ResNet Keras实现

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伪代码：

graph TD
A(导入相应库) --> Z[模型参数设置以及其它配置]
Z --> B[生成训练集,测试集,验证集的三个迭代器] 
B --> C[identity_block函数的编写]
C --> D[bottleneck_block函数的编写]
D --> F[根据resnet网络构建表来构建网络]
F --> G[模型训练与验证]
G --> H[模型保存]
H --> I(模型在测试集上测试)

代码：

from keras.models import Model
from keras.layers import Input, Dense, Dropout, BatchNormalization, Conv2D, MaxPooling2D, AveragePooling2D, concatenate, \
    Activation, ZeroPadding2D
from keras.layers import add, Flatten
from keras.utils import plot_model
from keras.metrics import top_k_categorical_accuracy
from keras.preprocessing.image import ImageDataGenerator
from keras.models import load_model
import os

# Global Constants
NB_CLASS=20
IM_WIDTH=224
IM_HEIGHT=224
train_root='your train dataset'
vaildation_root='your val dataset
test_root='your test dataset'
batch_size=32
EPOCH=60

# train data
train_datagen = ImageDataGenerator(
    width_shift_range=0.1,
    height_shift_range=0.1,
    shear_range=0.1,
    zoom_range=0.1,
    horizontal_flip=True,
    rescale=1./255
)
train_generator = train_datagen.flow_from_directory(
    train_root,
    target_size=(IM_WIDTH, IM_HEIGHT),
    batch_size=batch_size,
    shuffle=True
)

# vaild data
vaild_datagen = ImageDataGenerator(
    width_shift_range=0.1,
    height_shift_range=0.1,
    shear_range=0.1,
    zoom_range=0.1,
    horizontal_flip=True,
    rescale=1./255
)
vaild_generator = train_datagen.flow_from_directory(
    vaildation_root,
    target_size=(IM_WIDTH, IM_HEIGHT),
    batch_size=batch_size,
)

# test data
test_datagen = ImageDataGenerator(
    rescale=1./255
)
test_generator = train_datagen.flow_from_directory(
    test_root,
    target_size=(IM_WIDTH, IM_HEIGHT),
    batch_size=batch_size,
)

def Conv2d_BN(x, nb_filter, kernel_size, strides=(1, 1), padding='same', name=None):
    if name is not None:
        bn_name = name + '_bn'
        conv_name = name + '_conv'
    else:
        bn_name = None
        conv_name = None

    x = Conv2D(nb_filter, kernel_size, padding=padding, strides=strides, activation='relu', name=conv_name)(x)
    x = BatchNormalization(axis=3, name=bn_name)(x)
    return x


def identity_Block(inpt, nb_filter, kernel_size, strides=(1, 1), with_conv_shortcut=False):
    x = Conv2d_BN(inpt, nb_filter=nb_filter, kernel_size=kernel_size, strides=strides, padding='same')
    x = Conv2d_BN(x, nb_filter=nb_filter, kernel_size=kernel_size, padding='same')
    if with_conv_shortcut:
        shortcut = Conv2d_BN(inpt, nb_filter=nb_filter, strides=strides, kernel_size=kernel_size)
        x = add([x, shortcut])
        return x
    else:
        x = add([x, inpt])
        return x

def bottleneck_Block(inpt,nb_filters,strides=(1,1),with_conv_shortcut=False):
    k1,k2,k3=nb_filters
    x = Conv2d_BN(inpt, nb_filter=k1, kernel_size=1, strides=strides, padding='same')
    x = Conv2d_BN(x, nb_filter=k2, kernel_size=3, padding='same')
    x = Conv2d_BN(x, nb_filter=k3, kernel_size=1, padding='same')
    if with_conv_shortcut:
        shortcut = Conv2d_BN(inpt, nb_filter=k3, strides=strides, kernel_size=1)
        x = add([x, shortcut])
        return x
    else:
        x = add([x, inpt])
        return x

def resnet_34(width,height,channel,classes):
    inpt = Input(shape=(width, height, channel))
    x = ZeroPadding2D((3, 3))(inpt)

    #conv1
    x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='valid')
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

    #conv2_x
    x = identity_Block(x, nb_filter=64, kernel_size=(3, 3))
    x = identity_Block(x, nb_filter=64, kernel_size=(3, 3))
    x = identity_Block(x, nb_filter=64, kernel_size=(3, 3))

