def create_rollout_MaskGAN(hparams, is_training):
"""Create the MaskGAN model.
Args:
hparams: Hyperparameters for the MaskGAN.
is_training: Boolean indicating operational mode (train/inference).
evaluated with a teacher forcing regime.
Return:
model: Namedtuple for specifying the MaskGAN."""
global_step = tf.Variable(0, name='global_step', trainable=False)
new_learning_rate = tf.placeholder(tf.float32, [],
name='new_learning_rate')
learning_rate = tf.Variable(0.0, name='learning_rate', trainable=False)
learning_rate_update = tf.assign(learning_rate, new_learning_rate)
new_rate = tf.placeholder(tf.float32, [], name='new_rate')
percent_real_var = tf.Variable(0.0, trainable=False)
percent_real_update = tf.assign(percent_real_var, new_rate)
## Placeholders.
inputs = tf.placeholder(tf.int32,
shape=[FLAGS.batch_size, FLAGS.sequence_length])
present = tf.placeholder(tf.bool,
shape=[FLAGS.batch_size, FLAGS.sequence_length])
inv_present = tf.placeholder(
tf.bool, shape=[FLAGS.batch_size, FLAGS.sequence_length])
## Rollout Generator.
fwd_gen_rollouts = rollout_generator(hparams,
inputs,
present,
is_training=is_training,
is_validating=False)
inv_gen_rollouts = rollout_generator(hparams,
inputs,
inv_present,
is_training=is_training,
is_validating=False,
reuse=True)
## Rollout Discriminator.
fwd_dis_rollouts = rollout_discriminator(hparams,
fwd_gen_rollouts,
is_training=is_training)
inv_dis_rollouts = rollout_discriminator(hparams,
inv_gen_rollouts,
is_training=is_training,
reuse=True)
## Discriminator Loss.
[dis_loss, dis_loss_pred,
dis_loss_inv_pred] = rollout_discriminator_loss(fwd_dis_rollouts, present,
inv_dis_rollouts,
inv_present)
## Average log-perplexity for only missing words. However, to do this,
# the logits are still computed using teacher forcing, that is, the ground
# truth tokens are fed in at each time point to be valid.
# TODO(liamfedus): Fix the naming convention.
with tf.variable_scope('gen_rollout'):
_, fwd_eval_logits, _ = model_construction.create_generator(
hparams,
inputs,
present,
is_training=False,
is_validating=True,
reuse=True)
avg_log_perplexity = model_losses.calculate_log_perplexity(
fwd_eval_logits, inputs, present)
## Generator Loss.
# 1. Cross Entropy losses on missing tokens.
[fwd_cross_entropy_losses, inv_cross_entropy_losses
] = rollout_masked_cross_entropy_loss(inputs, present, inv_present,
fwd_gen_rollouts, inv_gen_rollouts)
# 2. GAN losses on missing tokens.
[fwd_RL_loss, fwd_RL_statistics,
fwd_averages_op] = rollout_reinforce_objective(hparams, fwd_gen_rollouts,
fwd_dis_rollouts, present)
[inv_RL_loss, inv_RL_statistics,
inv_averages_op] = rollout_reinforce_objective(hparams, inv_gen_rollouts,
inv_dis_rollouts,
inv_present)
# TODO(liamfedus): Generalize this to use all logs.
[fwd_sequence, fwd_logits, fwd_log_probs] = fwd_gen_rollouts[-1]
[inv_sequence, inv_logits, inv_log_probs] = inv_gen_rollouts[-1]
# TODO(liamfedus): Generalize this to use all logs.
fwd_predictions = fwd_dis_rollouts[-1]
inv_predictions = inv_dis_rollouts[-1]
# TODO(liamfedus): Generalize this to use all logs.
[fwd_log_probs, fwd_rewards, fwd_advantages,
fwd_baselines] = fwd_RL_statistics[-1]
[inv_log_probs, inv_rewards, inv_advantages,
inv_baselines] = inv_RL_statistics[-1]
## Pre-training.
if FLAGS.gen_pretrain_steps:
# TODO(liamfedus): Rewrite this.
fwd_cross_entropy_loss = tf.reduce_mean(fwd_cross_entropy_losses)
gen_pretrain_op = model_optimization.create_gen_pretrain_op(
hparams, fwd_cross_entropy_loss, global_step)
else:
gen_pretrain_op = tf.no_op('gen_pretrain_no_op')
if FLAGS.dis_pretrain_steps:
dis_pretrain_op = model_optimization.create_dis_pretrain_op(
hparams, dis_loss, global_step)
else:
dis_pretrain_op = tf.no_op('dis_pretrain_no_op')
## Generator Train Op.
# 1. Cross-Entropy.
if FLAGS.gen_training_strategy == 'cross_entropy':
gen_loss = tf.reduce_mean(fwd_cross_entropy_losses +
inv_cross_entropy_losses) / 2.
