deep_learning_2DUnet.py 文件源码

def get_unet():
    inputs = Input((1, img_rows, img_cols))
    conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(inputs)
    conv1 = Dropout(0.2)(conv1)
    conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    pool1 = BatchNormalization()(pool1)

    conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(pool1)
    conv2 = Dropout(0.2)(conv2)
    conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    pool2 = BatchNormalization()(pool2)

    conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(pool2)
    conv3 = Dropout(0.2)(conv3)
    conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    pool3 = BatchNormalization()(pool3)

    conv4 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(pool3)
    conv4 = Dropout(0.2)(conv4)
    conv4 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
    pool4 = BatchNormalization()(pool4)

    conv5 = Convolution2D(512, 3, 3, activation='relu', border_mode='same')(pool4)
    conv5 = Dropout(0.2)(conv5)
    conv5 = Convolution2D(512, 3, 3, activation='relu', border_mode='same')(conv5)

    up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1)
    conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(up6)
    conv6 = Dropout(0.2)(conv6)
    conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(conv6)
    conv6 = BatchNormalization()(conv6)

    up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=1)
    conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(up7)
    conv7 = Dropout(0.2)(conv7)
    conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv7)
    conv7 = BatchNormalization()(conv7)

    up8 = merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=1)
    conv8 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(up8)
    conv8 = Dropout(0.2)(conv8)
    conv8 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv8)
    conv8 = BatchNormalization()(conv8)

    up9 = merge([UpSampling2D(size=(2, 2))(conv8), conv1], mode='concat', concat_axis=1)
    conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(up9)
    conv9 = Dropout(0.2)(conv9)
    conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv9)
    conv9 = BatchNormalization()(conv9)

    conv10 = Convolution2D(1, 1, 1, activation='sigmoid')(conv9)
    model = Model(input=inputs, output=conv10)

    model.compile(optimizer=Adam(lr=1.0e-4), loss=dice_coef_loss, metrics=[dice_coef])

    return model