Some deprecated functions in TensorFlow

This page lists some deprecated functions in TensorFlow I have noticed.

THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-02. Instructions for updating: Use tf.global_variables_initializer instead.

THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-02. Instructions for updating: Use tf.local_variables_initializer instead.

The following deprecated are from this link

  • Division and modulus operators (/, //, %) now match Python (flooring) semantics. This applies to tf.div and tf.mod as well. To obtain forced integer truncation based behaviors you can use tf.truncatediv and tf.truncatemod.
  • tf.divide() is now the recommended division function. tf.div() will remain, but its semantics do not respond to Python 3 or from future mechanisms.
  • tf.reverse() now takes indices of axes to be reversed. E.g. tf.reverse(a, [True, False, True]) must now be written as tf.reverse(a, [0, 2]). tf.reverse_v2() will remain until 1.0 final.
  • tf.mul, tf.sub and tf.neg are deprecated in favor of tf.multiply, tf.subtract and tf.negative.
  • tf.pack and tf.unpack are deprecated in favor of tf.stack and tf.unstack.
  • TensorArray.pack and TensorArray.unpack are getting deprecated in favor of TensorArray.stack and TensorArray.unstack.
  • The following Python functions have had their arguments changed to use axis when referring to specific dimensions. We have kept the old keyword arguments for compatibility currently, but we will be removing them well before the final 1.0.
    • tf.argmax: dimension becomes axis
    • tf.argmin: dimension becomes axis
    • tf.count_nonzero: reduction_indices becomes axis
    • tf.expand_dims: dim becomes axis
    • tf.reduce_all: reduction_indices becomes axis
    • tf.reduce_any: reduction_indices becomes axis
    • tf.reduce_join: reduction_indices becomes axis
    • tf.reduce_logsumexp: reduction_indices becomes axis
    • tf.reduce_max: reduction_indices becomes axis
    • tf.reduce_mean: reduction_indices becomes axis
    • tf.reduce_min: reduction_indices becomes axis
    • tf.reduce_prod: reduction_indices becomes axis
    • tf.reduce_sum: reduction_indices becomes axis
    • tf.reverse_sequence: batch_dim becomes batch_axis, seq_dim becomes seq_axis
    • tf.sparse_concat: concat_dim becomes axis
    • tf.sparse_reduce_sum: reduction_axes becomes axis
    • tf.sparse_reduce_sum_sparse: reduction_axes becomes axis
    • tf.sparse_split: split_dim becomes axis
  • tf.listdiff has been renamed to tf.setdiff1d to match NumPy naming.
  • tf.inv has been renamed to be tf.reciprocal (component-wise reciprocal) to avoid confusion with np.inv which is matrix inversion
  • tf.round now uses banker’s rounding (round to even) semantics to match NumPy.
  • tf.split now takes arguments in a reversed order and with different keywords. In particular, we now match NumPy order as tf.split(value, num_or_size_splits, axis).
  • tf.sparse_split now takes arguments in reversed order and with different keywords. In particular we now match NumPy order as tf.sparse_split(sp_input, num_split, axis). NOTE: we have temporarily made tf.sparse_split require keyword arguments.
  • Deprecated tf.concat operator. Please switch to use tf.concat_v2 for now. In the Beta release, we will update tf.concat to match argument order of `tf.concat_v2.
  • tf.image.decode_jpeg by default uses the faster DCT method, sacrificing a little fidelity for improved speed. One can revert to the old behavior by specifying the attribute dct_method=’INTEGER_ACCURATE’.
  • tf.complex_abs has been removed from the Python interface. tf.abs supports complex tensors and should be used instead.


  • In TensorFlow version 1.0, be sure to switch all the tf.scalar_summary() to tf.summary.scalar(), and tf.merge_all_summaries() to tf.summary.merge_all()