Tensor objects are not iterable when eager execution is not enabled. To iterate over this tensor use tf.map_fn

Question

I am trying to create my own loss function:

def custom_mse(y_true, y_pred):
    tmp = 10000000000
    a = list(itertools.permutations(y_pred))
    for i in range(0, len(a)): 
     t = K.mean(K.square(a[i] - y_true), axis=-1)
     if t < tmp :
        tmp = t
     return tmp

It should create permutations of predicted vector, and return the smallest loss.

   "Tensor objects are not iterable when eager execution is not "
TypeError: Tensor objects are not iterable when eager execution is not enabled. To iterate over this tensor use tf.map_fn.

error. I fail to find any source for this error. Why is this happening?

Answer 1

The error is happening because y_pred is a tensor (non iterable without eager execution), and itertools.permutations expects an iterable to create the permutations from. In addition, the part where you compute the minimum loss would not work either, because the values of tensor t are unknown at graph creation time.

Instead of permuting the tensor, I would create permutations of the indices (this is something you can do at graph creation time), and then gather the permuted indices from the tensor. Assuming that your Keras backend is TensorFlow and that y_true/y_pred are 2-dimensional, your loss function could be implemented as follows:

def custom_mse(y_true, y_pred):
    batch_size, n_elems = y_pred.get_shape()
    idxs = list(itertools.permutations(range(n_elems)))
    permutations = tf.gather(y_pred, idxs, axis=-1)  # Shape=(batch_size, n_permutations, n_elems)
    mse = K.square(permutations - y_true[:, None, :])  # Shape=(batch_size, n_permutations, n_elems)
    mean_mse = K.mean(mse, axis=-1)  # Shape=(batch_size, n_permutations)
    min_mse = K.min(mean_mse, axis=-1)  # Shape=(batch_size,)
    return min_mse