class TestRNNLM(unittest.TestCase):
def setUp(self):
text = 'You said good-bye and I said hello.'
cbm = CountBasedMethod()
word_list = cbm.text_to_word_list(text)
word_to_id, *_ = cbm.preprocess(word_list)
vocab_size = len(word_to_id)
wordvec_size = 100
hidden_size = 100
self.rnnlm = RNNLM(vocab_size, wordvec_size, hidden_size)
self.xs = np.array([
[0, 4, 4, 1],
[4, 0, 2, 1]
])
self.ts = np.array([
[0, 1, 0, 0],
[0, 0, 0, 1]
])
def test_predict(self):
score = self.rnnlm._predict(self.xs)
self.assertEqual((2, 4, 7), score.shape)
def test_forward(self):
loss = self.rnnlm.forward(self.xs, self.ts)
self.assertEqual(1.94, round(loss, 2))
def test_backward(self):
self.rnnlm.forward(self.xs, self.ts)
dout = self.rnnlm.backward()
self.assertEqual(None, dout)
def test_reset_state(self):
self.rnnlm.forward(self.xs, self.ts)
self.rnnlm.backward()
self.assertEqual((2, 100), self.rnnlm.lstm_layer.h.shape)
self.rnnlm.reset_state()
self.assertEqual(None, self.rnnlm.lstm_layer.h)
def test_save_params(self):
self.rnnlm.forward(self.xs, self.ts)
self.rnnlm.backward()
self.rnnlm.save_params()
self.assertEqual(True, path.exists('../pkl/rnnlm.pkl'))
def test_load_params(self):
self.rnnlm.load_params()
a, b, c, d, e, f = self.rnnlm.params
self.assertEqual((7, 100), a.shape)
self.assertEqual((100, 400), b.shape)
self.assertEqual((100, 400), c.shape)
self.assertEqual((400,), d.shape)
self.assertEqual((100, 7), e.shape)
self.assertEqual((7,), f.shape)
from rnnlm import RNNLM
from better_rnnlm import BetterRNNLM
from datasets import ptb
from commons.util import eval_perplexity
if __name__ == '__main__':
# select model for evaluation
model = RNNLM()
# model = BetterRNNLM()
# read tunned params
model.load_params()
corpus, _, _ = ptb.load_data('test')
model.reset_state()
ppl_test = eval_perplexity(model, corpus)
print('Test Perplexity:', ppl_test)