def main(args):
validSet = Loader(os.path.join(FLAGS.data_dir, 'test.csv'),
FLAGS.vocab_path, FLAGS.pn_batch_size,
FLAGS.ctr_batch_size, FLAGS.seq_length)
G = Generator()
G(validSet.vocab_size)
with tf.Session() as sess:
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint('models/pre_G'))
graph = tf.get_default_graph()
encoder_inputs = graph.get_tensor_by_name("G_inputs/encoder_inputs:0")
user_inputs = graph.get_tensor_by_name("G_inputs/user_inputs:0")
input_lengths = graph.get_tensor_by_name("G_inputs/input_lengths:0")
pointer_labels = graph.get_tensor_by_name("G_outputs/pointer_labels:0")
pointer_hot_labels = graph.get_tensor_by_name(
"G_outputs/pointer_hot_labels:0")
# loss = graph.get_tensor_by_name("G_loss/loss:0")
pointer_prob = graph.get_tensor_by_name("G_loss/pointer_prob:0")
rank_pointers = graph.get_tensor_by_name("G_pointers/rank_pointers:0")
print('finish loading model!')
# test
G_val_acc0, G_val_loss0 = 0, 0
for itr in range(validSet.n_batches):
x_raw, x_batch, u_batch, x_lengths, y_batch, y_hot_batch = validSet.next_pn_batch(
)
test_dict = {
encoder_inputs: x_batch,
user_inputs: u_batch,
input_lengths: x_lengths,
pointer_labels: y_batch,
pointer_hot_labels: y_hot_batch
}
output_prob, pre_labels = sess.run([pointer_prob, rank_pointers],
feed_dict=test_dict)
jishu = 0
for j, line in enumerate(pre_labels):
# print u_batch[j]
for word in line:
if word in y_batch[j]:
jishu = jishu + 1
acc = jishu * 1.0 / (FLAGS.pn_batch_size * 5)
G_val_acc0 += acc
print(pre_labels)
print(y_batch)
if itr == 0:
for i in range(FLAGS.pn_batch_size):
print(i)
origin = ''
predict = ''
for j in range(20):
if j in y_batch[i]:
origin += x_raw[i, j]
for j in range(20):
if j in pre_labels[i]:
predict += x_raw[i, j]
print(i, origin)
print(i, predict)
print("Test Generator: test_acc:{:.2f}".format(G_val_acc0 /
validSet.n_batches))
def main(args):
# load data
click1_Set = Loader(os.path.join(FLAGS.data_dir, 'click1.csv'),
FLAGS.vocab_path, FLAGS.pn_batch_size,
FLAGS.ctr_batch_size, FLAGS.seq_length)
click0_Set = Loader(os.path.join(FLAGS.data_dir, 'click0.csv'),
FLAGS.vocab_path, FLAGS.pn_batch_size,
FLAGS.ctr_batch_size, FLAGS.seq_length)
# pretrain graph
generator = Generator(click1_Set.vocab_size)
pre_optimize = tf.train.AdamOptimizer(FLAGS.learning_rate)
pre_train_step = pre_optimize.minimize(generator.loss,
var_list=generator.vars)
saver = tf.train.Saver()
title_inputs = tf.placeholder(tf.int32, [None, FLAGS.seq_length],
name='title_inputs')
title_distribution = tf.placeholder(tf.float32, [None, FLAGS.seq_length],
name='title_distribution')
# formal train graph
generator_fake = Generator(click0_Set.vocab_size)
real_discriminator = Discriminator(click1_Set.vocab_size, title_inputs,
title_distribution)
fake_discriminator = Discriminator(click0_Set.vocab_size,
generator_fake.encoder_inputs,
generator_fake.pointer_prob)
reg = tc.layers.apply_regularization(
tc.layers.l1_regularizer(2.5e-5),
weights_list=[
var for var in tf.global_variables()
if 'kernel' or 'W1' or 'W2' in var.name
])
D_real_loss = tf.reduce_mean(real_discriminator.predictions)
D_fake_loss = tf.reduce_mean(fake_discriminator.predictions)
D_loss = D_fake_loss - D_real_loss
D_loss_reg = D_loss + reg
D_optimize = tf.train.RMSPropOptimizer(FLAGS.learning_rate)
# WGAN lipschitz-penalty
alpha = tf.random_uniform(shape=[tf.shape(title_distribution)[0], 1, 1],
minval=0.,
maxval=1.)
