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problem_unittests.py
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problem_unittests.py
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import numpy as np
import tensorflow as tf
from tensorflow.contrib import rnn
def _print_success_message():
print('Tests Passed')
def test_create_lookup_tables(create_lookup_tables):
with tf.Graph().as_default():
test_text = '''
Moe_Szyslak Moe's Tavern Where the elite meet to drink
Bart_Simpson Eh yeah hello is Mike there Last name Rotch
Moe_Szyslak Hold on I'll check Mike Rotch Mike Rotch Hey has anybody seen Mike Rotch lately
Moe_Szyslak Listen you little puke One of these days I'm gonna catch you and I'm gonna carve my name on your back with an ice pick
Moe_Szyslak Whats the matter Homer You're not your normal effervescent self
Homer_Simpson I got my problems Moe Give me another one
Moe_Szyslak Homer hey you should not drink to forget your problems
Barney_Gumble Yeah you should only drink to enhance your social skills'''
test_text = test_text.lower()
test_text = test_text.split()
vocab_to_int, int_to_vocab = create_lookup_tables(test_text)
# Check types
assert isinstance(vocab_to_int, dict),\
'vocab_to_int is not a dictionary.'
assert isinstance(int_to_vocab, dict),\
'int_to_vocab is not a dictionary.'
# Compare lengths of dicts
assert len(vocab_to_int) == len(int_to_vocab),\
'Length of vocab_to_int and int_to_vocab don\'t match. ' \
'vocab_to_int is length {}. int_to_vocab is length {}'.format(len(vocab_to_int), len(int_to_vocab))
# Make sure the dicts have the same words
vocab_to_int_word_set = set(vocab_to_int.keys())
int_to_vocab_word_set = set(int_to_vocab.values())
assert not (vocab_to_int_word_set - int_to_vocab_word_set),\
'vocab_to_int and int_to_vocab don\'t have the same words.' \
'{} found in vocab_to_int, but not in int_to_vocab'.format(vocab_to_int_word_set - int_to_vocab_word_set)
assert not (int_to_vocab_word_set - vocab_to_int_word_set),\
'vocab_to_int and int_to_vocab don\'t have the same words.' \
'{} found in int_to_vocab, but not in vocab_to_int'.format(int_to_vocab_word_set - vocab_to_int_word_set)
# Make sure the dicts have the same word ids
vocab_to_int_word_id_set = set(vocab_to_int.values())
int_to_vocab_word_id_set = set(int_to_vocab.keys())
assert not (vocab_to_int_word_id_set - int_to_vocab_word_id_set),\
'vocab_to_int and int_to_vocab don\'t contain the same word ids.' \
'{} found in vocab_to_int, but not in int_to_vocab'.format(vocab_to_int_word_id_set - int_to_vocab_word_id_set)
assert not (int_to_vocab_word_id_set - vocab_to_int_word_id_set),\
'vocab_to_int and int_to_vocab don\'t contain the same word ids.' \
'{} found in int_to_vocab, but not in vocab_to_int'.format(int_to_vocab_word_id_set - vocab_to_int_word_id_set)
# Make sure the dicts make the same lookup
missmatches = [(word, id, id, int_to_vocab[id]) for word, id in vocab_to_int.items() if int_to_vocab[id] != word]
assert not missmatches,\
'Found {} missmatche(s). First missmatch: vocab_to_int[{}] = {} and int_to_vocab[{}] = {}'.format(
len(missmatches),
*missmatches[0])
assert len(vocab_to_int) > len(set(test_text))/2,\
'The length of vocab seems too small. Found a length of {}'.format(len(vocab_to_int))
_print_success_message()