[gen_train_op, gen_grads,
gen_vars] = model_optimization.create_gen_train_op(hparams,
learning_rate,
gen_loss,
global_step,
mode='MINIMIZE')
# 2. GAN (REINFORCE)
elif FLAGS.gen_training_strategy == 'reinforce':
gen_loss = (fwd_RL_loss + inv_RL_loss) / 2.
[gen_train_op, gen_grads,
gen_vars] = model_optimization.create_reinforce_gen_train_op(
hparams, learning_rate, gen_loss, fwd_averages_op,
inv_averages_op, global_step)
else:
raise NotImplementedError
## Discriminator Train Op.
dis_train_op, dis_grads, dis_vars = model_optimization.create_dis_train_op(
hparams, dis_loss, global_step)
## Summaries.
with tf.name_scope('general'):
tf.summary.scalar('percent_real', percent_real_var)
tf.summary.scalar('learning_rate', learning_rate)
with tf.name_scope('generator_losses'):
tf.summary.scalar('gen_loss', tf.reduce_mean(gen_loss))
tf.summary.scalar('gen_loss_fwd_cross_entropy',
tf.reduce_mean(fwd_cross_entropy_losses))
tf.summary.scalar('gen_loss_inv_cross_entropy',
tf.reduce_mean(inv_cross_entropy_losses))
with tf.name_scope('REINFORCE'):
with tf.name_scope('objective'):
tf.summary.scalar('fwd_RL_loss', tf.reduce_mean(fwd_RL_loss))
tf.summary.scalar('inv_RL_loss', tf.reduce_mean(inv_RL_loss))
with tf.name_scope('rewards'):
helper.variable_summaries(fwd_rewards, 'fwd_rewards')
helper.variable_summaries(inv_rewards, 'inv_rewards')
with tf.name_scope('advantages'):
helper.variable_summaries(fwd_advantages, 'fwd_advantages')
helper.variable_summaries(inv_advantages, 'inv_advantages')
with tf.name_scope('baselines'):
helper.variable_summaries(fwd_baselines, 'fwd_baselines')
helper.variable_summaries(inv_baselines, 'inv_baselines')
with tf.name_scope('log_probs'):
helper.variable_summaries(fwd_log_probs, 'fwd_log_probs')
helper.variable_summaries(inv_log_probs, 'inv_log_probs')
with tf.name_scope('discriminator_losses'):
tf.summary.scalar('dis_loss', dis_loss)
tf.summary.scalar('dis_loss_fwd_sequence', dis_loss_pred)
tf.summary.scalar('dis_loss_inv_sequence', dis_loss_inv_pred)
with tf.name_scope('logits'):
helper.variable_summaries(fwd_logits, 'fwd_logits')
helper.variable_summaries(inv_logits, 'inv_logits')
for v, g in zip(gen_vars, gen_grads):
helper.variable_summaries(v, v.op.name)
helper.variable_summaries(g, 'grad/' + v.op.name)
for v, g in zip(dis_vars, dis_grads):
helper.variable_summaries(v, v.op.name)
helper.variable_summaries(g, 'grad/' + v.op.name)
merge_summaries_op = tf.summary.merge_all()
# Model saver.
saver = tf.train.Saver(keep_checkpoint_every_n_hours=1, max_to_keep=5)
# Named tuple that captures elements of the MaskGAN model.
Model = collections.namedtuple('Model', [
'inputs', 'present', 'inv_present', 'percent_real_update', 'new_rate',
'fwd_sequence', 'fwd_logits', 'fwd_rewards', 'fwd_advantages',
'fwd_log_probs', 'fwd_predictions', 'fwd_cross_entropy_losses',
'inv_sequence', 'inv_logits', 'inv_rewards', 'inv_advantages',
'inv_log_probs', 'inv_predictions', 'inv_cross_entropy_losses',
'avg_log_perplexity', 'dis_loss', 'gen_loss', 'dis_train_op',
'gen_train_op', 'gen_pretrain_op', 'dis_pretrain_op',
'merge_summaries_op', 'global_step', 'new_learning_rate',
'learning_rate_update', 'saver'
])
model = Model(inputs, present, inv_present, percent_real_update, new_rate,
fwd_sequence, fwd_logits, fwd_rewards, fwd_advantages,
fwd_log_probs, fwd_predictions, fwd_cross_entropy_losses,
inv_sequence, inv_logits, inv_rewards, inv_advantages,
inv_log_probs, inv_predictions, inv_cross_entropy_losses,
avg_log_perplexity, dis_loss, gen_loss, dis_train_op,
gen_train_op, gen_pretrain_op, dis_pretrain_op,
merge_summaries_op, global_step, new_learning_rate,
learning_rate_update, saver)
return model
def create_rollout_MaskGAN(hparams, is_training):
"""Create the MaskGAN model.
