Update generated Python Op docs.

Change: 140062662

12 files changed, 38 insertions, 40 deletions

diff --git a/tensorflow/g3doc/api_docs/python/contrib.rnn.md b/tensorflow/g3doc/api_docs/python/contrib.rnn.md
index 1d59c1c630..8f28c19232 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.rnn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.rnn.md
@@ -21,7 +21,7 @@ reduce the scale of forgetting in the beginning of the training.
 
 Unlike `rnn_cell.LSTMCell`, this is a monolithic op and should be much faster.
 The weight and bias matrixes should be compatible as long as the variable
-scope matches, and you use `use_compatible_names=True`.
+scope matches.
 - - -
 
 #### `tf.contrib.rnn.LSTMBlockCell.__call__(x, states_prev, scope=None)` {#LSTMBlockCell.__call__}
@@ -31,7 +31,7 @@ Long short-term memory cell (LSTM).
 
 - - -
 
-#### `tf.contrib.rnn.LSTMBlockCell.__init__(num_units, forget_bias=1.0, use_peephole=False, use_compatible_names=False)` {#LSTMBlockCell.__init__}
+#### `tf.contrib.rnn.LSTMBlockCell.__init__(num_units, forget_bias=1.0, use_peephole=False)` {#LSTMBlockCell.__init__}
 
 Initialize the basic LSTM cell.
 
@@ -41,8 +41,6 @@ Initialize the basic LSTM cell.
 *  <b>`num_units`</b>: int, The number of units in the LSTM cell.
 *  <b>`forget_bias`</b>: float, The bias added to forget gates (see above).
 *  <b>`use_peephole`</b>: Whether to use peephole connections or not.
-*  <b>`use_compatible_names`</b>: If True, use the same variable naming as
-    rnn_cell.LSTMCell
 
 
 - - -
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.bidirectional_rnn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.bidirectional_rnn.md
index 7ff1e48648..f9d14ef9de 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.bidirectional_rnn.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.bidirectional_rnn.md
@@ -29,7 +29,8 @@ length(s) of the sequence(s) or completely unrolled if length(s) is not given.
     either of the initial states are not provided.
 *  <b>`sequence_length`</b>: (optional) An int32/int64 vector, size `[batch_size]`,
     containing the actual lengths for each of the sequences.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "BiRNN"
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to
+    "bidirectional_rnn"
 
 ##### Returns:
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.rnn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.rnn.md
index a2d8187fad..ac38b8f422 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.rnn.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.rnn.md
@@ -46,7 +46,7 @@ The dynamic calculation performed is, at time `t` for batch row `b`,
     dtype.
 *  <b>`sequence_length`</b>: Specifies the length of each sequence in inputs.
     An int32 or int64 vector (tensor) size `[batch_size]`, values in `[0, T)`.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "RNN".
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "rnn".
 
 ##### Returns:
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.rnn.LSTMBlockCell.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.rnn.LSTMBlockCell.md
index 876a1592f1..cb90c403c1 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.rnn.LSTMBlockCell.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.rnn.LSTMBlockCell.md
@@ -7,7 +7,7 @@ reduce the scale of forgetting in the beginning of the training.
 
 Unlike `rnn_cell.LSTMCell`, this is a monolithic op and should be much faster.
 The weight and bias matrixes should be compatible as long as the variable
-scope matches, and you use `use_compatible_names=True`.
+scope matches.
 - - -
 
 #### `tf.contrib.rnn.LSTMBlockCell.__call__(x, states_prev, scope=None)` {#LSTMBlockCell.__call__}
@@ -17,7 +17,7 @@ Long short-term memory cell (LSTM).
 
 - - -
 
-#### `tf.contrib.rnn.LSTMBlockCell.__init__(num_units, forget_bias=1.0, use_peephole=False, use_compatible_names=False)` {#LSTMBlockCell.__init__}
+#### `tf.contrib.rnn.LSTMBlockCell.__init__(num_units, forget_bias=1.0, use_peephole=False)` {#LSTMBlockCell.__init__}
 
 Initialize the basic LSTM cell.
 
@@ -27,8 +27,6 @@ Initialize the basic LSTM cell.
 *  <b>`num_units`</b>: int, The number of units in the LSTM cell.
 *  <b>`forget_bias`</b>: float, The bias added to forget gates (see above).
 *  <b>`use_peephole`</b>: Whether to use peephole connections or not.
-*  <b>`use_compatible_names`</b>: If True, use the same variable naming as
-    rnn_cell.LSTMCell
 
 
 - - -
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.rnn_cell.BasicRNNCell.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.rnn_cell.BasicRNNCell.md
index a08ef164f1..a2aed04e46 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.rnn_cell.BasicRNNCell.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.rnn_cell.BasicRNNCell.md
@@ -3,7 +3,7 @@ The most basic RNN cell.
 
