saver = tf.train.Saver(tf.global_variables(), max_to_keep=1)
tf.add_to_collection('accuracy', accuracy)
tf.add_to_collection('x1', x1)
tf.add_to_collection('x2', x2)
tf.add_to_collection('y', y)
with tf.Session() as sess:
sess.run(init)
step = 1
model = data_helpers.load_model(
'./Data/GoogleNews-vectors-negative300.bin')
if test == 'PDTB':
sentences1, sentences2, labels = data_helpers.load_labels_and_data_PDTB(
model, './Data/PDTB_implicit/train.txt')
elif test == 'SICK':
sentences1, sentences2, labels = data_helpers.load_data_SICK(
model, './Data/SICK/train.txt')
total = 0
while total < training_iters:
start = total % len(sentences1)
end = (total + batch_size) % len(sentences1)
if end <= start:
end = len(sentences1)
batch_x1 = sentences1[start:end]
batch_x2 = sentences2[start:end]
batch_y = labels[start:end]
total += (len(batch_x1))
sess.run(optimizer, feed_dict={x1: batch_x1, x2: batch_x2, y: batch_y})
if step % display_step == 0:
#calculate batch loss and accuracy
loss, acc = sess.run([cost, accuracy],
tf.add_to_collection('accuracy', accuracy)
tf.add_to_collection('x1', x1)
tf.add_to_collection('x2', x2)
tf.add_to_collection('y', y)
with tf.Session() as sess:
sess.run(init)
step = 1
model = data_helpers.load_model(
'./Data/GoogleNews-vectors-negative300.bin')
if test == 'PDTB':
sentences1, sentences2, labels, lengths1, lengths2 = \
data_helpers.load_labels_and_data_PDTB(model, './Data/PDTB_implicit/train.txt', False, True, True)
elif test == 'SICK':
sentences1, sentences2, labels, lengths1, lengths2 = \
data_helpers.load_data_SICK(model, './Data/SICK/train.txt', False, True, True)
total = 0
while total < training_iters:
start = total % len(sentences1)
end = (total + batch_size) % len(sentences1)
if end <= start:
end = len(sentences1)
batch_x1 = sentences1[start:end]
batch_x2 = sentences2[start:end]
batch_x1_lengths = lengths1[start:end]
batch_x2_lengths = lengths2[start:end]
batch_y = labels[start:end]
total += (len(batch_x1))
sess.run(optimizer,
feed_dict={