def predict(token_vocab, target_vocab, sent):
# load trained model
model_fn = os.path.join(os.path.dirname(__file__), 'model.pt')
model = torch.load(model_fn)
batch_size = 1
num_steps = model.num_steps
in_sent = '{}\t{}'.format('___DUMMY_CLASS___', sent)
# to make raw sentence to id data easily
pred_data = N21TextData(in_sent, mode='sentence')
pred_id_data = N21Converter.convert(pred_data, target_vocab, token_vocab)
pred_data_set = SentimentDataset(pred_id_data, batch_size, num_steps)
#
a_batch_data = next(pred_data_set.predict_iterator) # a result
b_sentiment_id, b_token_ids, b_weight = a_batch_data
# convert numpy to pytorch
b_sentiment_id = Variable(torch.from_numpy(b_sentiment_id))
b_token_ids = Variable(torch.from_numpy(b_token_ids))
# feed-forward (inference)
model.batch_size = batch_size
model.initial_state = model.reset_initial_state()
logit = model(b_token_ids)
np_probs = F.softmax(logit).data[0].numpy() # to get probability
# torch to numpy
best_index = np_probs.argmax()
best_prob = np_probs[best_index]
best_target_class = target_vocab.get_symbol(best_index)
print("[Prediction] {} ===> {}".format(sent, best_target_class))
def predict(token_vocab, target_vocab, sent):
os.environ[
'CUDA_VISIBLE_DEVICES'] = '-1' # force to use cpu only (prediction)
model_dir = "./trained_models"
# prepare sentence converting
# to make raw sentence to id data easily
in_sent = '{}\t{}'.format('___DUMMY_CLASS___', sent)
pred_data = N21TextData(in_sent, mode='sentence')
pred_id_data = N21Converter.convert(pred_data, target_vocab, token_vocab)
pred_data_set = SentimentDataset(pred_id_data, 1, 128)
#
a_batch_data = next(pred_data_set.predict_iterator) # a result
b_sentiment_id, b_token_ids, b_weight = a_batch_data
# Restore graph
# note that frozen_graph.tf.pb contains graph definition with parameter values in binary format
_graph_fn = os.path.join(model_dir, 'frozen_graph.tf.pb')
with tf.gfile.GFile(_graph_fn, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def)
with tf.Session(graph=graph) as sess:
# to check load graph
#for n in tf.get_default_graph().as_graph_def().node: print(n.name)
# make interface for input
pl_token = graph.get_tensor_by_name('import/model/pl_tokens:0')
pl_keep_prob = graph.get_tensor_by_name('import/model/pl_keep_prob:0')
# make interface for output
out_pred = graph.get_tensor_by_name('import/model/out_pred:0')
out_probs = graph.get_tensor_by_name('import/model/out_probs:0')
# predict sentence
b_best_pred_index, b_pred_probs = sess.run([out_pred, out_probs],
feed_dict={
pl_token: b_token_ids,
pl_keep_prob: 1.0,
})
best_pred_index = b_best_pred_index[0]
pred_probs = b_pred_probs[0]
best_target_class = target_vocab.get_symbol(best_pred_index)
print(pred_probs[best_pred_index])
best_prob = int(pred_probs[best_pred_index])
print(best_target_class, best_prob)
def load_data():
# vocab loader
token_vocab_fn = os.path.join(os.path.dirname(__file__), 'data',
'token.vocab.txt')
token_vocab = Vocab(token_vocab_fn, mode='token')
target_vocab_fn = os.path.join(os.path.dirname(__file__), 'data',
'target.vocab.txt')
target_vocab = Vocab(target_vocab_fn, mode='target')
# load train data
#train_data_fn = os.path.join( os.path.dirname(__file__), 'data', 'train.sent_data.txt')
train_data_fn = os.path.join(os.path.dirname(__file__), 'data',
'small.train.sent_data.txt')
train_txt_data = N21TextData(train_data_fn)
# convert text data to id data
train_id_data = N21Converter.convert(train_txt_data, target_vocab,
token_vocab)
return train_id_data, token_vocab, target_vocab