 #### `tf.nn.rnn_cell.BasicRNNCell.__call__(inputs, state, scope=None)` {#BasicRNNCell.__call__}
 
-Most basic RNN: output = new_state = activation(W * input + U * state + B).
+Most basic RNN: output = new_state = act(W * input + U * state + B).
 
 
 - - -
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.rnn_cell.LSTMCell.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.rnn_cell.LSTMCell.md
index 9f1e999461..5ee8c2ad30 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.rnn_cell.LSTMCell.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.rnn_cell.LSTMCell.md
@@ -31,7 +31,7 @@ Run one step of LSTM.
     `2-D, batch x state_size`.  If `state_is_tuple` is True, this must be a
     tuple of state Tensors, both `2-D`, with column sizes `c_state` and
     `m_state`.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "LSTMCell".
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "lstm_cell".
 
 ##### Returns:
 
@@ -54,7 +54,7 @@ Run one step of LSTM.
 
 - - -
 
-#### `tf.nn.rnn_cell.LSTMCell.__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=True, activation=tanh)` {#LSTMCell.__init__}
+#### `tf.nn.rnn_cell.LSTMCell.__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=None, num_proj_shards=None, forget_bias=1.0, state_is_tuple=True, activation=tanh)` {#LSTMCell.__init__}
 
 Initialize the parameters for an LSTM cell.
 
@@ -71,13 +71,12 @@ Initialize the parameters for an LSTM cell.
 *  <b>`num_proj`</b>: (optional) int, The output dimensionality for the projection
     matrices.  If None, no projection is performed.
 *  <b>`proj_clip`</b>: (optional) A float value.  If `num_proj > 0` and `proj_clip` is
-  provided, then the projected values are clipped elementwise to within
-  `[-proj_clip, proj_clip]`.
-
-*  <b>`num_unit_shards`</b>: How to split the weight matrix.  If >1, the weight
-    matrix is stored across num_unit_shards.
-*  <b>`num_proj_shards`</b>: How to split the projection matrix.  If >1, the
-    projection matrix is stored across num_proj_shards.
+    provided, then the projected values are clipped elementwise to within
+    `[-proj_clip, proj_clip]`.
+*  <b>`num_unit_shards`</b>: Deprecated, will be removed by Jan. 2017.
+    Use a variable_scope partitioner instead.
+*  <b>`num_proj_shards`</b>: Deprecated, will be removed by Jan. 2017.
+    Use a variable_scope partitioner instead.
 *  <b>`forget_bias`</b>: Biases of the forget gate are initialized by default to 1
     in order to reduce the scale of forgetting at the beginning of
     the training.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.state_saving_rnn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.state_saving_rnn.md
index 67a444ad4a..14198ab9c2 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.state_saving_rnn.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.nn.state_saving_rnn.md
@@ -16,7 +16,7 @@ RNN that accepts a state saver for time-truncated RNN calculation.
     be a single string.
 *  <b>`sequence_length`</b>: (optional) An int32/int64 vector size [batch_size].
     See the documentation for rnn() for more details about sequence_length.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "RNN".
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "rnn".
 
 ##### Returns:
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bidirectional_dynamic_rnn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bidirectional_dynamic_rnn.md
index 368e588028..9d0fe0e3ef 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bidirectional_dynamic_rnn.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bidirectional_dynamic_rnn.md
@@ -51,7 +51,8 @@ given.
     accepts input and emits output in batch-major form.
 *  <b>`dtype`</b>: (optional) The data type for the initial state.  Required if
     either of the initial states are not provided.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "BiRNN"
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to
+    "bidirectional_rnn"
 
 ##### Returns:
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.dynamic_rnn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.dynamic_rnn.md
index 81517a1ac6..f2ae7527e7 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.dynamic_rnn.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.dynamic_rnn.md
@@ -67,7 +67,7 @@ for correctness than performance, unlike in rnn().
     transposes at the beginning and end of the RNN calculation.  However,
     most TensorFlow data is batch-major, so by default this function
     accepts input and emits output in batch-major form.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "RNN".
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "rnn".
 
 ##### Returns:
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.raw_rnn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.raw_rnn.md
index 8c0d9bd027..8cb2eab12f 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.raw_rnn.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.raw_rnn.md
@@ -136,7 +136,7 @@ outputs = outputs_ta.pack()
     but needed for back prop from GPU to CPU.  This allows training RNNs
     which would typically not fit on a single GPU, with very minimal (or no)
     performance penalty.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "RNN".
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "rnn".
 
 ##### Returns:
 
diff --git a/tensorflow/g3doc/api_docs/python/nn.md b/tensorflow/g3doc/api_docs/python/nn.md
index 438542bc88..5f1e189874 100644
--- a/tensorflow/g3doc/api_docs/python/nn.md
+++ b/tensorflow/g3doc/api_docs/python/nn.md
@@ -2591,7 +2591,7 @@ for correctness than performance, unlike in rnn().
     transposes at the beginning and end of the RNN calculation.  However,
     most TensorFlow data is batch-major, so by default this function
     accepts input and emits output in batch-major form.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "RNN".
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "rnn".
 