    #conv3_x
    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3), strides=(2, 2), with_conv_shortcut=True)
    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3))
    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3))
    x = identity_Block(x, nb_filter=128, kernel_size=(3, 3))

    #conv4_x
    x = identity_Block(x, nb_filter=256, kernel_size=(3, 3), strides=(2, 2), with_conv_shortcut=True)
    x = identity_Block(x, nb_filter=256, kernel_size=(3, 3))
    x = identity_Block(x, nb_filter=256, kernel_size=(3, 3))
    x = identity_Block(x, nb_filter=256, kernel_size=(3, 3))
    x = identity_Block(x, nb_filter=256, kernel_size=(3, 3))
    x = identity_Block(x, nb_filter=256, kernel_size=(3, 3))

    #conv5_x
    x = identity_Block(x, nb_filter=512, kernel_size=(3, 3), strides=(2, 2), with_conv_shortcut=True)
    x = identity_Block(x, nb_filter=512, kernel_size=(3, 3))
    x = identity_Block(x, nb_filter=512, kernel_size=(3, 3))
    x = AveragePooling2D(pool_size=(7, 7))(x)
    x = Flatten()(x)
    x = Dense(classes, activation='softmax')(x)

    model = Model(inputs=inpt, outputs=x)
    return model

def resnet_50(width,height,channel,classes):
    inpt = Input(shape=(width, height, channel))
    x = ZeroPadding2D((3, 3))(inpt)
    x = Conv2d_BN(x, nb_filter=64, kernel_size=(7, 7), strides=(2, 2), padding='valid')
    x = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same')(x)

    #conv2_x
    x = bottleneck_Block(x, nb_filters=[64,64,256],strides=(1,1),with_conv_shortcut=True)
    x = bottleneck_Block(x, nb_filters=[64,64,256])
    x = bottleneck_Block(x, nb_filters=[64,64,256])

    #conv3_x
    x = bottleneck_Block(x, nb_filters=[128, 128, 512],strides=(2,2),with_conv_shortcut=True)
    x = bottleneck_Block(x, nb_filters=[128, 128, 512])
    x = bottleneck_Block(x, nb_filters=[128, 128, 512])
    x = bottleneck_Block(x, nb_filters=[128, 128, 512])

    #conv4_x
    x = bottleneck_Block(x, nb_filters=[256, 256, 1024],strides=(2,2),with_conv_shortcut=True)
    x = bottleneck_Block(x, nb_filters=[256, 256, 1024])
    x = bottleneck_Block(x, nb_filters=[256, 256, 1024])
    x = bottleneck_Block(x, nb_filters=[256, 256, 1024])
    x = bottleneck_Block(x, nb_filters=[256, 256, 1024])
    x = bottleneck_Block(x, nb_filters=[256, 256, 1024])

    #conv5_x
    x = bottleneck_Block(x, nb_filters=[512, 512, 2048], strides=(2, 2), with_conv_shortcut=True)
    x = bottleneck_Block(x, nb_filters=[512, 512, 2048])
    x = bottleneck_Block(x, nb_filters=[512, 512, 2048])

    x = AveragePooling2D(pool_size=(7, 7))(x)
    x = Flatten()(x)
    x = Dense(classes, activation='softmax')(x)

    model = Model(inputs=inpt, outputs=x)
    return model

def acc_top2(y_true, y_pred):
    return top_k_categorical_accuracy(y_true, y_pred, k=2)


def check_print():
    # Create a Keras Model
    model = resnet_50(IM_WIDTH,IM_HEIGHT,3,NB_CLASS)
    model.summary()
    # Save a PNG of the Model Build
    plot_model(model, to_file='resnet.png')
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc',top_k_categorical_accuracy])
    print 'Model Compiled'
    return model


if __name__ == '__main__':
    if os.path.exists('resnet_50.h5'):
        model=load_model('resnet_50.h5')
    else:
        model=check_print()

    model.fit_generator(train_generator,validation_data=vaild_generator,epochs=EPOCH,steps_per_epoch=train_generator.n/batch_size
                        ,validation_steps=vaild_generator.n/batch_size)
    model.save('resnet_50.h5')
    loss,acc,top_acc=model.evaluate_generator(test_generator, steps=test_generator.n / batch_size)
    print 'Test result:loss:%f,acc:%f,top_acc:%f' % (loss, acc, top_acc)