernel_regularizer=keras.regularizers.l2(weight_decay),
                kernel_initializer=he_normal(), name='predictions_cifa10'))
model.add(BatchNormalization())
model.add(Activation('softmax'))

# load pretrained weight from VGG19 by name
# model.load_weights(filepath, by_name=True)

# -------- optimizer setting -------- #
sgd = optimizers.SGD(lr=.1, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

tb_cb = TensorBoard(log_dir=log_filepath, histogram_freq=0)
change_lr = LearningRateScheduler(scheduler)
cbks = [change_lr, tb_cb]

print('Using real-time data augmentation.')
datagen = ImageDataGenerator(horizontal_flip=True,
                             width_shift_range=0.125, height_shift_range=0.125, fill_mode='constant', cval=0.)

datagen.fit(x_train)

history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
                              steps_per_epoch=iterations,
                              epochs=epochs,
                              callbacks=cbks,
                              validation_data=(x_test, y_test))
model.save('retrain.h5')

loss = history.history.get('loss')
acc = history.history.get('acc')
val_loss = history.history.get('val_loss')
val_acc = history.history.get('val_acc')

plt.figure(0)
plt.subplot(121)
plt.plot(range(len(loss)), loss, label="Training")
plt.plot(range(len(val_loss)), val_loss, label='Validation')
plt.title('Loss')
plt.legend(loc='upper left')
plt.subplot(122)
plt.plot(range(len(acc)), acc, label='Training')
plt.plot(range(len(val_acc)), val_acc, label='Validation')
plt.title('Accuracy')
plt.show()

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运行结果