a02_zf_unet_model.py 文件源码

def ZF_UNET_224(dropout_val=0.05, batch_norm=True):
    from keras.models import Model
    from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D
    from keras.layers.normalization import BatchNormalization
    from keras.layers.core import Dropout, Activation
    inputs = Input((3, 224, 224))
    conv1 = double_conv_layer(inputs, 32, dropout_val, batch_norm)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = double_conv_layer(pool1, 64, dropout_val, batch_norm)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = double_conv_layer(pool2, 128, dropout_val, batch_norm)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = double_conv_layer(pool3, 256, dropout_val, batch_norm)
    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)

    conv5 = double_conv_layer(pool4, 512, dropout_val, batch_norm)
    pool5 = MaxPooling2D(pool_size=(2, 2))(conv5)

    conv6 = double_conv_layer(pool5, 1024, dropout_val, batch_norm)

    up6 = merge([UpSampling2D(size=(2, 2))(conv6), conv5], mode='concat', concat_axis=1)
    conv7 = double_conv_layer(up6, 512, dropout_val, batch_norm)

    up7 = merge([UpSampling2D(size=(2, 2))(conv7), conv4], mode='concat', concat_axis=1)
    conv8 = double_conv_layer(up7, 256, dropout_val, batch_norm)

    up8 = merge([UpSampling2D(size=(2, 2))(conv8), conv3], mode='concat', concat_axis=1)
    conv9 = double_conv_layer(up8, 128, dropout_val, batch_norm)

    up9 = merge([UpSampling2D(size=(2, 2))(conv9), conv2], mode='concat', concat_axis=1)
    conv10 = double_conv_layer(up9, 64, dropout_val, batch_norm)

    up10 = merge([UpSampling2D(size=(2, 2))(conv10), conv1], mode='concat', concat_axis=1)
    conv11 = double_conv_layer(up10, 32, 0, batch_norm)

    conv12 = Convolution2D(1, 1, 1)(conv11)
    conv12 = BatchNormalization(mode=0, axis=1)(conv12)
    conv12 = Activation('sigmoid')(conv12)

    model = Model(input=inputs, output=conv12)
    return model