Args:
hparams: Hyperparameters for the MaskGAN.
is_training: Boolean indicating operational mode (train/inference).
evaluated with a teacher forcing regime.
Return:
model: Namedtuple for specifying the MaskGAN."""
global_step = tf.Variable(0, name='global_step', trainable=False)
new_learning_rate = tf.placeholder(tf.float32, [], name='new_learning_rate')
learning_rate = tf.Variable(0.0, name='learning_rate', trainable=False)
learning_rate_update = tf.assign(learning_rate, new_learning_rate)
new_rate = tf.placeholder(tf.float32, [], name='new_rate')
percent_real_var = tf.Variable(0.0, trainable=False)
percent_real_update = tf.assign(percent_real_var, new_rate)
## Placeholders.
inputs = tf.placeholder(
tf.int32, shape=[FLAGS.batch_size, FLAGS.sequence_length])
present = tf.placeholder(
tf.bool, shape=[FLAGS.batch_size, FLAGS.sequence_length])
inv_present = tf.placeholder(
tf.bool, shape=[FLAGS.batch_size, FLAGS.sequence_length])
## Rollout Generator.
fwd_gen_rollouts = rollout_generator(
hparams, inputs, present, is_training=is_training, is_validating=False)
inv_gen_rollouts = rollout_generator(
hparams,
inputs,
inv_present,
is_training=is_training,
is_validating=False,
reuse=True)
## Rollout Discriminator.
fwd_dis_rollouts = rollout_discriminator(
hparams, fwd_gen_rollouts, is_training=is_training)
inv_dis_rollouts = rollout_discriminator(
hparams, inv_gen_rollouts, is_training=is_training, reuse=True)
## Discriminator Loss.
[dis_loss, dis_loss_pred, dis_loss_inv_pred] = rollout_discriminator_loss(
fwd_dis_rollouts, present, inv_dis_rollouts, inv_present)
## Average log-perplexity for only missing words. However, to do this,
# the logits are still computed using teacher forcing, that is, the ground
# truth tokens are fed in at each time point to be valid.
# TODO(liamfedus): Fix the naming convention.
with tf.variable_scope('gen_rollout'):
_, fwd_eval_logits, _ = model_construction.create_generator(
hparams,
inputs,
present,
is_training=False,
is_validating=True,
reuse=True)
avg_log_perplexity = model_losses.calculate_log_perplexity(
fwd_eval_logits, inputs, present)
## Generator Loss.
# 1. Cross Entropy losses on missing tokens.
[fwd_cross_entropy_losses,
inv_cross_entropy_losses] = rollout_masked_cross_entropy_loss(
inputs, present, inv_present, fwd_gen_rollouts, inv_gen_rollouts)
# 2. GAN losses on missing tokens.
[fwd_RL_loss,
fwd_RL_statistics, fwd_averages_op] = rollout_reinforce_objective(
hparams, fwd_gen_rollouts, fwd_dis_rollouts, present)
[inv_RL_loss,
inv_RL_statistics, inv_averages_op] = rollout_reinforce_objective(
hparams, inv_gen_rollouts, inv_dis_rollouts, inv_present)
# TODO(liamfedus): Generalize this to use all logs.
[fwd_sequence, fwd_logits, fwd_log_probs] = fwd_gen_rollouts[-1]
[inv_sequence, inv_logits, inv_log_probs] = inv_gen_rollouts[-1]
# TODO(liamfedus): Generalize this to use all logs.
fwd_predictions = fwd_dis_rollouts[-1]
inv_predictions = inv_dis_rollouts[-1]
# TODO(liamfedus): Generalize this to use all logs.
[fwd_log_probs, fwd_rewards, fwd_advantages,
fwd_baselines] = fwd_RL_statistics[-1]
[inv_log_probs, inv_rewards, inv_advantages,
inv_baselines] = inv_RL_statistics[-1]
## Pre-training.
if FLAGS.gen_pretrain_steps:
# TODO(liamfedus): Rewrite this.
fwd_cross_entropy_loss = tf.reduce_mean(fwd_cross_entropy_losses)
gen_pretrain_op = model_optimization.create_gen_pretrain_op(
hparams, fwd_cross_entropy_loss, global_step)
else:
gen_pretrain_op = tf.no_op('gen_pretrain_no_op')
if FLAGS.dis_pretrain_steps:
dis_pretrain_op = model_optimization.create_dis_pretrain_op(
hparams, dis_loss, global_step)
else:
dis_pretrain_op = tf.no_op('dis_pretrain_no_op')
## Generator Train Op.
# 1. Cross-Entropy.
if FLAGS.gen_training_strategy == 'cross_entropy':
gen_loss = tf.reduce_mean(
fwd_cross_entropy_losses + inv_cross_entropy_losses) / 2.
