def main():
startime = time.time()
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
# Set hyper-parameters.
batch_size = 32
epochs = 100
model_path = 'models/model.h5'
enc_arch = 'models/encoder.json'
dec_arch = 'models/decoder.json'
data_path = 'data/jpn.txt'
num_words = 10000
num_data = 20000
# Data loading.
print(return_time(startime), "1. Loading data ...")
en_texts, ja_texts = load_dataset(data_path)
en_texts, ja_texts = en_texts[:num_data], ja_texts[:num_data]
# Preprocessings.
print(return_time(startime), "2. Preprocessing dataset ...")
ja_texts = preprocess_ja(ja_texts)
ja_texts = preprocess_dataset(ja_texts)
en_texts = preprocess_dataset(en_texts)
x_train, x_test, y_train, y_test = train_test_split(en_texts,
ja_texts,
test_size=0.2,
random_state=42)
en_vocab = build_vocabulary(x_train, num_words)
ja_vocab = build_vocabulary(y_train, num_words)
x_train, y_train = create_dataset(x_train, y_train, en_vocab, ja_vocab)
# Build a simple model.
print(return_time(startime), "3. Build model ...")
encoder = Encoder(num_words)
decoder = Decoder(num_words)
# Build an attention model.
# encoder = Encoder(num_words, return_sequences=True)
# decoder = AttentionDecoder(num_words)
seq2seq = Seq2seq(encoder, decoder)
model = seq2seq.build()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
# Train the model.
print(return_time(startime), "4. Start training ...")
callbacks = [
EarlyStopping(patience=3),
ModelCheckpoint(model_path,
save_best_only=True,
save_weights_only=True)
]
model.fit(x=x_train,
y=y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks,
validation_split=0.1)
encoder.save_as_json(enc_arch)
decoder.save_as_json(dec_arch)
# Inference.
print(return_time(startime), "5. Evaluation")
print("***********************************")
encoder = Encoder.load(enc_arch, model_path)
decoder = Decoder.load(dec_arch, model_path)
api = InferenceAPI(encoder, decoder, en_vocab, ja_vocab)
# api = InferenceAPIforAttention(encoder, decoder, en_vocab, ja_vocab)
texts = sorted(set(en_texts[:50]), key=len)
for text in texts:
decoded = api.predict(text=text)
print('English : {}'.format(text))
print('Japanese: {}'.format(decoded))
print()
print(return_time(startime), "6. Calculating BLEU score ...")
y_test = [y.split(' ')[1:-1] for y in y_test]
bleu_score = evaluate_bleu(x_test, y_test, api)
print('BLEU: {}'.format(bleu_score))
print(return_time(startime), "7. Finished!")
def main():
# Set hyper-parameters.
batch_size = 32
epochs = 100
model_path = 'atmodel.h5'
enc_arch = 'encoder.json'
dec_arch = 'decoder.json'
data_path = '../data/w16to19hukusimaconv.txt'
num_words = 7000
num_data = 4367
# Data loading.
en_texts, ja_texts = load_dataset(data_path)
en_texts, ja_texts = en_texts[:num_data], ja_texts[:num_data]
# Preprocessings.
#ja_texts = preprocess_ja(ja_texts)
ja_texts = preprocess_dataset(ja_texts)
en_texts = preprocess_dataset(en_texts)
x_train, x_test, y_train, y_test = train_test_split(en_texts,
ja_texts,
test_size=0.2,
random_state=42)
en_vocab = build_vocabulary(x_train, num_words)
ja_vocab = build_vocabulary(y_train, num_words)
print(x_train[:3])
print(y_train[:3])
x_train, y_train = create_dataset(x_train, y_train, en_vocab, ja_vocab)
print(en_vocab.word_index)
print(ja_vocab.word_index)
# Build a simple model.
encoder = Encoder(num_words)
decoder = Decoder(num_words)
