message: Fix the example in the RNN tutorial which left out one of the pieces of data.
diff: @@ -51,10 +51,10 @@ The core of the model consists of an LSTM cell that processes one word at a
  time and computes probabilities of the possible values for the next word in the
  sentence. The memory state of the network is initialized with a vector of zeros
  and gets updated after reading each word. For computational reasons, we will
  -process data in mini-batches of size `batch_size`. In this example, it is important
  -to note that `current_batch_of_words` does not correspond to a "sentence" of words.
  -Every word in a batch should correspond to time t. Tensorflow will automatically sum
  -the gradients of each batch for you.
  +process data in mini-batches of size `batch_size`. In this example, it is
  +important to note that `current_batch_of_words` does not correspond to a
  +"sentence" of words. Every word in a batch should correspond to a time t.
  +TensorFlow will automatically sum the gradients of each batch for you.
  For example:
  ```
  @@ -63,16 +63,17 @@ For example:
  [The, red, fox, jumped, high]
  words_in_dataset[0] = [The, The]
  -words_in_dataset[1] = [fox, fox]
  -words_in_dataset[2] = [is, jumped]
  -words_in_dataset[3] = [quick, high]
  -num_batches = 4, batch_size = 2, time_steps = 5
  +words_in_dataset[1] = [brown, red]
  +words_in_dataset[2] = [fox, fox]
  +words_in_dataset[3] = [is, jumped]
  +words_in_dataset[4] = [quick, high]
  +batch_size = 2, time_steps = 5
  ```
  The basic pseudocode is as follows:
  ```python
  -words_in_dataset = tf.placeholder(tf.float32, [num_batches, batch_size, num_features])
  +words_in_dataset = tf.placeholder(tf.float32, [time_steps, batch_size, num_features])
  lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)
  # Initial state of the LSTM memory.
  hidden_state = tf.zeros([batch_size, lstm.state_size])
  