Keras损失为负数，准确性下降，但预测是否良好？

Question

我正在使用Tensorflow-gpu后端在Keras中训练模型。 任务是检测卫星图像中的建筑物。 损耗在下降（这很好），但在负方向上，准确性下降。但好的部分是，模型的预测正在改善。我担心的是为什么损失为负数。而且，为什么模型在精度下降的同时却在改进？？

from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import MaxPool2D as MaxPooling2D
from tensorflow.keras.layers import UpSampling2D
from tensorflow.keras.layers import concatenate
from tensorflow.keras.layers import Input
from tensorflow.keras import Model
from tensorflow.keras.optimizers import RMSprop


# LAYERS
inputs = Input(shape=(300, 300, 3))
# 300

down0 = Conv2D(32, (3, 3), padding='same')(inputs)
down0 = BatchNormalization()(down0)
down0 = Activation('relu')(down0)
down0 = Conv2D(32, (3, 3), padding='same')(down0)
down0 = BatchNormalization()(down0)
down0 = Activation('relu')(down0)
down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
# 150

down1 = Conv2D(64, (3, 3), padding='same')(down0_pool)
down1 = BatchNormalization()(down1)
down1 = Activation('relu')(down1)
down1 = Conv2D(64, (3, 3), padding='same')(down1)
down1 = BatchNormalization()(down1)
down1 = Activation('relu')(down1)
down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
# 75

center = Conv2D(1024, (3, 3), padding='same')(down1_pool)
center = BatchNormalization()(center)
center = Activation('relu')(center)  
center = Conv2D(1024, (3, 3), padding='same')(center)
center = BatchNormalization()(center)
center = Activation('relu')(center)
# center

up1 = UpSampling2D((2, 2))(center)
up1 = concatenate([down1, up1], axis=3)
up1 = Conv2D(64, (3, 3), padding='same')(up1)
up1 = BatchNormalization()(up1)
up1 = Activation('relu')(up1)
up1 = Conv2D(64, (3, 3), padding='same')(up1)
up1 = BatchNormalization()(up1)
up1 = Activation('relu')(up1)
up1 = Conv2D(64, (3, 3), padding='same')(up1)
up1 = BatchNormalization()(up1)
up1 = Activation('relu')(up1)
# 150

up0 = UpSampling2D((2, 2))(up1)
up0 = concatenate([down0, up0], axis=3)
up0 = Conv2D(32, (3, 3), padding='same')(up0)
up0 = BatchNormalization()(up0)
up0 = Activation('relu')(up0)
up0 = Conv2D(32, (3, 3), padding='same')(up0)
up0 = BatchNormalization()(up0)
up0 = Activation('relu')(up0) 
up0 = Conv2D(32, (3, 3), padding='same')(up0)
up0 = BatchNormalization()(up0)
up0 = Activation('relu')(up0)
# 300x300x3
classify = Conv2D(1, (1, 1), activation='sigmoid')(up0)
# 300x300x1

model = Model(inputs=inputs, outputs=classify)

model.compile(optimizer=RMSprop(lr=0.0001), 
              loss='binary_crossentropy', 
              metrics=[dice_coeff, 'accuracy'])

history = model.fit(sample_input, sample_target, batch_size=4, epochs=5)



OUTPUT:

Epoch 6/10
500/500 [==============================] - 76s 153ms/step - loss: -293.6920 - 
dice_coeff: 1.8607 - acc: 0.2653
Epoch 7/10
500/500 [==============================] - 75s 150ms/step - loss: -309.2504 - 
dice_coeff: 1.8730 - acc: 0.2618
Epoch 8/10
500/500 [==============================] - 75s 150ms/step - loss: -324.4123 - 
dice_coeff: 1.8810 - acc: 0.2659
Epoch 9/10
136/500 [=======>......................] - ETA: 55s - loss: -329.0757 - dice_coeff: 1.8940 - acc: 0.2757

已预测

目标

问题出在哪里？ （让dice_coeff成为自定义损失）

Answer 1

您的输出未针对二进制分类进行标准化。 （数据也可能未规范化）。

如果加载图像，则可能是0到255，甚至0到65355。

您应该归一化y_train（除以y_train.max()）并在模型结尾使用'sigmoid'激活函数。