# Build an attention model.
#encoder = Encoder(num_words, return_sequences=True)
#decoder = AttentionDecoder(num_words)
seq2seq = Seq2seq(encoder, decoder)
model = seq2seq.build()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
# Train the model.
callbacks = [
EarlyStopping(patience=10),
ModelCheckpoint(model_path,
save_best_only=True,
save_weights_only=True)
]
"""
model.fit(x=x_train,
y=y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks,
validation_split=0.1)"""
encoder.save_as_json(enc_arch)
decoder.save_as_json(dec_arch)
# Inference.
encoder = Encoder.load(enc_arch, model_path)
decoder = Decoder.load(dec_arch, model_path)
api = InferenceAPI(encoder, decoder, en_vocab, ja_vocab)
#api = InferenceAPIforAttention(encoder, decoder, en_vocab, ja_vocab)
texts = sorted(set(en_texts[:50]), key=len)
texts = ["お聞きしたいと思います", "さっき の 答弁 全く 納得 できません", "全く 納得 い き ません", "ありがとうございました", "おはようございます",\
"よろしいでしょうか", "是非 よろしくお願いいたします", "もう少し 具体的に 教えて いただける と 助 か る んですけれども", "ちょっと 待 って", "質問 主 意 書 では 当然 混 同 は しておりません",\
"正 式 な 要求 でいい んですか", "時間ですので まとめて ください", "ちょっと 静粛に お願いします", "よろしいですか", "静粛に お願いします",\
"答弁 を まとめて ください", "時間 ですから", "驚 き の答弁 ですね", "それは いつ ごろ でしょうか", "そのとおり です"
]
for text in texts:
decoded = api.predict(text=text)
print('入力: {}'.format(text))
print('応答: {}'.format(decoded))
y_test = [y.split(' ')[1:-1] for y in y_test]
bleu_score = evaluate_bleu(x_test, y_test, api)
print('BLEU: {}'.format(bleu_score))
def main():
# ハイパーパラメータの設定
batch_size = 32
epochs = 100
model_path = 'models/simple_model.h5'
enc_arch = 'models/encoder.json'
dec_arch = 'models/decoder.json'
data_path = 'data/jpn.txt'
num_words = 10000
num_data = 20000
# データ・セット読み込み
en_texts, ja_texts = load_dataset(data_path)
en_texts, ja_texts = en_texts[:num_data], ja_texts[:num_data]
# データ・セットの前処理
ja_texts = preprocess_ja(ja_texts)
ja_texts = preprocess_dataset(ja_texts)
en_texts = preprocess_dataset(en_texts)
x_train, x_test, y_train, y_test = train_test_split(en_texts,
ja_texts,
test_size=0.2,
random_state=42)
en_vocab = build_vocabulary(x_train, num_words)
ja_vocab = build_vocabulary(y_train, num_words)
x_train, y_train = create_dataset(x_train, y_train, en_vocab, ja_vocab)
# モデルの構築
encoder = Encoder(num_words)
decoder = Decoder(num_words)
seq2seq = Seq2seq(encoder, decoder)
model = seq2seq.build()
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
# コールバックの用意
callbacks = [
EarlyStopping(patience=3),
ModelCheckpoint(model_path,
save_best_only=True,
save_weights_only=True)
]
# モデルの学習
model.fit(x=x_train,
y=y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks,
validation_split=0.1)
encoder.save_as_json(enc_arch)
decoder.save_as_json(dec_arch)
# 予測
encoder = Encoder.load(enc_arch, model_path)
decoder = Decoder.load(dec_arch, model_path)
api = InferenceAPI(encoder, decoder, en_vocab, ja_vocab)
texts = sorted(set(en_texts[:50]), key=len)
for text in texts:
decoded = api.predict(text=text)
print('English : {}'.format(text))
print('Japanese: {}'.format(decoded))
# 性能評価
y_test = [y.split(' ')[1:-1] for y in y_test]
bleu_score = evaluate_bleu(x_test, y_test, api)
print('BLEU: {}'.format(bleu_score))
