def main():
BATCH_SIZE = 128
X_indices, Y_indices, X_char2idx, Y_char2idx, X_idx2char, Y_idx2char = preprocess_data(
)
X_train = X_indices[BATCH_SIZE:]
Y_train = Y_indices[BATCH_SIZE:]
X_test = X_indices[:BATCH_SIZE]
Y_test = Y_indices[:BATCH_SIZE]
model = Seq2Seq(
rnn_size=50,
n_layers=1,
X_word2idx=X_char2idx,
encoder_embedding_dim=128,
Y_word2idx=Y_char2idx,
decoder_embedding_dim=128,
)
model.fit(X_train,
Y_train,
val_data=(X_test, Y_test),
batch_size=BATCH_SIZE,
n_epoch=10,
display_step=32)
# model.infer('今朝有酒今朝醉', X_idx2char, Y_idx2char)
model.infer('你好', X_idx2char, Y_idx2char)
# model.infer('雪消狮子瘦', X_idx2char, Y_idx2char)
model.infer('晚上吃什么', X_idx2char, Y_idx2char)
# model.infer('生员里长，打里长不打生员。', X_idx2char, Y_idx2char)
model.infer('我没什么意见', X_idx2char, Y_idx2char)
def main(args):
batch_size = args.batch_size
print args
# if args.predict:
# assert args.model_dir, 'model directory must be specified when predicting'
x_train, y_train, x_valid, y_valid, x_word2idx, y_word2idx, x_idx2word, y_idx2word = \
preprocess_data(args.input, args.validation, args.vocabulary)
model = Seq2Seq(rnn_size=args.hidden_size,
n_layers=args.n_layers,
x_word2idx=x_word2idx,
encoder_embedding_dim=args.hidden_size,
y_word2idx=y_word2idx,
decoder_embedding_dim=args.hidden_size,
model_path=args.model_path)
if not args.predict:
model.build_graph()
print 'Training ...'
model.fit(x_train,
y_train,
val_data=(x_valid, y_valid),
batch_size=batch_size,
n_epoch=args.n_epoch)
else:
print 'Loading pre-trained model ...'
model.restore_graph()
print 'Translating ...'
model.infer_sentence(u'我 的 青蛙 叫 呱呱 ！', x_idx2word, y_idx2word)
model.infer_sentence(u'我 非常 期待 它 带 礼物 回来 !', x_idx2word, y_idx2word)
def main():
config = model_config.Config()
BATCH_SIZE = config.batch_size
(X_indices, Y_indices), (X_char2idx, Y_char2idx), (X_idx2char, Y_idx2char) = \
data_utils.load_preprocess(config.source_path, config.target_path)
X_train = X_indices[BATCH_SIZE:]
Y_train = Y_indices[BATCH_SIZE:]
X_test = X_indices[:BATCH_SIZE]
Y_test = Y_indices[:BATCH_SIZE]
model = Seq2Seq(
rnn_size=config.num_units,
n_layers=config.num_layers,
X_word2idx=X_char2idx,
encoder_embedding_dim=config.encoding_embedding_size,
Y_word2idx=Y_char2idx,
decoder_embedding_dim=config.decoding_embedding_size,
)
model.fit(X_train,
Y_train,
val_data=(X_test, Y_test),
batch_size=BATCH_SIZE,
n_epoch=config.num_epochs,
display_step=config.display_step)
model.save_model(config.save_path)
def main():
X_indices, Y_indices, X_char2idx, Y_char2idx, X_idx2char, Y_idx2char = preprocess_data()
model = Seq2Seq(
rnn_size = 50,
n_layers = 2,
X_word2idx = X_char2idx,
Y_word2idx = Y_char2idx)
model.fit(X_indices, Y_indices)
model.infer('common', X_idx2char, Y_idx2char)
model.infer('apple', X_idx2char, Y_idx2char)
model.infer('zhedong', X_idx2char, Y_idx2char)
def main():
_, (X_char2idx, Y_char2idx), (X_idx2char, Y_idx2char) = \
data_utils.load_preprocess(config.source_path, config.target_path)
model = Seq2Seq(rnn_size=config.num_units,
n_layers=config.num_layers,
X_word2idx=X_char2idx,
encoder_embedding_dim=config.encoding_embedding_size,
Y_word2idx=Y_char2idx,
decoder_embedding_dim=config.decoding_embedding_size,
load_path=config.save_path)
model.infer('今朝有酒今朝醉', X_idx2char, Y_idx2char)
# model.infer('你好', X_idx2char, Y_idx2char)
model.infer('雪消狮子瘦', X_idx2char, Y_idx2char)
# model.infer('晚上吃什么', X_idx2char, Y_idx2char)
model.infer('生员里长，打里长不打生员。', X_idx2char, Y_idx2char)
def main():
BATCH_SIZE = 128
X_indices, Y_indices, X_char2idx, Y_char2idx, X_idx2char, Y_idx2char = preprocess_data(
)
X_train = X_indices[BATCH_SIZE:]
Y_train = Y_indices[BATCH_SIZE:]
X_test = X_indices[:BATCH_SIZE]
Y_test = Y_indices[:BATCH_SIZE]
model = Seq2Seq(
rnn_size=50,
n_layers=2,
X_word2idx=X_char2idx,
encoder_embedding_dim=15,
Y_word2idx=Y_char2idx,
decoder_embedding_dim=15,
batch_size=BATCH_SIZE,
)
model.fit(X_train, Y_train, val_data=(X_test, Y_test))
model.infer('common', X_idx2char, Y_idx2char)
model.infer('apple', X_idx2char, Y_idx2char)
model.infer('zhedong', X_idx2char, Y_idx2char)
def main():
batch_size = 128
X_indices, Y_indices, X_char2idx, Y_char2idx, X_idx2char, Y_idx2char = preprocess_data(
)
X_train = X_indices[batch_size:]
Y_train = Y_indices[batch_size:]
X_test = X_indices[:batch_size]
Y_test = Y_indices[:batch_size]
model = Seq2Seq(
rnn_size=50,
n_layers=2,
x_word2idx=X_char2idx,
encoder_embedding_dim=15,
y_word2idx=Y_char2idx,
decoder_embedding_dim=15,
)
model.fit(X_train,
Y_train,
val_data=(X_test, Y_test),
batch_size=batch_size)
model.infer('common', X_idx2char, Y_idx2char)
model.infer('apple', X_idx2char, Y_idx2char)
model.infer('zhedong', X_idx2char, Y_idx2char)
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from seq2seq_attn import Seq2Seq
from utils import translate_sentence #,calculate_bleu
from data import getData
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
SRC, TRG, train_data, valid_data, test_data = getData(False)
src_vocab_size = len(SRC)
trg_vocab_size = len(TRG)
SRC_PAD_IDX = SRC.stoi[SRC.pad_token]
TRG_EOS_TOKEN = SRC.stoi[SRC.eos_token]
model = Seq2Seq(SRC_PAD_IDX, src_vocab_size, trg_vocab_size, device,
TRG_EOS_TOKEN).to(device)
model.load_state_dict(torch.load('tut4-model.pt'))
src = "ein pferd geht unter einer brücke neben einem boot ."
translation, attention = translate_sentence(model, src, SRC, TRG, device)
print(src)
print(translation)
#exit()
def display_attention(sentence, translation, attention):
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)