F.binary_cross_entropy() input size should be between 0 and 1

Hi everyone,
I’ve tried to set up an vae for audio files, but I get the error input size should be between 0 and 1 when using the binary_cross_entropy() function, even though I didn’t have any of these issues when testing with MNIST Image files.
I suspect the error to result from the following function:

def reparameterize(self, mu, logvar):
    if self.training:
        std = logvar.mul(0.5).exp_()  
        eps = Variable(std.data.new(std.size()).normal_())
        return eps.mul(std).add_(mu)

because that’s where any of my data starts to be out of the 0 to 1 range in the whole code. the processing continues as follows:

*def forward(self, x, sampleCount):*
  •    x = x.view(1,1,sampleCount).float()                                         #Reshape the audio data to fit Conv*
  •    mu, logvar = self.encode(x)*
      z = self.reparameterize(mu, logvar)
      return z, mu, logvar

next is the loss function, which causes the error:

BCE = F.binary_cross_entropy(z.view(-1, sampleCount), x.view(-1, sampleCount).float())

can anyone please help me?
I’ve already tried using binary_cross_entropy_with_logits which didn’t crash my code, but yield any useable results.


I think the error is pointing out that the span of x is not between 0 and 1.
Because, target needs to be either 0 or 1, in your case, the variable x in F.binary_crossentropy().
Could you check what’s the min and max value of x before you pass it to the loss function ?
And can you paste the error ?

Thanks for the reply! I’m sorry I didn’t paste the error, I forgot it… my bad.

The torch x before passing it through the loss function: [0.5729, 0.5729, 0.5729, …, 0.5160, 0.5266, 0.5555] (min 0, max 1)

The error:
all elements of input should be between 0 and 1

However, z (input for binary_cross_entropy) looks like that: [-0.9888, 0.1598, 1.3676, …, 1.4632, -2.4632, 2.5672] (min -4.68, max 5.9)
which is causing the error.

If I understood you correctly, both target and input should be either 0 and 1, which means that my data is not correctly transformed for that function? If so, is there a better loss function to use, since audio data is pretty hard to transform to only 0 and 1 while keeping the data loss to a minimum? That would also explain why it worked well on the mnist data, because that’s easy to transform to only 0 and 1 I guess.

Hello Hannes!

Just to clarify, binary_cross_entropy() does not require the input
and target values to be either 0 or 1. Rather, it requires these
values to be probabilities, that is, values in the range [0.0, 1.0]

In practice, it is often the case that the target values are 0 or 1
(and can be understood as binary class labels), but forcing your
input values to be 0 or 1 will break differentiablity, and hence
break back-propagation.

Two more general questions: Why are you reparameterizing your
data? Conceptually, why are you using binary_cross_entropy(),
and how is this supposed to interact with your reparameterization?


K. Frank

I fixed my issue, I had a function which imported the files and scaled the data to the range 0-1 which was not behaving properly. Everything is working now. Thanks to everybody helping, even if it didn’t solve my issue (because I didn’t upload enough of the code) it still helped me a understanding how pytorch works.