def transfer(model, decoder, sess, args, vocab, data0, data1, out_path):
batches, order0, order1 = get_batches(data0, data1, vocab.word2id,
args.batch_size)
data0_tsf, data1_tsf = [], []
losses = Losses(len(batches))
for batch in batches:
ori, tsf = decoder.rewrite(batch)
half = batch['size'] / 2
data0_tsf += tsf[:half]
data1_tsf += tsf[half:]
loss, loss_g, loss_d, loss_d0, loss_d1 = sess.run(
[
model.loss, model.loss_g, model.loss_d, model.loss_d0,
model.loss_d1
],
feed_dict=feed_dictionary(model, batch, args.rho, args.gamma_min))
losses.add(loss, loss_g, loss_d, loss_d0, loss_d1)
n0, n1 = len(data0), len(data1)
data0_tsf = reorder(order0, data0_tsf)[:n0]
data1_tsf = reorder(order1, data1_tsf)[:n1]
if out_path:
write_sent(data0_tsf, out_path + '.0' + '.tsf')
write_sent(data1_tsf, out_path + '.1' + '.tsf')
return losses
def transfer(model, decoder, sess, args, vocab, data0, data1, out_path):
batches, order0, order1 = get_batches(data0, data1,
vocab.word2id, args.batch_size)
#data0_rec, data1_rec = [], []
data0_tsf, data1_tsf = [], []
losses = Accumulator(len(batches), ['loss', 'rec', 'adv', 'd0', 'd1'])
for batch in batches:
rec, tsf = decoder.rewrite(batch)
half = batch['size'] / 2
#data0_rec += rec[:half]
#data1_rec += rec[half:]
data0_tsf += tsf[:half]
data1_tsf += tsf[half:]
loss, loss_rec, loss_adv, loss_d0, loss_d1 = sess.run([model.loss,
model.loss_rec, model.loss_adv, model.loss_d0, model.loss_d1],
feed_dict=feed_dictionary(model, batch, args.rho, args.gamma_min))
losses.add([loss, loss_rec, loss_adv, loss_d0, loss_d1])
n0, n1 = len(data0), len(data1)
#data0_rec = reorder(order0, data0_rec)[:n0]
#data1_rec = reorder(order1, data1_rec)[:n1]
data0_tsf = reorder(order0, data0_tsf)[:n0]
data1_tsf = reorder(order1, data1_tsf)[:n1]
if out_path:
#write_sent(data0_rec, out_path+'.0'+'.rec')
#write_sent(data1_rec, out_path+'.1'+'.rec')
write_sent(data0_tsf, out_path+'.0'+'.tsf')
write_sent(data1_tsf, out_path+'.1'+'.tsf')
return losses
def transfer(model, decoder, sess, args, vocab, data0, data1, out_path):
batches, order0, order1 = get_batches(data0, data1,
vocab.word2id, args.batch_size)
#data0_rec, data1_rec = [], []
data0_tsf, data1_tsf = [], []
losses = Accumulator(len(batches), ['loss', 'rec', 'adv', 'd0', 'd1'])
for batch in batches:
rec, tsf = decoder.rewrite(batch)
half = batch['size'] / 2
#data0_rec += rec[:half]
#data1_rec += rec[half:]
data0_tsf += tsf[:half]
data1_tsf += tsf[half:]
loss, loss_rec, loss_adv, loss_d0, loss_d1 = sess.run([model.loss,
model.loss_rec, model.loss_adv, model.loss_d0, model.loss_d1],
feed_dict=feed_dictionary(model, batch, args.rho, args.gamma_min))
losses.add([loss, loss_rec, loss_adv, loss_d0, loss_d1])
n0, n1 = len(data0), len(data1)
#data0_rec = reorder(order0, data0_rec)[:n0]
#data1_rec = reorder(order1, data1_rec)[:n1]
data0_tsf = reorder(order0, data0_tsf)[:n0]
data1_tsf = reorder(order1, data1_tsf)[:n1]
if out_path:
#write_sent(data0_rec, out_path+'.0'+'.rec')
#write_sent(data1_rec, out_path+'.1'+'.rec')
write_sent(data0_tsf, out_path+'.0'+'.tsf')
write_sent(data1_tsf, out_path+'.1'+'.tsf')
return losses
def transfer(model, decoder, sess, args, vocab, data0, data1, out_path):
batches, order0, order1 = get_batches(data0,
data1,
vocab.word2id,
args.batch_size,
max_seq_len=args.max_seq_length)
# data0_rec, data1_rec = [], []
data0_tsf, data1_tsf = [], []
losses = Accumulator(
len(batches),
['loss', 'rec', 'adv', 'd0', 'd1', 'loss_rec_cyc', 'loss_kld'])
for batch in batches:
rec, tsf = decoder.rewrite(batch)
half = batch['size'] // 2
# data0_rec += rec[:half]
# data1_rec += rec[half:]
data0_tsf += tsf[:half]
data1_tsf += tsf[half:]
loss, loss_rec, loss_adv, loss_d0, loss_d1, loss_rec_cyc, loss_kld = \
sess.run([model.loss,
model.loss_rec, model.loss_adv, model.loss_d0, model.loss_d1,
model.loss_rec_cyc, model.kld_loss],
feed_dict=feed_dictionary(model=model,
batch=batch,
rho=args.rho,
epsilon=args.epsilon,
gamma=args.gamma_min,
anneal=args.anneal,
C=args.C))
