def main():
# ハイパーパラメータの設定
window_size = 5
hidden_size = 100
batch_size = 100
max_epoch = 10
# データの読み込み
corpus, word_to_id, id_to_word = ptb.load_data('train')
vocab_size = len(word_to_id)
contexts, target = create_contexts_target(corpus, window_size)
# モデルなどの生成
model = CBOW(vocab_size, hidden_size, window_size, corpus)
optimizer = Adam()
trainer = Trainer(model, optimizer)
# 学習開始
trainer.fit(contexts, target, max_epoch, batch_size)
trainer.plot()
# 後ほど利用できるように、必要なデータを保存
word_vecs = model.word_vecs
params = {}
params['word_vecs'] = word_vecs.astype(np.float16)
params['word_to_id'] = word_to_id
params['id_to_word'] = id_to_word
pkl_file = 'cbow_params.pkl'
with open(pkl_file, 'wb') as f:
pickle.dump(params, f, -1)
def train_eval(x_train, x_test, is_peeky):
if is_peeky:
model = PeekySeq2seq(vocab_size, wordvec_size, hidden_size)
else:
model = Seq2seq(vocab_size, wordvec_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
acc_list = []
for epoch in range(max_epoch):
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model, question, correct, id_to_char,
verbose)
acc = float(correct_num) / len(x_test)
acc_list.append(acc)
print('val acc %.3f%%' % (acc * 100))
return acc_list
def main():
max_epoch = 300
batch_size = 30
hidden_size = 10
output_size = 3
learning_rate = 1.0
x, t = spiral.load_data()
model = TwoLayerNet(2, hidden_size, output_size)
optimizer = SGD(learning_rate)
trainer = Trainer(model, optimizer)
trainer.fit(x, t, max_epoch, batch_size)
trainer.plot()
def main():
# データセットの読み込み
(x_train, t_train), (x_test, t_test) = sequence.load_data('addition.txt')
char_to_id, id_to_char = sequence.get_vocab()
# 入力列を逆順にするとSeq2Se2の精度が上がるらしいが。。。クソ理論
is_reverse = True
if is_reverse:
x_train, x_test = x_train[:, ::-1], x_test[:, ::-1]
# ハイパーパラメータの設定
vocab_size = len(char_to_id)
wordvec_size = 16
hidden_size = 128
batch_size = 128
max_epoch = 25
max_grad = 5.0
# モデル/オプティマイザ/トレーナーの生成
# model = Seq2seq(vocab_size, wordvec_size, hidden_size)
model = PeekySeq2seq(vocab_size, wordvec_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
acc_list = []
for epoch in range(max_epoch):
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model, question, correct, id_to_char,
verbose)
acc = float(correct_num) / len(x_test)
acc_list.append(acc)
print(f'val acc {acc * 100}')
def main():
# データの読み込み
(x_train, t_train), (x_test, t_test) = sequence.load_data('date.txt')
char_to_id, id_to_char = sequence.get_vocab()
# 入力文を反転
x_train, x_test = x_train[:, ::-1], x_test[:, ::-1]
# ハイパーパラメータの設定
vocab_size = len(char_to_id)
wordvec_size = 16
hidden_size = 256
batch_size = 128
max_epoch = 10
max_grad = 5.0
model = AttentionSeq2seq(vocab_size, wordvec_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
acc_list = []
for epoch in range(max_epoch):
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model,
question,
correct,
id_to_char,
verbose,
is_reverse=True)
acc = float(correct_num) / len(x_test)
acc_list.append(acc)
print('val acc %.3f%%' % (acc * 100))
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()
def test_train_word2vec_model():
"""word2vecモデルの学習
"""
window_size = 1
hidden_size = 5 # 単語の分散表現ベクトルの次元数
batch_size = 3
max_epoch = 1000
text = 'You say goodbye and I say hello.'