def test_get_batches(get_batches):
with tf.Graph().as_default():
test_batch_size = 128
test_seq_length = 5
test_int_text = list(range(1000*test_seq_length))
batches = get_batches(test_int_text, test_batch_size, test_seq_length)
# Check type
assert isinstance(batches, np.ndarray),\
'Batches is not a Numpy array'
# Check shape
assert batches.shape == (7, 2, 128, 5),\
'Batches returned wrong shape. Found {}'.format(batches.shape)
_print_success_message()
def test_tokenize(token_lookup):
with tf.Graph().as_default():
symbols = set(['.', ',', '"', ';', '!', '?', '(', ')', '--', '\n'])
token_dict = token_lookup()
# Check type
assert isinstance(token_dict, dict), \
'Returned type is {}.'.format(type(token_dict))
# Check symbols
missing_symbols = symbols - set(token_dict.keys())
unknown_symbols = set(token_dict.keys()) - symbols
assert not missing_symbols, \
'Missing symbols: {}'.format(missing_symbols)
assert not unknown_symbols, \
'Unknown symbols: {}'.format(unknown_symbols)
# Check values type
bad_value_type = [type(val) for val in token_dict.values() if not isinstance(val, str)]
assert not bad_value_type,\
'Found token as {} type.'.format(bad_value_type[0])
# Check for spaces
key_has_spaces = [k for k in token_dict.keys() if ' ' in k]
val_has_spaces = [val for val in token_dict.values() if ' ' in val]
assert not key_has_spaces,\
'The key "{}" includes spaces. Remove spaces from keys and values'.format(key_has_spaces[0])
assert not val_has_spaces,\
'The value "{}" includes spaces. Remove spaces from keys and values'.format(val_has_spaces[0])
# Check for symbols in values
symbol_val = ()
for symbol in symbols:
for val in token_dict.values():
if symbol in val:
symbol_val = (symbol, val)
assert not symbol_val,\
'Don\'t use a symbol that will be replaced in your tokens. Found the symbol {} in value {}'.format(*symbol_val)
_print_success_message()
def test_get_inputs(get_inputs):
with tf.Graph().as_default():
input_data, targets, lr = get_inputs()
# Check type
assert input_data.op.type == 'Placeholder',\
'Input not a Placeholder.'
assert targets.op.type == 'Placeholder',\
'Targets not a Placeholder.'
assert lr.op.type == 'Placeholder',\
'Learning Rate not a Placeholder.'
# Check name
assert input_data.name == 'input:0',\
'Input has bad name. Found name {}'.format(input_data.name)
# Check rank
input_rank = 0 if input_data.get_shape() == None else len(input_data.get_shape())
targets_rank = 0 if targets.get_shape() == None else len(targets.get_shape())
lr_rank = 0 if lr.get_shape() == None else len(lr.get_shape())
assert input_rank == 2,\
'Input has wrong rank. Rank {} found.'.format(input_rank)
assert targets_rank == 2,\
'Targets has wrong rank. Rank {} found.'.format(targets_rank)
assert lr_rank == 0,\
'Learning Rate has wrong rank. Rank {} found'.format(lr_rank)
_print_success_message()
def test_get_init_cell(get_init_cell):
with tf.Graph().as_default():
test_batch_size_ph = tf.placeholder(tf.int32)
test_rnn_size = 256
cell, init_state = get_init_cell(test_batch_size_ph, test_rnn_size)
# Check type
assert isinstance(cell, tf.contrib.rnn.MultiRNNCell),\
'Cell is wrong type. Found {} type'.format(type(cell))
# Check for name attribute
assert hasattr(init_state, 'name'),\
'Initial state doesn\'t have the "name" attribute. Try using `tf.identity` to set the name.'