# feed_dict order: model, batch, rho, epsilon, gamma, dropout=1, learning_rate=None, anneal=1
losses.add([
loss, loss_rec, loss_adv, loss_d0, loss_d1, loss_rec_cyc, loss_kld
])
n0, n1 = len(data0), len(data1)
# data0_rec = reorder(order0, data0_rec)[:n0]
# data1_rec = reorder(order1, data1_rec)[:n1]
data0_tsf = reorder(order0, data0_tsf)[:n0]
data1_tsf = reorder(order1, data1_tsf)[:n1]
if out_path:
# write_sent(data0_rec, out_path+'.0'+'.rec')
# write_sent(data1_rec, out_path+'.1'+'.rec')
write_sent(data0_tsf, out_path + 'formal' + '.tsf')
write_sent(data1_tsf, out_path + 'informal' + '.tsf')
return losses
def transfer(model, sess, args, vocab, data0, data1, out_path):
batches, order0, order1 = get_batches(data0, data1, vocab.word2id,
args.batch_size)
data0_tsf, data1_tsf = [], []
losses = Losses(len(batches))
for batch in batches:
ori, tsf, loss, loss_g, loss_d, loss_d0, loss_d1 = rewrite(
model, sess, args, vocab, batch)
half = batch['size'] / 2
data0_tsf += tsf[:half]
data1_tsf += tsf[half:]
losses.add(loss, loss_g, loss_d, loss_d0, loss_d1)
n0, n1 = len(data0), len(data1)
data0_tsf = reorder(order0, data0_tsf)[:n0]
data1_tsf = reorder(order1, data1_tsf)[:n1]
if out_path:
write_sent(data0_tsf, out_path + '.0' + '.tsf')
write_sent(data1_tsf, out_path + '.1' + '.tsf')
return losses
def transfer(model, decoder, sess, args, vocab, data0, data1, out_path):
batches, order0, order1 = get_batches(data0, data1, vocab.word2id,
args.batch_size)
#data0_rec, data1_rec = [], []
data0_tsf, data1_tsf = [], []
losses = Accumulator(len(batches), ['loss', 'rec', 'adv', 'd0', 'd1'])
for batch in batches:
rec, tsf = decoder.rewrite(batch)
half = int(batch['size'] // 2)
#data0_rec += rec[:half]
#data1_rec += rec[half:]
data0_tsf += tsf[:half]
data1_tsf += tsf[half:]
loss, loss_rec, loss_adv, loss_d0, loss_d1 = sess.run(
[
model.loss, model.loss_rec, model.loss_adv, model.loss_d0,
model.loss_d1
],
feed_dict=feed_dictionary(model, batch, args.rho, args.gamma_min))
losses.add([loss, loss_rec, loss_adv, loss_d0, loss_d1])
#
# with tf.Graph().as_default():
# sess = tf.Session()
# with sess.as_default():
# m = model.Model()
# m = model.Model()
# global_step = tf.Variable(0, name='global_step', trainable=False)
# optimizer = tf.train.AdamOptimizer(1e-2)
# grads_and_vars = optimizer.compute_gradients(m.loss)
# train_op = optimizer.apply_gradients(grads_and_vars=grads_and_vars, global_step=global_step)
#
# loss_summary = tf.summary.scalar('loss', m.loss)
#
# train_summary_op = tf.summary.merge([loss_summary])
# train_summary_writer = tf.summary.FileWriter('./summary/train', sess.graph)
#
# dev_summary_op = tf.summary.merge([loss_summary])
# dev_summary_writer = tf.summary.FileWriter('./summary/dev', sess.graph)
#
# def train_step(x_batch, y_batch):
# feed_dict = {m.input_x: x_batch,
# m.input_y: y_batch}
# _, step, summaries, loss = sess.run(
# [train_op, global_step, train_summary_op, m.loss], feed_dict)
# train_summary_writer.add_summary(summaries, step)
#
# def dev_step(x_batch, y_batch):
# feed_dict = {m.input_x: x_batch,
# m.input_y: y_batch}
#
# step, summaries, loss = sess.run(
# [global_step, dev_summary_op, m.loss], feed_dict)
# dev_summary_writer.add_summary(summaries, step)
#
# sess.run(tf.global_variables_initializer())
# batches = batch_iter(list(zip(x_train, y_train)), 100, 100)
# for batch in batches:
# x_batch, y_batch = zip(*batch)
# train_step(x_batch, y_batch)
# current_step = tf.train.global_step(sess, global_step)
# if current_step % 3 == 0:
# print('\nEvaluation:')
# dev_step(x_val, y_val)
#
#
# tf.summary.scalar('loss', loss)
n0, n1 = len(data0), len(data1)
#data0_rec = reorder(order0, data0_rec)[:n0]
#data1_rec = reorder(order1, data1_rec)[:n1]
data0_tsf = reorder(order0, data0_tsf)[:n0]
data1_tsf = reorder(order1, data1_tsf)[:n1]
if out_path:
#write_sent(data0_rec, out_path+'.0'+'.rec')
#write_sent(data1_rec, out_path+'.1'+'.rec')
write_sent(data0_tsf, out_path + '.0' + '.tsf')
write_sent(data1_tsf, out_path + '.1' + '.tsf')
return losses