[gen_train_op, gen_grads,
gen_vars] = model_optimization.create_gen_train_op(
hparams, learning_rate, gen_loss, global_step, mode='MINIMIZE')
# 2. GAN (REINFORCE)
elif FLAGS.gen_training_strategy == 'reinforce':
gen_loss = (fwd_RL_loss + inv_RL_loss) / 2.
[gen_train_op, gen_grads,
gen_vars] = model_optimization.create_reinforce_gen_train_op(
hparams, learning_rate, gen_loss, fwd_averages_op, inv_averages_op,
global_step)
else:
raise NotImplementedError
## Discriminator Train Op.
dis_train_op, dis_grads, dis_vars = model_optimization.create_dis_train_op(
hparams, dis_loss, global_step)
## Summaries.
with tf.name_scope('general'):
tf.summary.scalar('percent_real', percent_real_var)
tf.summary.scalar('learning_rate', learning_rate)
with tf.name_scope('generator_losses'):
tf.summary.scalar('gen_loss', tf.reduce_mean(gen_loss))
tf.summary.scalar('gen_loss_fwd_cross_entropy',
tf.reduce_mean(fwd_cross_entropy_losses))
tf.summary.scalar('gen_loss_inv_cross_entropy',
tf.reduce_mean(inv_cross_entropy_losses))
with tf.name_scope('REINFORCE'):
with tf.name_scope('objective'):
tf.summary.scalar('fwd_RL_loss', tf.reduce_mean(fwd_RL_loss))
tf.summary.scalar('inv_RL_loss', tf.reduce_mean(inv_RL_loss))
with tf.name_scope('rewards'):
helper.variable_summaries(fwd_rewards, 'fwd_rewards')
helper.variable_summaries(inv_rewards, 'inv_rewards')
with tf.name_scope('advantages'):
helper.variable_summaries(fwd_advantages, 'fwd_advantages')
helper.variable_summaries(inv_advantages, 'inv_advantages')
with tf.name_scope('baselines'):
helper.variable_summaries(fwd_baselines, 'fwd_baselines')
helper.variable_summaries(inv_baselines, 'inv_baselines')
with tf.name_scope('log_probs'):
helper.variable_summaries(fwd_log_probs, 'fwd_log_probs')
helper.variable_summaries(inv_log_probs, 'inv_log_probs')
with tf.name_scope('discriminator_losses'):
tf.summary.scalar('dis_loss', dis_loss)
tf.summary.scalar('dis_loss_fwd_sequence', dis_loss_pred)
tf.summary.scalar('dis_loss_inv_sequence', dis_loss_inv_pred)
with tf.name_scope('logits'):
helper.variable_summaries(fwd_logits, 'fwd_logits')
helper.variable_summaries(inv_logits, 'inv_logits')
for v, g in zip(gen_vars, gen_grads):
helper.variable_summaries(v, v.op.name)
helper.variable_summaries(g, 'grad/' + v.op.name)
for v, g in zip(dis_vars, dis_grads):
helper.variable_summaries(v, v.op.name)
helper.variable_summaries(g, 'grad/' + v.op.name)
merge_summaries_op = tf.summary.merge_all()
# Model saver.
saver = tf.train.Saver(keep_checkpoint_every_n_hours=1, max_to_keep=5)
# Named tuple that captures elements of the MaskGAN model.
Model = collections.namedtuple('Model', [
'inputs', 'present', 'inv_present', 'percent_real_update', 'new_rate',
'fwd_sequence', 'fwd_logits', 'fwd_rewards', 'fwd_advantages',
'fwd_log_probs', 'fwd_predictions', 'fwd_cross_entropy_losses',
'inv_sequence', 'inv_logits', 'inv_rewards', 'inv_advantages',
'inv_log_probs', 'inv_predictions', 'inv_cross_entropy_losses',
'avg_log_perplexity', 'dis_loss', 'gen_loss', 'dis_train_op',
'gen_train_op', 'gen_pretrain_op', 'dis_pretrain_op',
'merge_summaries_op', 'global_step', 'new_learning_rate',
'learning_rate_update', 'saver'
])
model = Model(
inputs, present, inv_present, percent_real_update, new_rate, fwd_sequence,
fwd_logits, fwd_rewards, fwd_advantages, fwd_log_probs, fwd_predictions,
fwd_cross_entropy_losses, inv_sequence, inv_logits, inv_rewards,
inv_advantages, inv_log_probs, inv_predictions, inv_cross_entropy_losses,
avg_log_perplexity, dis_loss, gen_loss, dis_train_op, gen_train_op,
gen_pretrain_op, dis_pretrain_op, merge_summaries_op, global_step,
new_learning_rate, learning_rate_update, saver)
return model
def create_MaskGAN(hparams, is_training):
"""Create the MaskGAN model.
