Keras 损失为负数，准确率下降，但预测良好？

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。 损失正在下降（这很好），但在负面的方向和准确性正在下降。但好的部分是，模型的预测正在改进。我担心的是为什么损失是负数。还有，为什么模型在进步，而准确率却在下降？？

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

预测

目标

问题出在哪里？ （将 dice_coeff 保留为自定义损失）

Answer 1

您的输出未针对二元分类进行标准化。 （数据也可能未标准化）。

如果你加载了一张图片，它可能是 0 到 255，甚至是 0 到 65355。

您应该规范化y_train（除以y_train.max()）并在模型末尾使用'sigmoid' 激活函数。