问题
我正在进行两类图像分割,并希望使用Dice系数的损失函数。但是验证损失没有改善。如何解决这些问题?
操作步骤
使用One-hot编码方法,处理标签图像并不包括背景标签。
代码
X的形状为(数据数量,256,256,1)#灰度
y的形状为(数据数量,256,256,2)#两类别且不包括背景标签
one_hot_y = np.zeros((len(y), image_height, image_width, 2))
for i in range(len(y)):
one_hot = to_categorical(y[i])
one_hot_y[i] = one_hot[:,:,1:]
one_hot_y.shape #-> (566, 256, 256, 2)
#### <-- Unet Model --> ####
from tensorflow import keras
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Concatenate, Conv2DTranspose
from keras import Model
def unet(image_height, image_width, num_classes):
# inputs = Input(input_size)
inputs = Input(shape=(image_height, image_width, 1),name='U-net')
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
up6 = Concatenate()([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4])
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)
up7 = Concatenate()([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3])
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)
up8 = Concatenate()([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2])
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)
up9 = Concatenate()([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1])
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)
outputs = Conv2D(num_classes, (1, 1), activation='softmax')(conv9)
return Model(inputs=[inputs], outputs=[outputs])```
#### <-- Dice Score --> ####
from tensorflow.keras import backend as K
def dice_coef(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + 0.0001) / (K.sum(y_true_f) + K.sum(y_pred_f) + 0.0001)
def dice_coef_loss(y_true, y_pred):
return 1 - dice_coef(y_true, y_pred)```
#### <-- Fit the Model --> ####
from tensorflow.keras import optimizers
adam = optimizers.Adam(learning_rate=0.0001)
unet_model.compile(optimizer=adam, loss=[dice_coef_loss],metrics=[dice_coef])
hist = unet_model.fit(X_train,y_train, epochs=epochs, batch_size=batch_size,validation_data=(X_val,y_val), callbacks=[checkpoint,earlystopping])