def main(_):
"""Run td3/ddpg evaluation."""
contrib_eager_python_tfe.enable_eager_execution()
if FLAGS.use_gpu:
tf.device('/device:GPU:0').__enter__()
tf.gfile.MakeDirs(FLAGS.log_dir)
summary_writer = contrib_summary.create_file_writer(
FLAGS.log_dir, flush_millis=10000)
env = gym.make(FLAGS.env)
if FLAGS.wrap_for_absorbing:
env = lfd_envs.AbsorbingWrapper(env)
obs_shape = env.observation_space.shape
act_shape = env.action_space.shape
with tf.variable_scope('actor'):
actor = Actor(obs_shape[0], act_shape[0])
random_reward, _ = do_rollout(
env, actor, None, num_trajectories=10, sample_random=True)
reward_scale = contrib_eager_python_tfe.Variable(1, name='reward_scale')
saver = contrib_eager_python_tfe.Saver(actor.variables + [reward_scale])
last_checkpoint = tf.train.latest_checkpoint(FLAGS.load_dir)
with summary_writer.as_default():
while True:
last_checkpoint = wait_for_next_checkpoint(FLAGS.load_dir,
last_checkpoint)
total_numsteps = int(last_checkpoint.split('-')[-1])
saver.restore(last_checkpoint)
average_reward, average_length = do_rollout(
env, actor, None, noise_scale=0.0, num_trajectories=FLAGS.num_trials)
logging.info(
'Evaluation: average episode length %d, average episode reward %f',
average_length, average_reward)
print('Evaluation: average episode length {}, average episode reward {}'.
format(average_length, average_reward))
with contrib_summary.always_record_summaries():
if reward_scale.numpy() != 1.0:
contrib_summary.scalar(
'reward/scaled', (average_reward - random_reward) /
(reward_scale.numpy() - random_reward),
step=total_numsteps)
contrib_summary.scalar('reward', average_reward, step=total_numsteps)
contrib_summary.scalar('length', average_length, step=total_numsteps)
def restore_model(self, checkpoint_name=None):
""" Function to restore trained model."""
with tf.device(self._device):
dummy_input = tf.constant(
tf.zeros((5, 37, 37, self._num_input_channels)))
dummy_pred = self.call(dummy_input, dummy_input, training=False)
checkpoint_path = join(self._exp_dir, 'checkpoints')
if checkpoint_name is None:
checkpoint_path = tf.train.latest_checkpoint(checkpoint_path)
else:
checkpoint_path = join(checkpoint_path, checkpoint_name)
tfe.Saver(self.variables).restore(checkpoint_path)
def main(_):
tf.enable_eager_execution()
envs = [
'HalfCheetah-v1', 'Hopper-v1', 'Ant-v1', 'Walker2d-v1', 'Reacher-v1'
]
for ienv, env in enumerate(envs):
print('Processing environment %d of %d: %s' %
(ienv + 1, len(envs), env))
h5_filename = os.path.join(FLAGS.src_data_dir, '%s.h5' % env)
trajectories = h5py.File(h5_filename, 'r')
if (set(trajectories.keys()) != set(
['a_B_T_Da', 'len_B', 'obs_B_T_Do', 'r_B_T'])):
raise ValueError('Unexpected key set in file %s' % h5_filename)
replay_buffer = ReplayBuffer()
if env.find('Reacher') > -1:
max_len = 50
else:
max_len = 1000
for i in range(50):
print(' Processing trajectory %d of 50 (len = %d)' %
(i + 1, trajectories['len_B'][i]))
for j in range(trajectories['len_B'][i]):
mask = 1
if j + 1 == trajectories['len_B'][i]:
if trajectories['len_B'][i] == max_len:
mask = 1
else:
mask = 0
replay_buffer.push_back(
trajectories['obs_B_T_Do'][i][j],
trajectories['a_B_T_Da'][i][j],
trajectories['obs_B_T_Do'][i][(j + 1) %
trajectories['len_B'][i]],
[trajectories['r_B_T'][i][j]], [mask],
j == trajectories['len_B'][i] - 1)
replay_buffer_var = contrib_eager_python_tfe.Variable(
'', name='expert_replay_buffer')
saver = contrib_eager_python_tfe.Saver([replay_buffer_var])
odir = os.path.join(FLAGS.dst_data_dir, env)
print('Saving results to checkpoint in directory: %s' % odir)
tf.gfile.MakeDirs(odir)
replay_buffer_var.assign(pickle.dumps(replay_buffer))
saver.save(os.path.join(odir, 'expert_replay_buffer'))
def main(_):
tfe.enable_eager_execution()
if not FLAGS.data_path:
raise ValueError("Must specify --data_path")
corpus = Corpus(FLAGS.data_path)