# コーパスの作成
corpus, word_to_id, id_to_word = preprocess(text)
# コンテキストとターゲットの作成
vocab_size = len(word_to_id)
contexts, target = create_context_target(corpus, window_size)
target = convert_one_hot(target, vocab_size)
contexts = convert_one_hot(contexts, vocab_size)
print("one-hot target: ", target)
print("one-hot contexts: ", contexts)
# CBOWモデル
model = SimpleCBOW(vocab_size, hidden_size)
optimizer = Adam()
# trainer
trainer = Trainer(model, optimizer)
# 学習
trainer.fit(contexts, target, max_epoch=max_epoch, batch_size=batch_size)
trainer.plot()
# CBOWの重み(W_in)を取得する
word_vecs = model.word_vecs
for word_id, word in id_to_word.items():
print(word, word_vecs[word_id])
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, 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])
wordvec_size = 16
hidden_size = 256
batch_size = 128
max_epoch = 4
max_grad = 5.0
model = AttentionSeq2seq(vocab_size, wordvec_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
acc_list = []
for epoch in range(max_epoch):
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad)
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model,
question,
correct,
id_to_char,
verbose,
is_reverse=True)
acc = float(correct_num) / len(x_test)
x_test = pickle.load(x_test)
y_test = open(y_train_list[-1], "rb")
y_test = pickle.load(y_test)
x_test = x_test[0:30]
y_test = y_test[0:30]
for epoch in range(max_epoch):
# 分割したファイルを分けて開く
for i in range(3, len(x_train_list)):
x_train = open(x_train_list[i], "rb")
x_train = pickle.load(x_train)
y_train = open(y_train_list[i], "rb")
y_train = pickle.load(y_train)
for u in range(301):
trainer.fit(x_train[u * 100:(u + 1) * 100], y_train[u * 100:(u + 1) * 100], max_epoch=1, batch_size=batch_size, max_grad=max_grad)
if i%100==0:
model.save_params()
print(int(i / len(x_train_list)), "%", "completed...")
if i % 2 == 0:
# 念の為、訓練データのメモリ解放を行う
x_train = ""
y_train = ""
# テストデータに対する評価
x_test = open(x_train_list[-1], "rb")
x_test = pickle.load(x_test)
y_test = open(y_train_list[-1], "rb")
y_test = pickle.load(y_test)
x_test = x_test[0:30]
y_test = y_test[0:30]
correct_num = 0
import sys
sys.path.append('..')
from common.optimizer import SGD
from common.trainer import Trainer
from dataset import spiral
from two_layer_net import TwoLayerNet
# 하이퍼파라미터 설정
max_epoch = 300
batch_size = 30
hidden_size = 10
learning_rate = 1.0
x, t = spiral.load_data()
model = TwoLayerNet(input_size=2, hidden_size=hidden_size, output_size=3)
optimizer = SGD(lr=learning_rate)
trainer = Trainer(model, optimizer)
trainer.fit(x, t, max_epoch, batch_size, eval_interval=10)
trainer.plot()
# model = Seq2seq(vocab_size, wordvec_size, hidden_size)
model = PeekySeq2seq(vocab_size, wordvec_size, hidden_size)
# model = AttentionSeq2seq(vocab_size, wordvec_size, hidden_size)
# model = Seq2seqBiLSTM(vocab_size, wordvec_size, hidden_size)
# model = PeekySeq2seqBiLSTM(vocab_size, wordvec_size, hidden_size)
# model = AttentionSeq2seqBiLSTM(vocab_size, wordvec_size, hidden_size)
optimizer = Adam()
trainer = Trainer(model, optimizer)
# Train
print("Target dataset:", dataset_name)
trainer.fit(x_train,
t_train,
x_test,
t_test,
max_epoch=max_epoch,
batch_size=batch_size,
max_grad=max_grad)
# Inference
start_id = seq.start_id
sample_size = seq.sample_size
guess_train = model.generate(x_train, start_id, sample_size)
guess_test = model.generate(x_test, start_id, sample_size)
## 結果の保存
# 保存ファイルのファイル名生成
modelname = model.__class__.__name__
timestamp = datetime.now().strftime("_%y%m%d_%H%M")
save_dir = result_dir / (dataset_name + timestamp)
[[[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 = SimpleSkipGram(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])
num_encoders=1,
num_decoders=1)
if os.path.isfile("../pkl/myTransformer_params.pkl"):
model.load_params("../pkl/myTransformer_params.pkl")
optimizer = Adam(lr=0.00001)
# optimizer = SGD(lr=0.00005)
# optimizer = RMSprop(lr=0.00005)
trainer = Trainer(model, optimizer)
acc_list = []
for epoch in range(max_epoch):
trainer.fit(x_train,
t_train,
max_epoch=1,
batch_size=batch_size,
max_grad=max_grad,
eval_interval=10)
model.save_params('../pkl/myTransformer_params.pkl')
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_transformer(model,
question,
correct,
preprocessing.id_to_word,
preprocessing.word_to_id['<eos>'],
verbose,
is_reverse=False)
