def main():
train_file_dir = os.getcwd() + m_config.train_file
valid_file_dir = os.getcwd() + m_config.valid_file
test_file_dir = os.getcwd() + m_config.test_file
data_train_reader_type = RawStringDatasetReader(train_file_dir)
itera_x_train = data_train_reader_type.itera_x
itera_y_train = data_train_reader_type.itera_y
data_valid_reader_type = RawStringDatasetReader(valid_file_dir)
itera_x_valid = data_valid_reader_type.itera_x
itera_y_valid = data_valid_reader_type.itera_y
data_test_reader_type = RawStringDatasetReader(test_file_dir)
itera_x_test = data_test_reader_type.itera_x
itera_y_test = data_test_reader_type.itera_y
train_model = LanguageModel()
k_model,ppl = train_model.build_lm_model(itera_x_train, itera_y_train)
valid_loss = k_model.evaluate(x=itera_x_valid, y=itera_y_valid,steps=m_config.valid_steps)[0]
test_loss = k_model.evaluate(x=itera_x_test, y=itera_y_test,steps=m_config.test_steps)[0]
print("trainning's ppl:", ppl)
print("valid's ppl:", math.exp(valid_loss))
print("test's ppl:", math.exp(test_loss))
dataset = Dataset(data_dir, num_words)
dataset.set_batch_size(batch_size)
dataset.set_seq_len(seq_len)
dataset.save('./checkpoints/')
params = {}
params['vocab_size'] = dataset.vocab_size
params['num_classes'] = dataset.vocab_size
params['batch_size'] = batch_size
params['seq_len'] = seq_len
params['hidden_dim'] = hidden_size
params['num_layers'] = num_layers
params['embed_size'] = embed_size
model = LanguageModel(params)
model.compile()
eval_softmax = 5
for epoch in range(num_epochs):
dataset.set_data_dir(data_dir)
dataset.set_batch_size(batch_size)
progbar = generic_utils.Progbar(dataset.token.document_count)
for X_batch, Y_batch in dataset:
t0 = time.time()
loss = model.train_on_batch(X_batch, Y_batch)
perp = np.exp(np.float32(loss))
t1 = time.time()
wps = np.round((batch_size * seq_len) / (t1 - t0))
progbar.add(len(X_batch),
values=[("loss", loss), ("perplexity", perp),
("words/sec", wps)])
if __name__ == "__main__":
uniqueService = UniqueService(APP_DBUS_NAME, APP_OBJECT_NAME)
app = QApplication(sys.argv)
tray_icon = SystemTrayIcon(
QIcon(os.path.join(get_parent_dir(__file__), "image", "trayicon.png")),
app)
tray_icon.show()
(constant.TRAYAREA_TOP,
constant.TRAYAREA_BOTTOM) = tray_icon.get_trayarea()
plugin = Plugin()
source_lang_model = LanguageModel()
dest_lang_model = LanguageModel()
word_engine_name = setting_config.get_translate_config("word_engine")
words_engine_name = setting_config.get_translate_config("words_engine")
translate_simple = imp.load_source(
"translate_simple",
plugin.get_plugin_file(word_engine_name)).Translate()
translate_long = imp.load_source(
"translate_long",
plugin.get_plugin_file(words_engine_name)).Translate()
word_translate_model = plugin.get_word_model(
setting_config.get_translate_config("src_lang"),
setting_config.get_translate_config("dst_lang"))
words_translate_model = plugin.get_words_model(
setting_config.get_translate_config("src_lang"),
from model import LanguageModel
import argparse
new = LanguageModel()
parser = argparse.ArgumentParser()
parser.add_argument("source", type=str, help="file to train the model")
args = parser.parse_args()
result = new.fit(args.source)
print(result)