def run_test():
import problem_unittests as t
t.test_create_lookup_tables(create_lookup_tables)
t.test_get_batches(get_batches)
t.test_tokenize(token_lookup)
t.test_get_inputs(get_inputs)
t.test_get_init_cell(get_init_cell)
t.test_get_embed(get_embed)
t.test_build_rnn(build_rnn)
t.test_build_nn(build_nn)
t.test_get_tensors(get_tensors)
t.test_pick_word(pick_word)
Create TF Placeholders for input, targets, and learning rate.
:return: Tuple (input, targets, learning rate)
"""
# TODO: Implement Function
inputs = tf.placeholder(dtype=tf.int32, shape=[None, None], name='input')
targets = tf.placeholder(dtype=tf.int32,
shape=[None, None],
name='targets')
learning_rate = tf.placeholder(dtype=tf.float32, name='learning_rate')
return inputs, targets, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_inputs(get_inputs)
# ### Build RNN Cell and Initialize
# Stack one or more [`BasicLSTMCells`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/BasicLSTMCell) in a [`MultiRNNCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell).
# - The Rnn size should be set using `rnn_size`
# - Initalize Cell State using the MultiRNNCell's [`zero_state()`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell#zero_state) function
# - Apply the name "initial_state" to the initial state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)
#
# Return the cell and initial state in the following tuple `(Cell, InitialState)`
# In[48]:
def get_init_cell(batch_size, rnn_size):
"""
Create an RNN Cell and initialize it.