Args:
hparams: Hyperparameters for the MaskGAN.
is_training: Boolean indicating operational mode (train/inference).
evaluated with a teacher forcing regime.
Return:
model: Namedtuple for specifying the MaskGAN.
"""
global_step = tf.Variable(0, name='global_step', trainable=False)
new_learning_rate = tf.placeholder(tf.float32, [], name='new_learning_rate')
learning_rate = tf.Variable(0.0, name='learning_rate', trainable=False)
learning_rate_update = tf.assign(learning_rate, new_learning_rate)
new_rate = tf.placeholder(tf.float32, [], name='new_rate')
percent_real_var = tf.Variable(0.0, trainable=False)
percent_real_update = tf.assign(percent_real_var, new_rate)
## Placeholders.
inputs = tf.placeholder(
tf.int32, shape=[FLAGS.batch_size, FLAGS.sequence_length])
targets = tf.placeholder(
tf.int32, shape=[FLAGS.batch_size, FLAGS.sequence_length])
present = tf.placeholder(
tf.bool, shape=[FLAGS.batch_size, FLAGS.sequence_length])
# TODO(adai): Placeholder for IMDB label.
## Real Sequence is the targets.
real_sequence = targets
## Fakse Sequence from the Generator.
# TODO(adai): Generator must have IMDB labels placeholder.
(fake_sequence, fake_logits, fake_log_probs, fake_gen_initial_state,
fake_gen_final_state, _) = model_construction.create_generator(
hparams,
inputs,
targets,
present,
is_training=is_training,
is_validating=False)
(_, eval_logits, _, eval_initial_state, eval_final_state,
_) = model_construction.create_generator(
hparams,
inputs,
targets,
present,
is_training=False,
is_validating=True,
reuse=True)
## Discriminator.
fake_predictions = model_construction.create_discriminator(
hparams,
fake_sequence,
is_training=is_training,
inputs=inputs,
present=present)
real_predictions = model_construction.create_discriminator(
hparams,
real_sequence,
is_training=is_training,
reuse=True,
inputs=inputs,
present=present)
## Critic.
# The critic will be used to estimate the forward rewards to the Generator.
if FLAGS.baseline_method == 'critic':
est_state_values = model_construction.create_critic(
hparams, fake_sequence, is_training=is_training)
else:
est_state_values = None
## Discriminator Loss.
[dis_loss, dis_loss_fake, dis_loss_real] = model_losses.create_dis_loss(
fake_predictions, real_predictions, present)
## Average log-perplexity for only missing words. However, to do this,
# the logits are still computed using teacher forcing, that is, the ground
# truth tokens are fed in at each time point to be valid.
avg_log_perplexity = model_losses.calculate_log_perplexity(
eval_logits, targets, present)
## Generator Objective.
# 1. Cross Entropy losses on missing tokens.
fake_cross_entropy_losses = model_losses.create_masked_cross_entropy_loss(
targets, present, fake_logits)
# 2. GAN REINFORCE losses.
[
fake_RL_loss, fake_log_probs, fake_rewards, fake_advantages,
fake_baselines, fake_averages_op, critic_loss, cumulative_rewards
] = model_losses.calculate_reinforce_objective(
hparams, fake_log_probs, fake_predictions, present, est_state_values)
## Pre-training.
if FLAGS.gen_pretrain_steps:
raise NotImplementedError
# # TODO(liamfedus): Rewrite this.
# fwd_cross_entropy_loss = tf.reduce_mean(fwd_cross_entropy_losses)
# gen_pretrain_op = model_optimization.create_gen_pretrain_op(
# hparams, fwd_cross_entropy_loss, global_step)
else:
gen_pretrain_op = None
if FLAGS.dis_pretrain_steps:
dis_pretrain_op = model_optimization.create_dis_pretrain_op(
hparams, dis_loss, global_step)
else:
