Reshape TensorFlow Tensor Inside Keras Loss Function?

Is there a way to reshape a TF tensor inside of a custom Keras loss function? I'm defining this custom loss function for a convolutional neural network? def custom_loss(x, x_hat):

Solution 1:

Is there a proper way to reshape tensors...

If you are using Keras you should use the K.reshape(x,shape) method, which is a wrapper for tf.reshape(x,shape) as we can see in the docs.

I also notice you are using get_shape() to obtain your tensor shape, when on Keras you can do this with K.int_shape(x) as also mentioned in the docs, like this:

shape = K.int_shape(x_hat)

Besides that there are several other operations you do directly calling your Tensorflow import, instead of the Keras Backend (like tf.abs(), tf.reduce_mean(), tf.transpose(), etc.). You should consider using its corresponding wrappers in the keras backend to have uniform notation and guarantee a more regular behaviour. Also, by using the Keras backend you are giving your program compatibility with both Theano and Tensorflow, so it is a big plus you should consider.

Additionally, some TypeError may appear when working with tensors with undefined dimension(s). Please take a look at this question where they explain about reshaping tensors with undefined dimensions. Also, for its equivalent in Keras, check this other question, where in an answer I explain how to achieve that using Keras with Tensorflow as backend.

...Now regarding your code. Basically, as you have some undefined dimensions, you can pass the value -1 to have it infer the shape no matter what size it could be (it is explained in the first linked question, but can also be seen in the docs). Something like:

x = tf.reshape(x, [-1, image_size])

Or using Keras backend:

x = K.reshape(x, [-1, image_size])