differences = generator_fake.pointer_prob_hot - title_distribution
interpolates = title_distribution + (alpha * differences)
gradients = tf.gradients(
Discriminator(click0_Set.vocab_size, generator_fake.encoder_inputs,
interpolates).predictions, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1, 2]))
gradient_penalty = tf.reduce_mean((slopes - 1.)**2)
D_loss_reg = D_loss + 10 * gradient_penalty
D_train_step = D_optimize.minimize(D_loss_reg,
var_list=fake_discriminator.vars)
G_loss = -tf.reduce_mean(fake_discriminator.predictions)
G_loss_reg = G_loss + reg
G_optimize = tf.train.RMSPropOptimizer(FLAGS.learning_rate)
G_train_step = G_optimize.minimize(G_loss_reg,
var_list=generator_fake.vars)
saver_G = tf.train.Saver(var_list=generator.vars)
saver_D = tf.train.Saver(var_list=real_discriminator.vars)
# for var in tf.global_variables():
# print(var.name)
# training precess
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
pretrain_G(sess, saver, generator, click1_Set, FLAGS.G_pretrain_epochs,
pre_train_step)
print('Start training...')
for i in range(FLAGS.train_epochs):
d_iters = FLAGS.d_iters
g_iters = FLAGS.g_iters
for _ in range(0, d_iters):
x_batch_fake, u_batch_fake, x_lengths_fake, y_batch_fake, y_hot_batch_fake = click0_Set.next_pn_batch(
)
x_batch_real, u_batch_real, x_lengths_real, y_batch_real, y_hot_batch_real = click1_Set.next_pn_batch(
)
D_dict = {
generator_fake.encoder_inputs: x_batch_fake,
generator_fake.user_inputs: u_batch_fake,
generator_fake.input_lengths: x_lengths_fake,
generator_fake.pointer_labels: y_batch_fake,
generator_fake.pointer_hot_labels: y_hot_batch_fake,
title_inputs: x_batch_real,
title_distribution: y_hot_batch_real,
real_discriminator.user_inputs: u_batch_real,
fake_discriminator.user_inputs: u_batch_fake
}
sess.run(fake_discriminator.d_clip)
loss_Real, loss_Fake, lossD, _ = sess.run(
[D_real_loss, D_fake_loss, D_loss, D_train_step],
feed_dict=D_dict)
for _ in range(0, g_iters):
x_batch_fake, u_batch_fake, x_lengths_fake, y_batch_fake, y_hot_batch_fake = click0_Set.next_pn_batch(
)
x_batch_real, u_batch_real, x_lengths_real, y_batch_real, y_hot_batch_real = click1_Set.next_pn_batch(
)
D_dict = {
generator_fake.encoder_inputs: x_batch_fake,
generator_fake.user_inputs: u_batch_fake,
generator_fake.input_lengths: x_lengths_fake,
generator_fake.pointer_labels: y_batch_fake,
generator_fake.pointer_hot_labels: y_hot_batch_fake,
title_inputs: x_batch_real,
title_distribution: y_hot_batch_real,
real_discriminator.user_inputs: u_batch_real,
fake_discriminator.user_inputs: u_batch_fake
}
lossG, _ = sess.run([G_loss, G_train_step], feed_dict=D_dict)
print(
"epoch:{}, D_loss:{:.2f}, G_loss:{:.2f}, loss_Real:{:.2f}, loss_Fake:{:.2f}, Sum_loss:{:.2f}"
.format(i, lossD, lossG, loss_Real, loss_Fake, lossD + lossG))
saver_G.save(sess, os.path.join(FLAGS.save_dir, 'G/train_generator'))
saver_D.save(sess, os.path.join(FLAGS.save_dir,
'D/train_discriminator'))