def main():
parser = ArgumentParser()
parser.add_argument('mode',
choices=['train', 'predict'],
help='pipeline mode')
parser.add_argument('model',
choices=['rnn', 'cnn', 'multihead'],
help='model to be used')
parser.add_argument('dataset',
choices=['QQP', 'SNLI'],
help='dataset to be used')
parser.add_argument('--gpu',
default='0',
help='index of GPU to be used (default: %(default))')
args = parser.parse_args()
set_visible_gpu(args.gpu)
main_config = init_config()
model_config = init_config(args.model)
mode = args.mode
if 'train' in mode:
train(main_config, model_config, args.model, args.dataset)
else:
predict(main_config, model_config, args.model)
def main():
parser = ArgumentParser()
parser.add_argument(
'mode',
choices=['train', 'predict'],
help='pipeline mode',
)
parser.add_argument(
'model',
choices=['rnn', 'cnn', 'multihead'],
help='model to be used',
)
parser.add_argument(
'dataset',
choices=['QQP', 'SNLI', 'ANLI'],
nargs='?',
help='dataset to be used',
)
parser.add_argument(
'--experiment_name',
required=False,
help='the name of run experiment',
)
parser.add_argument(
'--gpu',
default='0',
help='index of GPU to be used (default: %(default))',
)
args = parser.parse_args()
if 'train' in args.mode:
if args.dataset is None:
parser.error('Positional argument [dataset] is mandatory')
set_visible_gpu(args.gpu)
main_config = init_config()
model_config = init_config(args.model)
mode = args.mode
experiment_name = args.experiment_name
if experiment_name is None:
experiment_name = create_experiment_name(args.model, main_config,
model_config)
if 'train' in mode:
train(main_config, model_config, args.model, experiment_name,
args.dataset)
else:
predict(main_config, model_config, args.model, experiment_name)
def main():
parser = ArgumentParser()
parser.add_argument('--data-dir',
default='./corpora',
help='Path to original quora split')
parser.add_argument('--model-dir',
default='./model_dir',
help='Path to save the trained model')
parser.add_argument('--use-help',
choices=[True, False],
default=False,
type=bool,
help='should model use help on difficult examples')
parser.add_argument('--gpu',
default='0',
help='index of GPU to be used (default: %(default))')
parser.add_argument('--embeddings',
choices=['no', 'fixed', 'tunable'],
default='no',
type=str,
help='embeddings')
parser.add_argument('--batch-size',
choices=[4, 128, 256, 512],
default=128,
type=int,
help='batch size')
parser.add_argument('--syn-weight',
default=1,
type=float,
help='Weight for loss function')
args = parser.parse_args()
logger.info(args)
if args.embeddings == 'no':
args.use_embed = False
args.tune = False
else:
args.use_embed = True
args.tune = False if args.embeddings == 'fixed' else True
set_visible_gpu(args.gpu)
args.model_dir = '{}_bilstm_{}_{}_{}'.format(args.model_dir,
args.embeddings,
args.batch_size,
args.syn_weight
)
main_config = init_config()
args.max_seq_length, args.vocab_size = get_vocab(main_config, args, logger)
logger.info(args)
train(main_config, args)
def load_model(self, model_name):
if 'multihead' in model_name:
self.visualize_attentions_checkbox.grid(row=2,
column=0,
sticky=W + E,
ipady=1)
else:
self.visualize_attentions_checkbox.grid_forget()
tf.reset_default_graph()
self.session = tf.Session()
logger.info('Loading model: %s', model_name)
model = MODELS[model_name.split('_')[0]]
model_config = init_config(model_name.split('_')[0])
self.model = model(self.max_doc_len, self.vocabulary_size,
self.main_config, model_config)
saver = tf.train.Saver()
last_checkpoint = tf.train.latest_checkpoint('{}/{}'.format(
self.model_dir, model_name))
saver.restore(self.session, last_checkpoint)
logger.info('Loaded model from: %s', last_checkpoint)
def __init__(self, master):
self.frame = master
self.frame.title('Multihead Siamese Nets')
sample1 = StringVar(master, value=SAMPLE_SENTENCE1)
sample2 = StringVar(master, value=SAMPLE_SENTENCE2)
self.first_sentence_entry = Entry(
self.frame,
width=50,
font="Helvetica {}".format(GUI_FONT_SIZE),
textvariable=sample1)
self.second_sentence_entry = Entry(
self.frame,
width=50,
font="Helvetica {}".format(GUI_FONT_SIZE),
textvariable=sample2)
self.predictButton = Button(self.frame,
text='Predict',
font="Helvetica {}".format(GUI_FONT_SIZE),
command=self.predict)
self.clearButton = Button(self.frame,
text='Clear',
command=self.clear,
font="Helvetica {}".format(GUI_FONT_SIZE))
self.resultLabel = Label(self.frame,
text='Result',
font="Helvetica {}".format(GUI_FONT_SIZE))
self.first_sentence_label = Label(
self.frame,
text='Sentence 1',
font="Helvetica {}".format(GUI_FONT_SIZE))
self.second_sentence_label = Label(
self.frame,
text='Sentence 2',
font="Helvetica {}".format(GUI_FONT_SIZE))
self.main_config = init_config()
self.model_dir = str(self.main_config['DATA']['model_dir'])
model_dirs = [os.path.basename(x[0]) for x in os.walk(self.model_dir)]
self.visualize_attentions = IntVar()
self.visualize_attentions_checkbox = Checkbutton(
master,
text="Visualize attention weights",
font="Helvetica {}".format(int(GUI_FONT_SIZE / 2)),
variable=self.visualize_attentions,
onvalue=1,
offvalue=0)
variable = StringVar(master)
variable.set('Choose a model...')
self.model_type = OptionMenu(master,
variable,
*model_dirs,
command=self.load_model)
self.model_type.configure(font=('Helvetica', GUI_FONT_SIZE))
self.first_sentence_entry.grid(row=0, column=1, columnspan=4)
self.first_sentence_label.grid(row=0, column=0, sticky=E)
self.second_sentence_entry.grid(row=1, column=1, columnspan=4)
self.second_sentence_label.grid(row=1, column=0, sticky=E)
self.model_type.grid(row=2, column=1, sticky=W + E, ipady=1)
self.predictButton.grid(row=2, column=2, sticky=W + E, ipady=1)
self.clearButton.grid(row=2, column=3, sticky=W + E, ipady=1)
self.resultLabel.grid(row=2, column=4, sticky=W + E, ipady=1)
self.vectorizer = DatasetVectorizer(self.model_dir)
self.max_doc_len = self.vectorizer.max_sentence_len
self.vocabulary_size = self.vectorizer.vocabulary_size
self.session = tf.Session()
self.model = None