def test_character_level_from_text():
test_strs = ["All work and no play makes jack a dull boy!@#**-~`", ""]
# Make sure that it raises if it gets the wrong input
assert_raises(ValueError, make_character_level_from_text, test_strs[0])
assert_raises(ValueError, make_character_level_from_text, test_strs[0][0])
clean, mf, imf, m = make_character_level_from_text(test_strs)
if len(clean) != len(test_strs) - 1:
raise AssertionError("Failed to remove empty line")
new_str = "zzzzzzzzzzzzzz"
new_clean = mf(new_str)
# Make sure all the unknown chars get UNK tags
if sum(new_clean[:-1]) != len(new_clean[:-1]) * m["UNK"]:
raise AssertionError("Failed to handle unknown char")
# Make sure last tag is EOS
if new_clean[-1] != m["EOS"]:
raise AssertionError("Failed to add EOS tag")
def test_character_level_from_text():
test_strs = ["All work and no play makes jack a dull boy!@#**-~`",
""]
# Make sure that it raises if it gets the wrong input
assert_raises(ValueError, make_character_level_from_text, test_strs[0])
assert_raises(ValueError, make_character_level_from_text, test_strs[0][0])
clean, mf, imf, m = make_character_level_from_text(test_strs)
if len(clean) != len(test_strs) - 1:
raise AssertionError("Failed to remove empty line")
new_str = "zzzzzzzzzzzzzz"
new_clean = mf(new_str)
# Make sure all the unknown chars get UNK tags
if sum(new_clean[:-1]) != len(new_clean[:-1]) * m["UNK"]:
raise AssertionError("Failed to handle unknown char")
# Make sure last tag is EOS
if new_clean[-1] != m["EOS"]:
raise AssertionError("Failed to add EOS tag")
from dagbldr.nodes import masked_cost, categorical_crossentropy
from dagbldr.nodes import softmax_layer, shift_layer
from dagbldr.nodes import gru_recurrent_layer, conditional_gru_recurrent_layer
from dagbldr.nodes import bidirectional_gru_recurrent_layer
from dagbldr.nodes import conditional_attention_gru_recurrent_layer
# minibatch size
minibatch_size = 10
# Get data for lovecraft experiments
mountains = load_mountains()
text = mountains["data"]
# Get a tiny subset
text = text[:10]
cleaned, mfunc, inv_mfunc, mapper = make_character_level_from_text(text)
n_chars = len(mapper.keys())
# Necessary setup since text is done on per minibatch basis
text_minibatch_func = gen_make_list_one_hot_minibatch(n_chars)
X = [l[:3] for l in cleaned]
y = [l[3:5] for l in cleaned]
X_mb, X_mask = text_minibatch_func(X, slice(0, minibatch_size))
y_mb, y_mask = text_minibatch_func(y, slice(0, minibatch_size))
def test_conditional_gru_recurrent():
random_state = np.random.RandomState(1999)
graph = OrderedDict()
n_hid = 5
n_out = n_chars
from dagbldr.nodes import masked_cost, categorical_crossentropy
from dagbldr.nodes import softmax_layer, shift_layer
from dagbldr.nodes import gru_recurrent_layer, conditional_gru_recurrent_layer
from dagbldr.nodes import bidirectional_gru_recurrent_layer
from dagbldr.nodes import conditional_attention_gru_recurrent_layer
# minibatch size
minibatch_size = 10
# Get data for lovecraft experiments
mountains = load_mountains()
text = mountains["data"]
# Get a tiny subset
text = text[:10]
cleaned, mfunc, inv_mfunc, mapper = make_character_level_from_text(text)
n_chars = len(mapper.keys())
# Necessary setup since text is done on per minibatch basis
text_minibatch_func = gen_make_list_one_hot_minibatch(n_chars)
X = [l[:3] for l in cleaned]
y = [l[3:5] for l in cleaned]
X_mb, X_mask = text_minibatch_func(X, slice(0, minibatch_size))
y_mb, y_mask = text_minibatch_func(y, slice(0, minibatch_size))
def test_conditional_gru_recurrent():
random_state = np.random.RandomState(1999)
graph = OrderedDict()
n_hid = 5
n_out = n_chars