# Check name
assert init_state.name == 'initial_state:0',\
'Initial state doesn\'t have the correct name. Found the name {}'.format(init_state.name)
_print_success_message()
def test_get_embed(get_embed):
with tf.Graph().as_default():
embed_shape = [50, 5, 256]
test_input_data = tf.placeholder(tf.int32, embed_shape[:2])
test_vocab_size = 27
test_embed_dim = embed_shape[2]
embed = get_embed(test_input_data, test_vocab_size, test_embed_dim)
# Check shape
assert embed.shape == embed_shape,\
'Wrong shape. Found shape {}'.format(embed.shape)
_print_success_message()
def test_build_rnn(build_rnn):
with tf.Graph().as_default():
test_rnn_size = 256
test_rnn_layer_size = 2
test_cell = rnn.MultiRNNCell([rnn.BasicLSTMCell(test_rnn_size)] * test_rnn_layer_size)
test_inputs = tf.placeholder(tf.float32, [None, None, test_rnn_size])
outputs, final_state = build_rnn(test_cell, test_inputs)
# Check name
assert hasattr(final_state, 'name'),\
'Final state doesn\'t have the "name" attribute. Try using `tf.identity` to set the name.'
assert final_state.name == 'final_state:0',\
'Final state doesn\'t have the correct name. Found the name {}'.format(final_state.name)
# Check shape
assert outputs.get_shape().as_list() == [None, None, test_rnn_size],\
'Outputs has wrong shape. Found shape {}'.format(outputs.get_shape())
assert final_state.get_shape().as_list() == [test_rnn_layer_size, 2, None, test_rnn_size],\
'Final state wrong shape. Found shape {}'.format(final_state.get_shape())
_print_success_message()
def test_build_nn(build_nn):
with tf.Graph().as_default():
test_input_data_shape = [128, 5]
test_input_data = tf.placeholder(tf.int32, test_input_data_shape)
test_rnn_size = 256
test_embed_dim = 300
test_rnn_layer_size = 2
test_vocab_size = 27
test_cell = rnn.MultiRNNCell([rnn.BasicLSTMCell(test_rnn_size)] * test_rnn_layer_size)
logits, final_state = build_nn(test_cell, test_rnn_size, test_input_data, test_vocab_size, test_embed_dim)
# Check name
assert hasattr(final_state, 'name'), \
'Final state doesn\'t have the "name" attribute. Are you using build_rnn?'
assert final_state.name == 'final_state:0', \
'Final state doesn\'t have the correct name. Found the name {}. Are you using build_rnn?'.format(final_state.name)
# Check Shape
assert logits.get_shape().as_list() == test_input_data_shape + [test_vocab_size], \
'Outputs has wrong shape. Found shape {}'.format(logits.get_shape())
assert final_state.get_shape().as_list() == [test_rnn_layer_size, 2, None, test_rnn_size], \
'Final state wrong shape. Found shape {}'.format(final_state.get_shape())
_print_success_message()
def test_get_tensors(get_tensors):
test_graph = tf.Graph()
with test_graph.as_default():
test_input = tf.placeholder(tf.int32, name='input')
test_initial_state = tf.placeholder(tf.int32, name='initial_state')
test_final_state = tf.placeholder(tf.int32, name='final_state')
test_probs = tf.placeholder(tf.float32, name='probs')
input_text, initial_state, final_state, probs = get_tensors(test_graph)
# Check correct tensor
assert input_text == test_input,\
'Test input is wrong tensor'
assert initial_state == test_initial_state, \
'Initial state is wrong tensor'
assert final_state == test_final_state, \
'Final state is wrong tensor'
assert probs == test_probs, \
'Probabilities is wrong tensor'
_print_success_message()
def test_pick_word(pick_word):
with tf.Graph().as_default():
test_probabilities = np.array([0.1, 0.8, 0.05, 0.05])
test_int_to_vocab = {word_i: word for word_i, word in enumerate(['this', 'is', 'a', 'test'])}
pred_word = pick_word(test_probabilities, test_int_to_vocab)
# Check type
assert isinstance(pred_word, str),\
'Predicted word is wrong type. Found {} type.'.format(type(pred_word))
# Check word is from vocab
assert pred_word in test_int_to_vocab.values(),\
'Predicted word not found in int_to_vocab.'
_print_success_message()