def main():
import problem_unittests as tests
tests.test_get_init_cell(get_init_cell)
tests.test_get_embed(get_embed)
tests.test_build_rnn(build_rnn)
tests.test_build_nn(build_nn)
tests.test_get_batches(get_batches)
tests.test_get_tensors(get_tensors)
tests.test_pick_word(pick_word)
print(get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
batch_size=3,
seq_length=2))
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)
# TODO: Implement Function
layers = 1
keep_prob = 1
lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)
cell = tf.contrib.rnn.MultiRNNCell([drop] * layers)
initial_state = cell.zero_state(batch_size, tf.float32)
initial_state = tf.identity(initial_state, name='initial_state')
return (cell, initial_state)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_init_cell(get_init_cell)
# ### Word Embedding
# Apply embedding to `input_data` using TensorFlow. Return the embedded sequence.
# In[49]:
def get_embed(input_data, vocab_size, embed_dim):
"""
Create embedding for <input_data>.
:param input_data: TF placeholder for text input.
:param vocab_size: Number of words in vocabulary.
:param embed_dim: Number of embedding dimensions
:return: Embedded input.
"""