dis_pretrain_op = None
## Generator Train Op.
# 1. Cross-Entropy.
if FLAGS.gen_training_strategy == 'cross_entropy':
gen_loss = tf.reduce_mean(fake_cross_entropy_losses)
[gen_train_op, gen_grads,
gen_vars] = model_optimization.create_gen_train_op(
hparams, learning_rate, gen_loss, global_step, mode='MINIMIZE')
# 2. GAN (REINFORCE)
elif FLAGS.gen_training_strategy == 'reinforce':
gen_loss = fake_RL_loss
[gen_train_op, gen_grads,
gen_vars] = model_optimization.create_reinforce_gen_train_op(
hparams, learning_rate, gen_loss, fake_averages_op, global_step)
else:
raise NotImplementedError
## Discriminator Train Op.
dis_train_op, dis_grads, dis_vars = model_optimization.create_dis_train_op(
hparams, dis_loss, global_step)
## Critic Train Op.
if critic_loss is not None:
[critic_train_op, _, _] = model_optimization.create_critic_train_op(
hparams, critic_loss, global_step)
dis_train_op = tf.group(dis_train_op, critic_train_op)
## Summaries.
with tf.name_scope('general'):
tf.summary.scalar('percent_real', percent_real_var)
tf.summary.scalar('learning_rate', learning_rate)
with tf.name_scope('generator_objectives'):
tf.summary.scalar('gen_objective', tf.reduce_mean(gen_loss))
tf.summary.scalar('gen_loss_cross_entropy',
tf.reduce_mean(fake_cross_entropy_losses))
with tf.name_scope('REINFORCE'):
with tf.name_scope('objective'):
tf.summary.scalar('fake_RL_loss', tf.reduce_mean(fake_RL_loss))
with tf.name_scope('rewards'):
helper.variable_summaries(cumulative_rewards, 'rewards')
with tf.name_scope('advantages'):
helper.variable_summaries(fake_advantages, 'advantages')
with tf.name_scope('baselines'):
helper.variable_summaries(fake_baselines, 'baselines')
with tf.name_scope('log_probs'):
helper.variable_summaries(fake_log_probs, 'log_probs')
with tf.name_scope('discriminator_losses'):
tf.summary.scalar('dis_loss', dis_loss)
tf.summary.scalar('dis_loss_fake_sequence', dis_loss_fake)
tf.summary.scalar('dis_loss_prob_fake_sequence', tf.exp(-dis_loss_fake))
tf.summary.scalar('dis_loss_real_sequence', dis_loss_real)
tf.summary.scalar('dis_loss_prob_real_sequence', tf.exp(-dis_loss_real))
if critic_loss is not None:
with tf.name_scope('critic_losses'):
tf.summary.scalar('critic_loss', critic_loss)
with tf.name_scope('logits'):
helper.variable_summaries(fake_logits, 'fake_logits')
for v, g in zip(gen_vars, gen_grads):
helper.variable_summaries(v, v.op.name)
helper.variable_summaries(g, 'grad/' + v.op.name)
for v, g in zip(dis_vars, dis_grads):
helper.variable_summaries(v, v.op.name)
helper.variable_summaries(g, 'grad/' + v.op.name)
merge_summaries_op = tf.summary.merge_all()
text_summary_placeholder = tf.placeholder(tf.string)
text_summary_op = tf.summary.text('Samples', text_summary_placeholder)
# Model saver.
saver = tf.train.Saver(keep_checkpoint_every_n_hours=1, max_to_keep=5)
# Named tuple that captures elements of the MaskGAN model.
Model = collections.namedtuple('Model', [
'inputs', 'targets', 'present', 'percent_real_update', 'new_rate',
'fake_sequence', 'fake_logits', 'fake_rewards', 'fake_baselines',
'fake_advantages', 'fake_log_probs', 'fake_predictions',
'real_predictions', 'fake_cross_entropy_losses', 'fake_gen_initial_state',
'fake_gen_final_state', 'eval_initial_state', 'eval_final_state',
'avg_log_perplexity', 'dis_loss', 'gen_loss', 'critic_loss',
'cumulative_rewards', 'dis_train_op', 'gen_train_op', 'gen_pretrain_op',
'dis_pretrain_op', 'merge_summaries_op', 'global_step',
'new_learning_rate', 'learning_rate_update', 'saver', 'text_summary_op',
'text_summary_placeholder'
])
model = Model(
inputs, targets, present, percent_real_update, new_rate, fake_sequence,
fake_logits, fake_rewards, fake_baselines, fake_advantages,
fake_log_probs, fake_predictions, real_predictions,
fake_cross_entropy_losses, fake_gen_initial_state, fake_gen_final_state,
eval_initial_state, eval_final_state, avg_log_perplexity, dis_loss,
gen_loss, critic_loss, cumulative_rewards, dis_train_op, gen_train_op,
gen_pretrain_op, dis_pretrain_op, merge_summaries_op, global_step,
new_learning_rate, learning_rate_update, saver, text_summary_op,
text_summary_placeholder)
return model
def create_MaskGAN(hparams, is_training):
"""Create the MaskGAN model.
