使用类权重的喀拉拉邦U-net自定义损失函数:3维以上目标不支持`class_weight` [英] Custom loss function for U-net in keras using class weights: `class_weight` not supported for 3+ dimensional targets
问题描述
这是我正在使用的代码(主要是从Kaggle提取):
inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
...
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类和A类的权重的计算方法为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，那就放心了)
在最后一行中，我试图正确设置class_weights，并且这样做: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.
3维以上目标不支持
class_weight.
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，我将如何修改损失函数(我知道此帖子，但是，仅将权重传递给损失函数就不会削减它，因为损失函数是针对每个类分别调用的)?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.如果有人想要完整的工作代码，请参见此帖子.但是请记住将最终conv2d层的num类更改为4而不是1.
您始终可以自己应用权重.
您可以从keras.losses导入下面的originalLossFunc.
weightsList是按类别排序的权重列表.
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)
#if your loss is sparse, use only true as classSelectors
#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
要在compile中使用它:
model.compile(loss= weightedLoss(keras.losses.categorical_crossentropy, weights),
optimizer=..., ...)
直接在输入数据上更改班级余额
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.
.
使用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)
Here's the code I'm working with (pulled from Kaggle mostly):
inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
...
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)
I have 4 classes that are very imbalanced. Class A equals 70%, class B = 15%, class C = 10%, and class D = 5%. However, I care most about class D. So I did the following type of calculations: D_weight = A/D = 70/5 = 14 and so on for the weight for class B and A. (if there are better methods to select these weights, then feel free)
In the last line, I'm trying to properly set class_weights and I'm doing it as so: class_weights = {0: 1.0, 1: 6, 2: 7, 3: 14}.
However, when I do this, I get the following error.
class_weightnot supported for 3+ dimensional targets.
Is it possible that I add a dense layer after the last layer and just use it as a dummy layer so I can pass the class_weights and then only use the output of the last conv2d layer to do the prediction?
If this is not possible, how would I modify the loss function (I'm aware of this post, however, just passing in the weights in to the loss function won't cut it, because the loss function is called separately for each class) ? Currently, I'm using the following loss function:
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)
But I don't see any way in which I can input class weights. If someone wants the full working code see this post. But remember to change the final conv2d layer's num classes to 4 instead of 1.
You can always apply the weights yourself.
The originalLossFunc below you can import from keras.losses.
The weightsList is your list with the weights ordered by class.
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)
#if your loss is sparse, use only true as classSelectors
#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
For using this in compile:
model.compile(loss= weightedLoss(keras.losses.categorical_crossentropy, weights),
optimizer=..., ...)
Changing the class balance directly on the input data
You can change the balance of the input samples too.
For instance, if you have 5 samples from class 1 and 10 samples from class 2, pass the samples for class 5 twice in the input arrays.
.
Using the sample_weight argument.
Instead of working "by class", you can also work "by sample".
Create an array of weights for each sample in your input array: len(x_train) == len(weights)
And fit passing this array to the sample_weight argument.
(If it's fit_generator, the generator will have to return the weights along with the train/true pairs: return/yield inputs, targets, weights)
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