Made TensorFlow documentation on LSTMs slightly more accurate.

PiperOrigin-RevId: 160047054

2 files changed, 17 insertions, 2 deletions

diff --git a/tensorflow/docs_src/tutorials/recurrent.md b/tensorflow/docs_src/tutorials/recurrent.md
index 708a9620dd..346b6be06c 100644
--- a/tensorflow/docs_src/tutorials/recurrent.md
+++ b/tensorflow/docs_src/tutorials/recurrent.md
@@ -75,7 +75,9 @@ The basic pseudocode is as follows:
 words_in_dataset = tf.placeholder(tf.float32, [num_batches, batch_size, num_features])
 lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)
 # Initial state of the LSTM memory.
-state = tf.zeros([batch_size, lstm.state_size])
+hidden_state = tf.zeros([batch_size, lstm.state_size])
+current_state = tf.zeros([batch_size, lstm.state_size])
+state = hidden_state, current_state
 probabilities = []
 loss = 0.0
 for current_batch_of_words in words_in_dataset:
diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py
index ca69cddae2..c0d9c971a0 100644
--- a/tensorflow/python/ops/rnn_cell_impl.py
+++ b/tensorflow/python/ops/rnn_cell_impl.py
@@ -374,7 +374,20 @@ class BasicLSTMCell(RNNCell):
     return self._num_units
 
   def call(self, inputs, state):
-    """Long short-term memory cell (LSTM)."""
+    """Long short-term memory cell (LSTM).
+
+    Args:
+      inputs: `2-D` tensor with shape `[batch_size x input_size]`.
+      state: An `LSTMStateTuple` of state tensors, each shaped
+        `[batch_size x self.state_size]`, if `state_is_tuple` has been set to
+        `True`.  Otherwise, a `Tensor` shaped
+        `[batch_size x 2 * self.state_size]`.
+
+    Returns:
+      A pair containing the new hidden state, and the new state (either a
+        `LSTMStateTuple` or a concatenated state, depending on
+        `state_is_tuple`).
+    """
     sigmoid = math_ops.sigmoid
     # Parameters of gates are concatenated into one multiply for efficiency.
     if self._state_is_tuple: