models.py 文件源码

def build_model(self):
        from resnet50 import identity_block, conv_block
        from keras.layers import Input
        from keras import layers
        from keras.layers import Dense
        from keras.layers import Activation
        from keras.layers import Flatten
        from keras.layers import Conv2D
        from keras.layers import MaxPooling2D
        from keras.layers import GlobalMaxPooling2D
        from keras.layers import ZeroPadding2D
        from keras.layers import AveragePooling2D
        from keras.layers import GlobalAveragePooling2D
        from keras.layers import BatchNormalization
        from keras.models import Model
        from keras.preprocessing import image
        import keras.backend as K
        from keras.utils import layer_utils

        x = ZeroPadding2D((3, 3))(input_shape=self.X[0].shape)
        x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
        x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
        x = Activation('relu')(x)
        x = MaxPooling2D((3, 3), strides=(2, 2))(x)

        x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
        x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
        x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')

        x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
        x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
        x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
        x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')

        x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
        x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')

        x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
        x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
        x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')

        x = AveragePooling2D((7, 7), name='avg_pool')(x)
        x = Flatten()(x)
        x = Dense(2, activation='softmax', name = 'fc1000')(x)

        self.model = Model(inputs, x, name='resnet50')