[theano-users] Applying condition in mean squared error cost function using Theano

[Feras Almasri]

I'm building a convolution neural network and I'm using mean squared error
as a cost function. I'm changing the cost function to not have error when
the network output is over one so I'm thresholding the out to one when it
is bigger. using this code

def MSE2(self, y):

loc = np.where(y == 1)[0]
for i in range(len(loc)):
if self.input2[loc[i]] > 1:
self.input2[loc[i]] = 1

return T.mean((y - self.input2) ** 2)

I'd like to know if theano gradient function will take this into account
when it calculate the gradient or I should change something else.

Beside this, Is there any other way I can optimize this code to run faster
or maybe on the GPU.

[Jesse Livezey]

You can use T.switch

y_hat_clipped = T.switch(self.input2 > 1, 1, self.input2)
return T.mean((y - y_hat_clipped) ** 2)

Gradients will be calculated correctly. In this case, the gradient with
respect to self.input2 will be zero if it is greater than 1.

Post by Feras Almasri
I'm building a convolution neural network and I'm using mean squared error
as a cost function. I'm changing the cost function to not have error when
the network output is over one so I'm thresholding the out to one when it
is bigger. using this code
loc = np.where(y == 1)[0]
self.input2[loc[i]] = 1
return T.mean((y - self.input2) ** 2)
I'd like to know if theano gradient function will take this into account
when it calculate the gradient or I should change something else.
Beside this, Is there any other way I can optimize this code to run faster
or maybe on the GPU.