How to implement Weighted Binary CrossEntropy on theano?
My Convolutional neural network only predict 0 ~~ 1 (sigmoid).
I want to penalize my predictions in this way :
Basically, i want to penalize MORE when the model predicts 0 but the truth was 1.
Question : How can I create this Weighted Binary CrossEntropy function using theano and lasagne ?
I tried this below
prediction = lasagne.layers.get_output(model)
import theano.tensor as T
def weighted_crossentropy(predictions, targets):
# Copy the tensor
tgt = targets.copy("tgt")
# Make it a vector
# tgt = tgt.flatten()
# tgt = tgt.reshape(3000)
# tgt = tgt.dimshuffle(1,0)
newshape = (T.shape(tgt)[0])
tgt = T.reshape(tgt, newshape)
#Process it so [index] < 0.5 = 0 , and [index] >= 0.5 = 1
# Make it an integer.
tgt = T.cast(tgt, 'int32')
weights_per_label = theano.shared(lasagne.utils.floatX([0.2, 0.4]))
weights = weights_per_label[tgt] # returns a targets-shaped weight matrix
loss = lasagne.objectives.aggregate(T.nnet.binary_crossentropy(predictions, tgt), weights=weights)
return loss
loss_or_grads = weighted_crossentropy(prediction, self.target_var)
But I get this error below :
TypeError: New shape in reshape must be a vector or a list/tuple of scalar. Got Subtensor{int64}.0 after conversion to a vector.
Reference : https://github.com/fchollet/keras/issues/2115
Reference : https://groups.google.com/forum/#!topic/theano-users/R_Q4uG9BXp8
Thanks to the developers on lasagne group, i fixed this by constructing my own loss function.
loss_or_grads = -(customized_rate * target_var * tensor.log(prediction) + (1.0 - target_var) * tensor.log(1.0 - prediction))
loss_or_grads = loss_or_grads.mean()
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