class TestSimpleCBOW(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, _, self.corpus = cbm.preprocess(word_list)
self.vocab_size = len(word_to_id)
hidden_size = 3
self.simple_cbow = SimpleCBOW(self.vocab_size, hidden_size)
self.simple_word2vec = SimpleWord2Vec()
self.contexts_array, self.target_array = self.simple_word2vec.create_contexts_target(
self.corpus)
self.contexts = self.simple_word2vec.convert_to_one_hot(
self.contexts_array, self.vocab_size)
self.target = self.simple_word2vec.convert_to_one_hot(
self.target_array, self.vocab_size)
def test_forward(self):
loss = self.simple_cbow.forward(self.contexts, self.target)
self.assertEqual(1.946, round(loss, 3))
def test_grads_diff(self):
before_in_grads_0, = self.simple_cbow.in_layer_0.grads
before_in_grads_0 = copy.copy(before_in_grads_0)
before_in_grads_1, = self.simple_cbow.in_layer_1.grads
before_in_grads_1 = copy.copy(before_in_grads_1)
before_out_grads, = self.simple_cbow.out_layer.grads
before_out_grads = copy.copy(before_out_grads)
self.simple_cbow.forward(self.contexts, self.target)
self.simple_cbow.backward()
after_in_grads_0, = self.simple_cbow.in_layer_0.grads
after_in_grads_1, = self.simple_cbow.in_layer_1.grads
after_out_grads, = self.simple_cbow.out_layer.grads
in_grads_0 = before_in_grads_0 == after_in_grads_0
in_grads_1 = before_in_grads_1 == after_in_grads_1
out_grads = before_out_grads == after_out_grads
assert_array_equal(
np.array([[False, False, False], [False, False, False],
[False, False, False], [False, False, False],
[False, False, False], [True, True, True],
[True, True, True]]), in_grads_0)
assert_array_equal(
np.array([[True, True, True], [False, False, False],
[False, False, False], [False, False, False],
[False, False, False], [False, False, False],
[False, False, False]]), in_grads_1)
assert_array_equal(
np.array([[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]]),
out_grads)
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, _, self.corpus = cbm.preprocess(word_list)
self.vocab_size = len(word_to_id)
hidden_size = 3
self.simple_cbow = SimpleCBOW(self.vocab_size, hidden_size)
self.simple_word2vec = SimpleWord2Vec()
self.contexts_array, self.target_array = self.simple_word2vec.create_contexts_target(
self.corpus)
self.contexts = self.simple_word2vec.convert_to_one_hot(
self.contexts_array, self.vocab_size)
self.target = self.simple_word2vec.convert_to_one_hot(
self.target_array, self.vocab_size)
def main():
window_size = 1
hidden_size = 5
batch_size = 3
max_epoch = 1000
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
vocab_size = len(word_to_id)
contexts, target = create_context_target(corpus, window_size)
one_hot_target = convert_one_hot(target, vocab_size)
one_hot_contexts = convert_one_hot(contexts, vocab_size)
model = SimpleCBOW(vocab_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
trainer.fit(one_hot_contexts, one_hot_target, max_epoch, batch_size)
# trainer.plot()
word_vecs = model.word_vecs
for word_id, word in id_to_word.items():
print(word, word_vecs[word_id])
print('DONE')
def main():
window_size = 1
hidden_size = 5
batch_size = 3
max_epoch = 1000
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
vocab_size = len(word_to_id)
contexts, target = create_contexts_target(corpus, window_size)
target = convert_one_hot(target, vocab_size)
contexts = convert_one_hot(contexts, vocab_size)
model = SimpleCBOW(vocab_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
trainer.fit(contexts, target, max_epoch, batch_size)
trainer.plot()
sys.path.append(
'/home/hiromasa/deep-learning-from-scratch-2') # 親ディレクトリのファイルをインポートするための設定
from common.trainer import Trainer
from common.optimizer import Adam
from simple_cbow import SimpleCBOW
from common.util import preprocess, create_contexts_target, convert_one_hot
window_size = 1
hidden_size = 5
batch_size = 3
max_epoch = 1000
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
vocab_size = len(word_to_id)
contexts, target = create_contexts_target(corpus, window_size)
target = convert_one_hot(target, vocab_size)
contexts = convert_one_hot(contexts, vocab_size)
model = SimpleCBOW(vocab_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
trainer.fit(contexts, target, max_epoch, batch_size)
trainer.plot()
word_vecs = model.word_vecs
for word_id, word in id_to_word.items():
print(word, word_vecs[word_id])
[[[1 0 0 0 0 0 0]
[0 0 1 0 0 0 0]]
<BLANKLINE>
[[0 1 0 0 0 0 0]
[0 0 0 1 0 0 0]]
<BLANKLINE>
[[0 0 1 0 0 0 0]
[0 0 0 0 1 0 0]]
<BLANKLINE>
[[0 0 0 1 0 0 0]
[0 1 0 0 0 0 0]]
<BLANKLINE>
[[0 0 0 0 1 0 0]
[0 0 0 0 0 1 0]]
<BLANKLINE>
[[0 1 0 0 0 0 0]
[0 0 0 0 0 0 1]]]
"""
print(contexts_one_hot)
model = SimpleCBOW(vocabulary_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
trainer.fit(contexts_one_hot, target_one_hot, max_epoch, batch_size)
trainer.plot()
word_vecs = model.word_vecs
for word_id, word in id_to_word.items():
print(word, word_vecs[word_id])
# coding: utf-8
import sys
sys.path.append('..')
import numpy as np
from simple_cbow import SimpleCBOW
from utils.layers import MatMul
from utils.tools import create_contexts_target, preprocess, convert_one_hot
if __name__ == '__main__':
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
# print(corpus) # [0 1 2 3 4 1 5 6]
# {'you': 0, 'say': 1, 'goodbye': 2, 'and': 3, 'i': 4, 'hello': 5, '.': 6}
# print(word_to_id)
# {0: 'you', 1: 'say', 2: 'goodbye', 3: 'and', 4: 'i', 5: 'hello', 6: '.'}
# print(corpus[1:-1]) # [1 2 3 4 1 5]
contexts, target = create_contexts_target(corpus)
contexts = convert_one_hot(contexts, len(word_to_id))
target = convert_one_hot(target, len(word_to_id))
# print(contexts) [[0 2][1 3][2 4][3 1][4 5][1 6]]
# print(contexts[:, 1]) [2 3 4 1 5 6]
print(contexts.shape)
print(
np.dot(contexts[:, 1],
0.01 * np.random.randn(len(word_to_id), 5).astype('f')))
model = SimpleCBOW(vocab_size, hidden_size)
model.forward(contexts, target)
model.forward(contexts, target)
import sys
sys.path.append('..')
from common.trainer import trainer
from common.optimizer import Adam
from simple_cbow import SimpleCBOW
from common.util import preprocess, create_contexts_target, convert_one_hot
window_size = 1
hidden_size = 5
batch_size = 3
max_epoch = 1000
text = 'You say goodbye and I say hello.'
corpus, word_to_id, id_to_word = preprocess(text)
vocab_size = len(word_to_id)
contexts, target = create_contexts_target(corpus, window_size)
target = convert_one_hot(target, vocab_size)
contexts = convert_one_hot(contexts, vocab_size)
model = SimpleCBOW(vocab_size, vocab_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
trainer.fit(contexts, target, max_epoch, batch_size)
trainer.plot()