这是我正在使用的代码（主要从Kaggle提取）：
inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
s = Lambda(lambda x: x / 255) (inputs)
c1 = Conv2D(8, (3, 3), activation='relu', padding='same') (s)
c1 = Conv2D(8, (3, 3), activation='relu', padding='same') (c1)
p1 = MaxPooling2D((2, 2)) (c1)
c2 = Conv2D(16, (3, 3), activation='relu', padding='same') (p1)
c2 = Conv2D(16, (3, 3), activation='relu', padding='same') (c2)
p2 = MaxPooling2D((2, 2)) (c2)
c3 = Conv2D(32, (3, 3), activation='relu', padding='same') (p2)
c3 = Conv2D(32, (3, 3), activation='relu', padding='same') (c3)
p3 = MaxPooling2D((2, 2)) (c3)
c4 = Conv2D(64, (3, 3), activation='relu', padding='same') (p3)
c4 = Conv2D(64, (3, 3), activation='relu', padding='same') (c4)
p4 = MaxPooling2D(pool_size=(2, 2)) (c4)
c5 = Conv2D(128, (3, 3), activation='relu', padding='same') (p4)
c5 = Conv2D(128, (3, 3), activation='relu', padding='same') (c5)
u6 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (c5)
u6 = concatenate([u6, c4])
c6 = Conv2D(64, (3, 3), activation='relu', padding='same') (u6)
c6 = Conv2D(64, (3, 3), activation='relu', padding='same') (c6)
u7 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c6)
u7 = concatenate([u7, c3])
c7 = Conv2D(32, (3, 3), activation='relu', padding='same') (u7)
c7 = Conv2D(32, (3, 3), activation='relu', padding='same') (c7)
u8 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c7)
u8 = concatenate([u8, c2])
c8 = Conv2D(16, (3, 3), activation='relu', padding='same') (u8)
c8 = Conv2D(16, (3, 3), activation='relu', padding='same') (c8)
u9 = Conv2DTranspose(8, (2, 2), strides=(2, 2), padding='same') (c8)
u9 = concatenate([u9, c1], axis=3)
c9 = Conv2D(8, (3, 3), activation='relu', padding='same') (u9)
c9 = Conv2D(8, (3, 3), activation='relu', padding='same') (c9)
outputs = Conv2D(4, (1, 1), activation='sigmoid') (c9)
model = Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer='adam', loss='dice', metrics=[mean_iou])
results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=8, epochs=30, class_weight=class_weights)
我有4个班级非常不平衡。 A级等于70％，B级= 15％，C级= 10％，D级= 5％。但是，我最关心D类。因此，我进行了以下类型的计算：B和A类的权重为D_weight = A/D = 70/5 = 14，依此类推。（如果有更好的方法来选择这些权重，则可以随意使用）
在最后一行中，我正在尝试正确设置class_weights，我正在这样做：class_weights = {0: 1.0, 1: 6, 2: 7, 3: 14}。
但是，当我这样做时，出现以下错误。
class_weight不适用于3维以上的目标。
是否有可能在最后一层之后添加一个密集层并将其用作虚拟层，以便我可以传递class_weights，然后仅使用最后一个conv2d层的输出进行预测？
如果这不可能，那么我将如何修改损失函数（我知道这个post，但是，仅将权重传递给损失函数并不会削减它，因为损失每个类分别调用该函数）？目前，我正在使用以下损失函数：
def dice_coef(y_true, y_pred):
smooth = 1.
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 + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def bce_dice_loss(y_true, y_pred):
return 0.5 * binary_crossentropy(y_true, y_pred) - dice_coef(y_true, y_pred)
但是我看不到任何可以输入班级权重的方法。如果有人需要完整的工作代码，请参阅此post。但是请记住将最终conv2d层的num类更改为4而不是1。
答案 0 :(得分：2)
您始终可以自己应用权重：
def weightedLoss(originalLossFunc, weightsList):
def lossFunc(true, pred):
axis = -1 #if channels last
#axis= 1 #if channels first
#argmax returns the index of the element with the greatest value
#done in the class axis, it returns the class index
classSelectors = K.argmax(true, axis=axis)
#considering weights are ordered by class, for each class
#true(1) if the class index is equal to the weight index
classSelectors = [K.equal(i, classSelectors) for i in range(len(weightsList))]
#casting boolean to float for calculations
#each tensor in the list contains 1 where ground true class is equal to its index
#if you sum all these, you will get a tensor full of ones.
classSelectors = [K.cast(x, K.floatx()) for x in classSelectors]
#for each of the selections above, multiply their respective weight
weights = [sel * w for sel,w in zip(classSelectors, weightsList)]
#sums all the selections
#result is a tensor with the respective weight for each element in predictions
weightMultiplier = weights[0]
for i in range(1, len(weights)):
weightMultiplier = weightMultiplier + weights[i]
#make sure your originalLossFunc only collapses the class axis
#you need the other axes intact to multiply the weights tensor
loss = originalLossFunc(true,pred)
loss = loss * weightMultiplier
return loss
return lossFunc
您也可以更改输入样本的余额。
例如，如果您有1类的5个样本和2类的10个样本，请在输入数组中两次传递5类的样本。
。
sample_weight参数。您也可以“按样本”工作，而不是“按班”工作。
为输入数组中的每个样本创建权重数组：len(x_train) == len(weights)
然后fit将此数组传递给sample_weight参数。
（如果为fit_generator，则生成器将必须返回权重以及火车/真实对：return/yield inputs, targets, weights）