Args:
hparams: Hyperparameters for the MaskGAN.
is_training: Boolean indicating operational mode (train/inference).
evaluated with a teacher forcing regime.
Return:
model: Namedtuple for specifying the MaskGAN.
"""
global_step = tf.Variable(0, name='global_step', trainable=False)
new_learning_rate = tf.placeholder(tf.float32, [], name='new_learning_rate')
learning_rate = tf.Variable(0.0, name='learning_rate', trainable=False)
learning_rate_update = tf.assign(learning_rate, new_learning_rate)
new_rate = tf.placeholder(tf.float32, [], name='new_rate')
percent_real_var = tf.Variable(0.0, trainable=False)
percent_real_update = tf.assign(percent_real_var, new_rate)
## Placeholders.
inputs = tf.placeholder(
tf.int32, shape=[FLAGS.batch_size, FLAGS.sequence_length])
targets = tf.placeholder(
tf.int32, shape=[FLAGS.batch_size, FLAGS.sequence_length])
present = tf.placeholder(
tf.bool, shape=[FLAGS.batch_size, FLAGS.sequence_length])
# TODO(adai): Placeholder for IMDB label.
## Real Sequence is the targets.
real_sequence = targets
## Fakse Sequence from the Generator.
# TODO(adai): Generator must have IMDB labels placeholder.
(fake_sequence, fake_logits, fake_log_probs, fake_gen_initial_state,
fake_gen_final_state, _) = model_construction.create_generator(
hparams,
inputs,
targets,
present,
is_training=is_training,
is_validating=False)
(_, eval_logits, _, eval_initial_state, eval_final_state,
_) = model_construction.create_generator(
hparams,
inputs,
targets,
present,
is_training=False,
is_validating=True,
reuse=True)
## Discriminator.
fake_predictions = model_construction.create_discriminator(
hparams,
fake_sequence,
is_training=is_training,
inputs=inputs,
present=present)
real_predictions = model_construction.create_discriminator(
hparams,
real_sequence,
is_training=is_training,
reuse=True,
inputs=inputs,
present=present)
## Critic.
# The critic will be used to estimate the forward rewards to the Generator.
if FLAGS.baseline_method == 'critic':
est_state_values = model_construction.create_critic(
hparams, fake_sequence, is_training=is_training)
else:
est_state_values = None
## Discriminator Loss.
[dis_loss, dis_loss_fake, dis_loss_real] = model_losses.create_dis_loss(
fake_predictions, real_predictions, present)
## Average log-perplexity for only missing words. However, to do this,
# the logits are still computed using teacher forcing, that is, the ground
# truth tokens are fed in at each time point to be valid.
avg_log_perplexity = model_losses.calculate_log_perplexity(
eval_logits, targets, present)
## Generator Objective.
# 1. Cross Entropy losses on missing tokens.
fake_cross_entropy_losses = model_losses.create_masked_cross_entropy_loss(
targets, present, fake_logits)
# 2. GAN REINFORCE losses.
[
fake_RL_loss, fake_log_probs, fake_rewards, fake_advantages,
fake_baselines, fake_averages_op, critic_loss, cumulative_rewards
] = model_losses.calculate_reinforce_objective(
hparams, fake_log_probs, fake_predictions, present, est_state_values)
## Pre-training.
if FLAGS.gen_pretrain_steps:
raise NotImplementedError
# # TODO(liamfedus): Rewrite this.
# fwd_cross_entropy_loss = tf.reduce_mean(fwd_cross_entropy_losses)
# gen_pretrain_op = model_optimization.create_gen_pretrain_op(
# hparams, fwd_cross_entropy_loss, global_step)
else:
gen_pretrain_op = None
if FLAGS.dis_pretrain_steps:
dis_pretrain_op = model_optimization.create_dis_pretrain_op(
hparams, dis_loss, global_step)
else:
