Exemplos de load_trained_vecs em Python

Exemplo n.º 1

print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.iteritems()):
    print("{}={}".format(attr.upper(), value))
print("")


# Data Preparatopn
# ==================================================
# Load data
print("Loading data...")
x_, y_, vocabulary, vocabulary_inv, test_size = data_helpers.load_data(FLAGS.vn)
print(x_)
print(y_)

print("Loading pre-trained vectors...")
trained_vecs = data_helpers.load_trained_vecs(
    FLAGS.vn, FLAGS.vn_embeddings, FLAGS.en_embeddings, vocabulary)

# Create embedding lookup table
count = data_helpers.add_unknown_words(trained_vecs, vocabulary)
embedding_mat = [trained_vecs[p] for i, p in enumerate(vocabulary_inv)]
embedding_mat = np.array(embedding_mat, dtype = np.float32)

# Randomly shuffle data
x, x_test = x_[:-test_size], x_[-test_size:]
y, y_test = y_[:-test_size], y_[-test_size:]
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = y[shuffle_indices]
if FLAGS.hold_out == 0:
    x_train = x_shuffled
    y_train = y_shuffled

Exemplo n.º 2

Arquivo: train_cnn.py Projeto: 333caowei/qclass_dl

FLAGS = tf.flags.FLAGS
FLAGS.batch_size
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.iteritems()):
    print("{}={}".format(attr.upper(), value))
print("")


# Data Preparatopn
# ==================================================
# Load data
print("Loading data...")
x_, y_, vocabulary, vocabulary_inv, test_size = data_helpers.load_data(FLAGS.vn)

print("Loading pre-trained vectors...")
trained_vecs = data_helpers.load_trained_vecs(
    FLAGS.vn, FLAGS.vn_embeddings, FLAGS.en_embeddings, vocabulary)

# Create embedding lookup table
count = data_helpers.add_unknown_words(trained_vecs, vocabulary)
embedding_mat = [trained_vecs[p] for i, p in enumerate(vocabulary_inv)]
embedding_mat = np.array(embedding_mat, dtype = np.float32)

# Randomly shuffle data
x, x_test = x_[:-test_size], x_[-test_size:]
y, y_test = y_[:-test_size], y_[-test_size:]
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = y[shuffle_indices]
if FLAGS.hold_out == 0:
    x_train = x_shuffled
    y_train = y_shuffled