Update cookbook.

README.md

@@ -16,6 +16,8 @@ Table of Contents
 12. [Numerical stability in TensorFlow](#stable)
 13. [Building a neural network training framework with learn API](#tf_learn)
 14. [TensorFlow Cookbook](#cookbook)
+    - [Get shape](#get_shape)
+    - [Batch gather](#batch_gather)
     - [Beam search](#beam_search)
     - [Merge](#merge)
     - [Entropy](#entropy)
@@ -1202,22 +1204,20 @@ And that's it! This is all you need to get started with TensorFlow learn API. I
 <a name="cookbook"></a>
 This section includes implementation of a set of common operations in TensorFlow.
 
-### Beam Search <a name="beam_search"></a>
+### Get shape <a name="get_shape"></a>
 ```python
-import tensorflow as tf
-
 def get_shape(tensor):
   """Returns static shape if available and dynamic shape otherwise."""
   static_shape = tensor.shape.as_list()
   dynamic_shape = tf.unstack(tf.shape(tensor))
   dims = [s[1] if s[0] is None else s[0]
           for s in zip(static_shape, dynamic_shape)]
   return dims
+```
 
-def log_prob_from_logits(logits, axis=-1):
-  """Normalize the log-probabilities so that probabilities sum to one."""
-  return logits - tf.reduce_logsumexp(logits, axis=axis, keep_dims=True)
+### Batch Gather <a name="batch_gather"></a>
 
+```python
 def batch_gather(tensor, indices):
   """Gather in batch from a tensor of arbitrary size.
 
@@ -1237,6 +1237,11 @@ def batch_gather(tensor, indices):
   offset = tf.reshape(tf.range(shape[0]) * shape[1], offset_shape)
   output = tf.gather(flat_first, indices + offset)
   return output
+```
+
+### Beam Search <a name="beam_search"></a>
+```python
+import tensorflow as tf
 
 def rnn_beam_search(update_fn, initial_state, sequence_length, beam_width,
                     begin_token_id, end_token_id, name="rnn"):
@@ -1270,7 +1275,7 @@ def rnn_beam_search(update_fn, initial_state, sequence_length, beam_width,
     with tf.variable_scope(name, reuse=True if i > 0 else None):
 
       state, logits = update_fn(state, ids)
-      logits = log_prob_from_logits(logits)
+      logits = tf.nn.log_softmax(logits)
 
       sum_logprobs = (
           tf.expand_dims(sel_sum_logprobs, axis=2) +