dis_pretrain_op = None
## Generator Train Op.
# 1. Cross-Entropy.
if FLAGS.gen_training_strategy == 'cross_entropy':
gen_loss = tf.reduce_mean(fake_cross_entropy_losses)
[gen_train_op, gen_grads,
gen_vars] = model_optimization.create_gen_train_op(
hparams, learning_rate, gen_loss, global_step, mode='MINIMIZE')
# 2. GAN (REINFORCE)
elif FLAGS.gen_training_strategy == 'reinforce':
gen_loss = fake_RL_loss
[gen_train_op, gen_grads,
gen_vars] = model_optimization.create_reinforce_gen_train_op(
hparams, learning_rate, gen_loss, fake_averages_op, global_step)
else:
raise NotImplementedError
## Discriminator Train Op.
dis_train_op, dis_grads, dis_vars = model_optimization.create_dis_train_op(
hparams, dis_loss, global_step)
## Critic Train Op.
if critic_loss is not None:
[critic_train_op, _, _] = model_optimization.create_critic_train_op(
hparams, critic_loss, global_step)
dis_train_op = tf.group(dis_train_op, critic_train_op)
## Summaries.
with tf.name_scope('general'):
tf.summary.scalar('percent_real', percent_real_var)
tf.summary.scalar('learning_rate', learning_rate)
with tf.name_scope('generator_objectives'):
tf.summary.scalar('gen_objective', tf.reduce_mean(gen_loss))
tf.summary.scalar('gen_loss_cross_entropy',
tf.reduce_mean(fake_cross_entropy_losses))
with tf.name_scope('REINFORCE'):
with tf.name_scope('objective'):
tf.summary.scalar('fake_RL_loss', tf.reduce_mean(fake_RL_loss))
with tf.name_scope('rewards'):
helper.variable_summaries(cumulative_rewards, 'rewards')
with tf.name_scope('advantages'):
helper.variable_summaries(fake_advantages, 'advantages')
with tf.name_scope('baselines'):
helper.variable_summaries(fake_baselines, 'baselines')
with tf.name_scope('log_probs'):
helper.variable_summaries(fake_log_probs, 'log_probs')
with tf.name_scope('discriminator_losses'):
tf.summary.scalar('dis_loss', dis_loss)
tf.summary.scalar('dis_loss_fake_sequence', dis_loss_fake)
tf.summary.scalar('dis_loss_prob_fake_sequence', tf.exp(-dis_loss_fake))
tf.summary.scalar('dis_loss_real_sequence', dis_loss_real)
tf.summary.scalar('dis_loss_prob_real_sequence', tf.exp(-dis_loss_real))
if critic_loss is not None:
with tf.name_scope('critic_losses'):
tf.summary.scalar('critic_loss', critic_loss)
with tf.name_scope('logits'):
helper.variable_summaries(fake_logits, 'fake_logits')
for v, g in zip(gen_vars, gen_grads):
helper.variable_summaries(v, v.op.name)
helper.variable_summaries(g, 'grad/' + v.op.name)
for v, g in zip(dis_vars, dis_grads):
helper.variable_summaries(v, v.op.name)
helper.variable_summaries(g, 'grad/' + v.op.name)
merge_summaries_op = tf.summary.merge_all()
text_summary_placeholder = tf.placeholder(tf.string)
text_summary_op = tf.summary.text('Samples', text_summary_placeholder)
# Model saver.
saver = tf.train.Saver(keep_checkpoint_every_n_hours=1, max_to_keep=5)
# Named tuple that captures elements of the MaskGAN model.
Model = collections.namedtuple('Model', [
'inputs', 'targets', 'present', 'percent_real_update', 'new_rate',
'fake_sequence', 'fake_logits', 'fake_rewards', 'fake_baselines',
'fake_advantages', 'fake_log_probs', 'fake_predictions',
'real_predictions', 'fake_cross_entropy_losses', 'fake_gen_initial_state',
'fake_gen_final_state', 'eval_initial_state', 'eval_final_state',
'avg_log_perplexity', 'dis_loss', 'gen_loss', 'critic_loss',
'cumulative_rewards', 'dis_train_op', 'gen_train_op', 'gen_pretrain_op',
'dis_pretrain_op', 'merge_summaries_op', 'global_step',
'new_learning_rate', 'learning_rate_update', 'saver', 'text_summary_op',
'text_summary_placeholder'
])
model = Model(
inputs, targets, present, percent_real_update, new_rate, fake_sequence,
fake_logits, fake_rewards, fake_baselines, fake_advantages,
fake_log_probs, fake_predictions, real_predictions,
fake_cross_entropy_losses, fake_gen_initial_state, fake_gen_final_state,
eval_initial_state, eval_final_state, avg_log_perplexity, dis_loss,
gen_loss, critic_loss, cumulative_rewards, dis_train_op, gen_train_op,
gen_pretrain_op, dis_pretrain_op, merge_summaries_op, global_step,
new_learning_rate, learning_rate_update, saver, text_summary_op,
text_summary_placeholder)
return model