Remove tf.contrib.ndlstm as it is not maintained and barely used.

Users can find an external implementation by the original author at:

https://github.com/tmbarchive/tfndlstm

PiperOrigin-RevId: 184914822

4 files changed, 0 insertions, 62 deletions

diff --git a/tensorflow/contrib/specs/BUILD b/tensorflow/contrib/specs/BUILD
index 4b688690ae..084953a0a2 100644
--- a/tensorflow/contrib/specs/BUILD
+++ b/tensorflow/contrib/specs/BUILD
@@ -23,7 +23,6 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow/contrib/layers:layers_py",
-        "//tensorflow/contrib/ndlstm",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:framework_for_generated_wrappers",
         "//tensorflow/python:logging_ops",
diff --git a/tensorflow/contrib/specs/README.md b/tensorflow/contrib/specs/README.md
index b764e6e714..bcf34e601f 100644
--- a/tensorflow/contrib/specs/README.md
+++ b/tensorflow/contrib/specs/README.md
@@ -59,17 +59,6 @@ Reshaping:
  - `Squeeze` = tf.squeeze
  - `Expand` = tf.expand_dims
 
-Multidimensional LSTM:
-
-These are intended as alternatives to 2D convolutions.  For sequence models,
-there will be other modeling primitives.
-
- - `Lstm2` = Fun(lstm2d.separable_lstm)  # 2D-to-2D
- - `Lstm2to1` = Fun(lstm2d.reduce_to_sequence)  # 2D-to-1D
- - `Lstm2to0` = Fun(lstm2d.reduce_to_final)  # 2D-to-vector
- - `Clstm2(n, m)` is a `Cl(n, [3,3])` followed by `Lstm2(m)`
- - `Dws(n)` is a depthwise convolution `Cs(n, [1, 1])`
-
 Other:
 
  - `Id` = identity
diff --git a/tensorflow/contrib/specs/python/specs_ops.py b/tensorflow/contrib/specs/python/specs_ops.py
index a6bd4d16c2..49b989b8d0 100644
--- a/tensorflow/contrib/specs/python/specs_ops.py
+++ b/tensorflow/contrib/specs/python/specs_ops.py
@@ -23,8 +23,6 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.layers.python.layers import layers
-from tensorflow.contrib.ndlstm.python import lstm1d
-from tensorflow.contrib.ndlstm.python import lstm2d
 from tensorflow.contrib.specs.python import specs_lib
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import logging_ops
@@ -122,17 +120,6 @@ Sig = Fun(math_ops.sigmoid)
 Tanh = Fun(math_ops.tanh)
 Smax = Fun(nn_ops.softmax)
 
-# 2D LSTM
-
-Lstm2 = Fun(lstm2d.separable_lstm)
-Lstm2to1 = Fun(lstm2d.reduce_to_sequence)  # 2D to 1D
-Lstm2to0 = Fun(lstm2d.reduce_to_final)  # 2D to depth-only
-
-
-def Clstm2(n, *args, **kw):
-  """2D LSTM with 3x3 pre-convolution."""
-  return Cl(n, [3, 3]) | Lstm2(*args, **kw)
-
 
 def Dws(n):
   """Depth-wise convolution + sigmoid (used after LSTM)."""
@@ -143,13 +130,6 @@ def Dwm(n):
   """Depth-wise convolution + softmax (used after LSTM)."""
   return Cm(n, [1, 1])
 
-
-# 1D LSTM
-
-Lstm1 = Fun(lstm1d.ndlstm_base)
-Lstm1to0 = Fun(lstm1d.sequence_to_final)  # 1D to depth-only
-Ssm = Fun(lstm1d.sequence_softmax)
-
 # Sharing of Variables
 
 
diff --git a/tensorflow/contrib/specs/python/specs_test.py b/tensorflow/contrib/specs/python/specs_test.py
index 41782a9fc9..9a4ad36793 100644
--- a/tensorflow/contrib/specs/python/specs_test.py
+++ b/tensorflow/contrib/specs/python/specs_test.py
@@ -149,36 +149,6 @@ class SpecsTest(test.TestCase):
       self.assertEqual(tuple(result.shape), (10, 20))
       self.assertEqual(summaries.tf_spec_structure(spec, inputs), "_ sig sig")
 
-  def testLstm2(self):
-    with self.test_session():
-      inputs = constant_op.constant(_rand(1, 64, 64, 5))
-      spec = "net = Lstm2(15)"
-      outputs = specs.create_net(spec, inputs)
-      self.assertEqual(outputs.get_shape().as_list(), [1, 64, 64, 15])
-      variables.global_variables_initializer().run()
-      result = outputs.eval()
-      self.assertEqual(tuple(result.shape), (1, 64, 64, 15))
-
-  def testLstm2to1(self):
-    with self.test_session():
-      inputs = constant_op.constant(_rand(1, 64, 64, 5))
-      spec = "net = Lstm2to1(15)"
-      outputs = specs.create_net(spec, inputs)
-      self.assertEqual(outputs.get_shape().as_list(), [1, 64, 15])
-      variables.global_variables_initializer().run()
-      result = outputs.eval()
-      self.assertEqual(tuple(result.shape), (1, 64, 15))
-
-  def testLstm2to0(self):
-    with self.test_session():
-      inputs = constant_op.constant(_rand(1, 64, 64, 5))
-      spec = "net = Lstm2to0(15)"
-      outputs = specs.create_net(spec, inputs)
-      self.assertEqual(outputs.get_shape().as_list(), [1, 15])
-      variables.global_variables_initializer().run()
-      result = outputs.eval()
-      self.assertEqual(tuple(result.shape), (1, 15))
-
   def testKeywordRestriction(self):
     with self.test_session():
       inputs = constant_op.constant(_rand(10, 20))