unet.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 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv1)
  pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

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

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

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

  conv5 = Convolution2D(512, 3, 3, activation='relu', border_mode='same')(pool4)
  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 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(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 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(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 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(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 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv9)

  conv10 = Convolution2D(1, 1, 1, activation='sigmoid')(conv9)

  model = Model(input=inputs, output=conv10)

  model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])

  return model