"Tensor objects are only iterable when eager execution is enabled" error when trying to create custom loss function in Keras

Question

I am trying to do SVD using a neural network. My input is a matrix (let's say 4x4 matrices only) and the output is a vector representing the decomposed form (given that the input is 4x4 this would be a 36 element vector with 16 elements for U, 4 elements for S, and 16 elements for V.T).

I am trying to define a custom loss function instead of using something like MSE on the decomposed form. So instead of comparing the 36 length vectors for loss, I want to compute the loss between the reconstructed matrices. So if A = U * S * V.T (actual) and A' = U' * S' * V.T' (predicted), I want to compute the loss between A and A'.

I am pretty new to tensorflow and keras, so I may be doing some naive things, but here is what I have so far. While the logic seems okay to me, I get a TypeError: Tensor objects are only iterable when eager execution is enabled. To iterate over this tensor use tf.map_fn. I am not sure why this is the case and how to fix it? Also, do I need to flatten the output from the reconstruct_matrix, as I am currently doing, or should I just leave it as is?

# This function takes the decomposed matrix (vector of U, S, V.T)
# and reconstructs the original matrix

def reconstruct_matrix(decomposed_vector):
  example = decomposed_vector
  s = np.zeros((4,4))
  for en, i in enumerate(example[16:20]):
    s[en, en] = i
  u = example[:16].reshape(4,4)
  vt = example[20:].reshape(4,4)
  orig = np.matmul(u, s)
  orig = np.matmul(orig, vt)
  return orig.flatten() # Given that matrices are 4x4, this will be a length 16 vector

# Custom loss that essentially computes MSE on reconstructed matrices 

def custom_loss(y_true, y_pred):
    Y = reconstruct_matrix(y_true)
    Y_prime = reconstruct_matrix(y_pred)
    return K.mean(K.square(Y - Y_prime)) 

model.compile(optimizer='adam',
              loss=custom_loss)

Note: My keras version is 2.2.4 and my tensorflow version is 1.14.0.

Tensorflow version? Always specify it when posting a question as this framework is changing paradigm even with minor releases. — Szymon Maszke
Great point! My keras version is 2.2.4 and my tensorflow version is 1.14.0. — Jane Sully

Szymon Maszke Szymon Maszke · Accepted Answer · 2019-08-11T21:23:05

In tf1.x eager execution is disabled by default (it's on for version 2 onwards).

You have to enable it by calling at the top of your script:

import tensorflow as tf

tf.enable_eager_execution()

This mode allows you to use Python-like abstractions for flow control (e.g. if statements and for loop you've been using in your code). If it's disabled you need to use Tensorflow functions (tf.cond and tf.while_loop for if and for respectively).

More information about it in the docs.

BTW. I'm not sure about flatten, but remember your y_true and y_pred need the same shape and samples have to be respective to each other, if that's fulfilled you should be fine.

"Tensor objects are only iterable when eager execution is enabled" error when trying to create custom loss function in Keras

1 Answers