# TODO(ashankar): Remove _batchify and _get_batch and use the Datasets API
# instead.
train_data = _batchify(corpus.train, FLAGS.batch_size)
eval_data = _batchify(corpus.valid, 10)
have_gpu = tfe.num_gpus() > 0
use_cudnn_rnn = not FLAGS.no_use_cudnn_rnn and have_gpu
with tfe.restore_variables_on_create(
tf.train.latest_checkpoint(FLAGS.logdir)):
with tf.device("/device:GPU:0" if have_gpu else None):
# Make learning_rate a Variable so it can be included in the checkpoint
# and we can resume training with the last saved learning_rate.
learning_rate = tfe.Variable(20.0, name="learning_rate")
sys.stderr.write("learning_rate=%f\n" % learning_rate.numpy())
model = PTBModel(corpus.vocab_size(), FLAGS.embedding_dim,
FLAGS.hidden_dim, FLAGS.num_layers, FLAGS.dropout,
use_cudnn_rnn)
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
best_loss = None
for _ in range(FLAGS.epoch):
train(model, optimizer, train_data, FLAGS.seq_len, FLAGS.clip)
eval_loss = evaluate(model, eval_data)
if not best_loss or eval_loss < best_loss:
if FLAGS.logdir:
tfe.Saver(model.trainable_weights +
[learning_rate]).save(
os.path.join(FLAGS.logdir, "ckpt"))
best_loss = eval_loss
else:
learning_rate.assign(learning_rate / 4.0)
sys.stderr.write(
"eval_loss did not reduce in this epoch, "
"changing learning rate to %f for the next epoch\n" %
learning_rate.numpy())
x = self.block1(inputs)
x = self.block2(x)
x = self.pool(x)
output = self.classifier(x)
output = tf.keras.activations.softmax(output)
return output
if __name__ == '__main__':
model = CNN(num_classes)
model.compile(optimizer=tf.train.AdamOptimizer(learning_rate=0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train,
y_train_ohe,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test_ohe),
verbose=1)
scores = model.evaluate(x_test,
y_test_ohe,
batch_size=batch_size,
verbose=1)
print('Final test loss and accuracy :', scores)
saver = tfe.Saver(model.variables)
saver.save('weights/k_04_02_cnn_block/weights.ckpt')
def main(_):
"""Run td3/ddpg training."""
contrib_eager_python_tfe.enable_eager_execution()
if FLAGS.use_gpu:
tf.device('/device:GPU:0').__enter__()
tf.gfile.MakeDirs(FLAGS.log_dir)
summary_writer = contrib_summary.create_file_writer(FLAGS.log_dir,
flush_millis=10000)
tf.set_random_seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
random.seed(FLAGS.seed)
env = gym.make(FLAGS.env)
env.seed(FLAGS.seed)
if FLAGS.env in ['HalfCheetah-v2', 'Ant-v1']:
rand_actions = int(1e4)
else:
rand_actions = int(1e3)
obs_shape = env.observation_space.shape
act_shape = env.action_space.shape
if FLAGS.algo == 'td3':
model = ddpg_td3.DDPG(obs_shape[0],
act_shape[0],
use_td3=True,
policy_update_freq=2,
actor_lr=1e-3)
else:
model = ddpg_td3.DDPG(obs_shape[0],
act_shape[0],
use_td3=False,
policy_update_freq=1,
actor_lr=1e-4)
replay_buffer_var = contrib_eager_python_tfe.Variable('',
name='replay_buffer')
gym_random_state_var = contrib_eager_python_tfe.Variable(
'', name='gym_random_state')
np_random_state_var = contrib_eager_python_tfe.Variable(
'', name='np_random_state')
py_random_state_var = contrib_eager_python_tfe.Variable(
'', name='py_random_state')
saver = contrib_eager_python_tfe.Saver(
model.variables + [replay_buffer_var] +
[gym_random_state_var, np_random_state_var, py_random_state_var])
tf.gfile.MakeDirs(FLAGS.save_dir)
reward_scale = contrib_eager_python_tfe.Variable(1, name='reward_scale')
eval_saver = contrib_eager_python_tfe.Saver(model.actor.variables +
[reward_scale])
tf.gfile.MakeDirs(FLAGS.eval_save_dir)
last_checkpoint = tf.train.latest_checkpoint(FLAGS.save_dir)
if last_checkpoint is None:
replay_buffer = ReplayBuffer()
total_numsteps = 0
prev_save_timestep = 0
prev_eval_save_timestep = 0
else:
saver.restore(last_checkpoint)
replay_buffer = pickle.loads(zlib.decompress(
replay_buffer_var.numpy()))
total_numsteps = int(last_checkpoint.split('-')[-1])
assert len(replay_buffer) == total_numsteps
prev_save_timestep = total_numsteps
prev_eval_save_timestep = total_numsteps
env.unwrapped.np_random.set_state(
pickle.loads(gym_random_state_var.numpy()))
np.random.set_state(pickle.loads(np_random_state_var.numpy()))
random.setstate(pickle.loads(py_random_state_var.numpy()))
with summary_writer.as_default():
while total_numsteps < FLAGS.training_steps:
rollout_reward, rollout_timesteps = do_rollout(
env,
model.actor,
replay_buffer,
noise_scale=FLAGS.exploration_noise,
rand_actions=rand_actions)
total_numsteps += rollout_timesteps
logging.info('Training: total timesteps %d, episode reward %f',
total_numsteps, rollout_reward)
print('Training: total timesteps {}, episode reward {}'.format(
total_numsteps, rollout_reward))
with contrib_summary.always_record_summaries():
contrib_summary.scalar('reward',
rollout_reward,
step=total_numsteps)
contrib_summary.scalar('length',
rollout_timesteps,
step=total_numsteps)
if len(replay_buffer) >= FLAGS.min_samples_to_start:
for _ in range(rollout_timesteps):
time_step = replay_buffer.sample(
batch_size=FLAGS.batch_size)
batch = TimeStep(*zip(*time_step))
model.update(batch)
if total_numsteps - prev_save_timestep >= FLAGS.save_interval:
replay_buffer_var.assign(
zlib.compress(pickle.dumps(replay_buffer)))
gym_random_state_var.assign(
pickle.dumps(env.unwrapped.np_random.get_state()))
np_random_state_var.assign(
pickle.dumps(np.random.get_state()))
py_random_state_var.assign(pickle.dumps(random.getstate()))
saver.save(os.path.join(FLAGS.save_dir, 'checkpoint'),
global_step=total_numsteps)
prev_save_timestep = total_numsteps
if total_numsteps - prev_eval_save_timestep >= FLAGS.eval_save_interval:
eval_saver.save(os.path.join(FLAGS.eval_save_dir,
'checkpoint'),
global_step=total_numsteps)
prev_eval_save_timestep = total_numsteps
def save_model(self):
""" Function to save trained model."""
tfe.Saver(self.variables).save(join(self._exp_dir, 'checkpoints',
'checkpoints'),
global_step=self._global_step)
def main(_):
"""Run td3/ddpg training."""
contrib_eager_python_tfe.enable_eager_execution()
if FLAGS.use_gpu:
tf.device('/device:GPU:0').__enter__()
tf.gfile.MakeDirs(FLAGS.log_dir)
summary_writer = contrib_summary.create_file_writer(
FLAGS.log_dir, flush_millis=10000)
tf.set_random_seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
random.seed(FLAGS.seed)
env = gym.make(FLAGS.env)
env.seed(FLAGS.seed)
if FLAGS.learn_absorbing:
env = lfd_envs.AbsorbingWrapper(env)
if FLAGS.env in ['HalfCheetah-v2', 'Ant-v1']:
rand_actions = int(1e4)
else:
rand_actions = int(1e3)
obs_shape = env.observation_space.shape
act_shape = env.action_space.shape
subsampling_rate = env._max_episode_steps // FLAGS.trajectory_size # pylint: disable=protected-access
lfd = gail.GAIL(
obs_shape[0] + act_shape[0],
subsampling_rate=subsampling_rate,
gail_loss=FLAGS.gail_loss)
if FLAGS.algo == 'td3':
model = ddpg_td3.DDPG(
obs_shape[0],
act_shape[0],
use_td3=True,
policy_update_freq=2,
actor_lr=FLAGS.actor_lr,
get_reward=lfd.get_reward,
use_absorbing_state=FLAGS.learn_absorbing)
else:
model = ddpg_td3.DDPG(
obs_shape[0],
act_shape[0],
use_td3=False,
policy_update_freq=1,
actor_lr=FLAGS.actor_lr,
get_reward=lfd.get_reward,
use_absorbing_state=FLAGS.learn_absorbing)
random_reward, _ = do_rollout(
env, model.actor, None, num_trajectories=10, sample_random=True)
replay_buffer_var = contrib_eager_python_tfe.Variable(
'', name='replay_buffer')
expert_replay_buffer_var = contrib_eager_python_tfe.Variable(
'', name='expert_replay_buffer')
