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When I use log_likelihood criticism method on the bayesian regression example, it fails with an error (message shown below):

Program:

....

  # CRITICISM

  # Plot posterior samples.
  sns.jointplot(qb.params.eval()[FLAGS.nburn:FLAGS.T:FLAGS.stride],
                qw.params.eval()[FLAGS.nburn:FLAGS.T:FLAGS.stride])
  plt.show()

  # Posterior predictive checks.
  y_post = ed.copy(y, {w: qw, b: qb})

 ....

  print("Log likelihood on test data:")
  print(ed.evaluate('log_lik', data={X: X_test, y_post: y_test}))

  print("Displaying prior predictive samples.")
  n_prior_samples = 10

  ....

if __name__ == "__main__":
  tf.app.run()

Error:

 File "bayesian_regression.py", line 70, in main
    print(ed.evaluate('log_lik', data={X: X_test, y_post: y_test}))
  File "/usr/local/lib/python2.7/site-packages/edward/criticisms/evaluate.py", line 219, in evaluate
    evaluations += [log_likelihood(y_true, n_samples, output_key, feed_dict, sess)]
  File "/usr/local/lib/python2.7/site-packages/edward/criticisms/evaluate.py", line 478, in log_likelihood
    tensor = tf.reduce_mean(output_key.log_prob(y_true))
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/distributions/distribution.py", line 718, in log_prob
    return self._call_log_prob(value, name)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/distributions/distribution.py", line 700, in _call_log_prob
    return self._log_prob(value, **kwargs)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/distributions/normal.py", line 189, in _log_prob
    return self._log_unnormalized_prob(x) - self._log_normalization()
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/distributions/normal.py", line 207, in _log_unnormalized_prob
    return -0.5 * math_ops.square(self._z(x))
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/distributions/normal.py", line 232, in _z
    return (x - self.loc) / self.scale
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 925, in binary_op_wrapper
    y = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name="y")
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 946, in convert_to_tensor
    as_ref=False)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1036, in internal_convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/usr/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 879, in _TensorTensorConversionFunction
    (dtype.name, t.dtype.name, str(t)))
ValueError: Tensor conversion requested dtype float64 for Tensor with dtype float32: 'Tensor("copied/Normal_2/loc:0", shape=(40,), dtype=float32)'

It seems like the log_likelihood module was missing the cast to float32. I have added it in this change and also refactored the code a little.

Thanks for sharing. Is this something that should be fixed in the code base? It seems like the inputs to evaluate should just have compatible dtype.

I also thought so, but the dtype is not used anywhere in the code except the placeholder. i tried making it float64, but the program crashed with that as well. Also, this does not happen when i try mse or other methods, since they are already casting the values in evaluate.py. Hence, I thought log_lik might also need this. What do you think?

Can you provide a minimal reproducible example? What happens if you downcast the NumPy array to float32?