 ##### Returns:
 
@@ -2675,7 +2675,7 @@ The dynamic calculation performed is, at time `t` for batch row `b`,
     dtype.
 *  <b>`sequence_length`</b>: Specifies the length of each sequence in inputs.
     An int32 or int64 vector (tensor) size `[batch_size]`, values in `[0, T)`.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "RNN".
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "rnn".
 
 ##### Returns:
 
@@ -2713,7 +2713,7 @@ RNN that accepts a state saver for time-truncated RNN calculation.
     be a single string.
 *  <b>`sequence_length`</b>: (optional) An int32/int64 vector size [batch_size].
     See the documentation for rnn() for more details about sequence_length.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "RNN".
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "rnn".
 
 ##### Returns:
 
@@ -2784,7 +2784,8 @@ given.
     accepts input and emits output in batch-major form.
 *  <b>`dtype`</b>: (optional) The data type for the initial state.  Required if
     either of the initial states are not provided.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "BiRNN"
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to
+    "bidirectional_rnn"
 
 ##### Returns:
 
@@ -2848,7 +2849,8 @@ length(s) of the sequence(s) or completely unrolled if length(s) is not given.
     either of the initial states are not provided.
 *  <b>`sequence_length`</b>: (optional) An int32/int64 vector, size `[batch_size]`,
     containing the actual lengths for each of the sequences.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "BiRNN"
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to
+    "bidirectional_rnn"
 
 ##### Returns:
 
@@ -3005,7 +3007,7 @@ outputs = outputs_ta.pack()
     but needed for back prop from GPU to CPU.  This allows training RNNs
     which would typically not fit on a single GPU, with very minimal (or no)
     performance penalty.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "RNN".
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "rnn".
 
 ##### Returns:
 
diff --git a/tensorflow/g3doc/api_docs/python/rnn_cell.md b/tensorflow/g3doc/api_docs/python/rnn_cell.md
index 0c1140799d..c6d39bd936 100644
--- a/tensorflow/g3doc/api_docs/python/rnn_cell.md
+++ b/tensorflow/g3doc/api_docs/python/rnn_cell.md
@@ -109,7 +109,7 @@ The most basic RNN cell.
 
 #### `tf.nn.rnn_cell.BasicRNNCell.__call__(inputs, state, scope=None)` {#BasicRNNCell.__call__}
 
-Most basic RNN: output = new_state = activation(W * input + U * state + B).
+Most basic RNN: output = new_state = act(W * input + U * state + B).
 
 
 - - -
@@ -326,7 +326,7 @@ Run one step of LSTM.
     `2-D, batch x state_size`.  If `state_is_tuple` is True, this must be a
     tuple of state Tensors, both `2-D`, with column sizes `c_state` and
     `m_state`.
-*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "LSTMCell".
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "lstm_cell".
 
 ##### Returns:
 
@@ -349,7 +349,7 @@ Run one step of LSTM.
 
 - - -
 
-#### `tf.nn.rnn_cell.LSTMCell.__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=True, activation=tanh)` {#LSTMCell.__init__}
+#### `tf.nn.rnn_cell.LSTMCell.__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=None, num_proj_shards=None, forget_bias=1.0, state_is_tuple=True, activation=tanh)` {#LSTMCell.__init__}
 
 Initialize the parameters for an LSTM cell.
 
@@ -366,13 +366,12 @@ Initialize the parameters for an LSTM cell.
 *  <b>`num_proj`</b>: (optional) int, The output dimensionality for the projection
     matrices.  If None, no projection is performed.
 *  <b>`proj_clip`</b>: (optional) A float value.  If `num_proj > 0` and `proj_clip` is
-  provided, then the projected values are clipped elementwise to within
-  `[-proj_clip, proj_clip]`.
-
-*  <b>`num_unit_shards`</b>: How to split the weight matrix.  If >1, the weight
-    matrix is stored across num_unit_shards.
-*  <b>`num_proj_shards`</b>: How to split the projection matrix.  If >1, the
-    projection matrix is stored across num_proj_shards.
+    provided, then the projected values are clipped elementwise to within
+    `[-proj_clip, proj_clip]`.
+*  <b>`num_unit_shards`</b>: Deprecated, will be removed by Jan. 2017.
+    Use a variable_scope partitioner instead.
+*  <b>`num_proj_shards`</b>: Deprecated, will be removed by Jan. 2017.
+    Use a variable_scope partitioner instead.
 *  <b>`forget_bias`</b>: Biases of the forget gate are initialized by default to 1
     in order to reduce the scale of forgetting at the beginning of
     the training.