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, and*tf.summary.scalar()**tf.merge_all_summaries()*to*tf.summary.merge_all()*