def test_simple_long(lstm_kwargs_1):
model = lstm.LSTMModel('test-simple-long',
2,
use_long=True,
**lstm_kwargs_1)
model.train([[1, 0] * 6000])
s = model.sample([1])
assert isinstance(s, list)
print(s)
model.analyze([1, 0])
model.save_to_file()
model.sample([1])
model = lstm.LSTMModelFromFile('test-simple-long', **lstm_kwargs_1)
s = model.sample([1])
assert isinstance(s, list)
print(s)
model.analyze([1, 0])
model.train([[1, 0] * 6000])
model.sample([1])
model.analyze([1, 0])
model.save_to_file()
model.train([[1, 0]])
model.train([[1, 0] * 6000], count=True)
def test_simple_long_skip_padding(lstm_kwargs_1):
model = lstm.LSTMModel('test-simple-long',
2,
use_long=True,
skip_padding=True,
**lstm_kwargs_1)
model.train([[1, 0] * 1000])
model.train([[1, 0]])
model.save_to_file()
model = lstm.LSTMModelFromFile('test-simple-long', **lstm_kwargs_1)
model.train([[1, 0] * 1000])
model.train([[1, 0]])
model.save_to_file()
def test_simple_short(lstm_kwargs_1, lstm_kwargs_2):
model = lstm.LSTMModel('test-simple', 2, **lstm_kwargs_1)
model.train([[1, 0]] * 1000)
s = model.sample()
assert isinstance(s, list)
print(s)
model.analyze([1, 0])
model.save_to_file()
model.sample([1])
model = lstm.LSTMModelFromFile('test-simple', **lstm_kwargs_1)
s = model.sample()
assert isinstance(s, list)
print(s)
model.analyze([1, 0])
model.train([[1, 0]] * 1000)
model.sample()
model.analyze([1, 0])
model.save_to_file()
# Double saving is ok
model.save_to_file()
model = lstm.LSTMModel('test-simple', 2, **lstm_kwargs_1)
model.train([[1, 0]] * 1000, autosave=30)
model = lstm.LSTMModel('test-simple', 2, **lstm_kwargs_2)
model.train([[1, 0]] * 1000, autosave=30)
model.train([[1, 0]] * 1000, autosave=True)
model = lstm.LSTMModel('test-simple', 2, **lstm_kwargs_2)
model.train([[1, 0]] * 1000, autosave=30, count=True)
model = lstm.LSTMModelFromFile('test-simple-copy',
from_name='test-simple',
training_steps=100,
**lstm_kwargs_1)
model.train([[1, 0]] * 1000, autosave=30)
def test_simple_string_short(lstm_kwargs_1):
model = lstm.LSTMModelString('test-simple-string', **lstm_kwargs_1)
model.train(['ab'] * 50)
s = model.sample()
assert tools.is_string_or_bytes(s)
print(s)
model.analyze('ab')
model.save_to_file()
model.sample('a')
model = lstm.LSTMModelFromFile('test-simple-string', **lstm_kwargs_1)
s = model.sample()
assert tools.is_string_or_bytes(s)
print(s)
model.analyze('ab')
model.train(['ab'] * 50)
model.sample()
model.analyze('ab')
model.save_to_file()
def test_simple_alphabet_short(lstm_kwargs_1):
model = lstm.LSTMModelAlphabet('test-simple-alphabet', ['hey', 'yo'],
**lstm_kwargs_1)
model.train([['hey', 'yo']] * 50)
s = model.sample()
assert isinstance(s, list)
print(s)
model.analyze(['hey', 'yo'])
model.save_to_file()
model.sample(['hey'])
model = lstm.LSTMModelFromFile('test-simple-alphabet', **lstm_kwargs_1)
s = model.sample()
assert isinstance(s, list)
print(s)
model.analyze(['hey', 'yo'])
model.train([['hey', 'yo']] * 50)
model.sample()
model.analyze(['hey', 'yo'])
model.save_to_file()
def test_text_file_alphabet_file(lstm_kwargs_1):
path = os.path.join(config.ROOT_DIR, 'tests', 'small.txt')
model = lstm.LSTMModelTextFileAlphabetFile('test-text-file-alphabet-file',
path, **lstm_kwargs_1)
model.train([path])
s = model.sample('a')
assert tools.is_string_or_bytes(s)
print(s)
model.analyze('ab')
model.save_to_file()
model = lstm.LSTMModelFromFile('test-text-file-alphabet-file',
**lstm_kwargs_1)
s = model.sample('a')
assert tools.is_string_or_bytes(s)
print(s)
model.analyze('ab')
model.train([path])
model.sample('a')
model.analyze('ab')
model.save_to_file()