# Save and restore random states of gym/numpy/python.
# If the job is preempted, it guarantees that it won't affect the results.
# And the results will be deterministic (on CPU) and reproducible.
gym_random_state_var = contrib_eager_python_tfe.Variable(
'', name='gym_random_state')
np_random_state_var = contrib_eager_python_tfe.Variable(
'', name='np_random_state')
py_random_state_var = contrib_eager_python_tfe.Variable(
'', name='py_random_state')
reward_scale = contrib_eager_python_tfe.Variable(1, name='reward_scale')
saver = contrib_eager_python_tfe.Saver(
model.variables + lfd.variables +
[replay_buffer_var, expert_replay_buffer_var, reward_scale] +
[gym_random_state_var, np_random_state_var, py_random_state_var])
tf.gfile.MakeDirs(FLAGS.save_dir)
eval_saver = contrib_eager_python_tfe.Saver(model.actor.variables +
[reward_scale])
tf.gfile.MakeDirs(FLAGS.eval_save_dir)
last_checkpoint = tf.train.latest_checkpoint(FLAGS.save_dir)
if last_checkpoint is None:
expert_saver = contrib_eager_python_tfe.Saver([expert_replay_buffer_var])
last_checkpoint = os.path.join(FLAGS.expert_dir, 'expert_replay_buffer')
expert_saver.restore(last_checkpoint)
expert_replay_buffer = pickle.loads(expert_replay_buffer_var.numpy())
expert_reward = expert_replay_buffer.get_average_reward()
logging.info('Expert reward %f', expert_reward)
print('Expert reward {}'.format(expert_reward))
reward_scale.assign(expert_reward)
expert_replay_buffer.subsample_trajectories(FLAGS.num_expert_trajectories)
if FLAGS.learn_absorbing:
expert_replay_buffer.add_absorbing_states(env)
# Subsample after adding absorbing states, because otherwise we can lose
# final states.
print('Original dataset size {}'.format(len(expert_replay_buffer)))
expert_replay_buffer.subsample_transitions(subsampling_rate)
print('Subsampled dataset size {}'.format(len(expert_replay_buffer)))
replay_buffer = ReplayBuffer()
total_numsteps = 0
prev_save_timestep = 0
prev_eval_save_timestep = 0
else:
saver.restore(last_checkpoint)
replay_buffer = pickle.loads(zlib.decompress(replay_buffer_var.numpy()))
expert_replay_buffer = pickle.loads(
zlib.decompress(expert_replay_buffer_var.numpy()))
total_numsteps = int(last_checkpoint.split('-')[-1])
prev_save_timestep = total_numsteps
prev_eval_save_timestep = total_numsteps
env.unwrapped.np_random.set_state(
pickle.loads(gym_random_state_var.numpy()))
np.random.set_state(pickle.loads(np_random_state_var.numpy()))
random.setstate(pickle.loads(py_random_state_var.numpy()))
with summary_writer.as_default():
while total_numsteps < FLAGS.training_steps:
# Decay helps to make the model more stable.
# TODO(agrawalk): Use tf.train.exponential_decay
model.actor_lr.assign(
model.initial_actor_lr * pow(0.5, total_numsteps // 100000))
logging.info('Learning rate %f', model.actor_lr.numpy())
rollout_reward, rollout_timesteps = do_rollout(
env,
model.actor,
replay_buffer,
noise_scale=FLAGS.exploration_noise,
rand_actions=rand_actions,
sample_random=(model.actor_step.numpy() == 0),
add_absorbing_state=FLAGS.learn_absorbing)
total_numsteps += rollout_timesteps
logging.info('Training: total timesteps %d, episode reward %f',
total_numsteps, rollout_reward)
print('Training: total timesteps {}, episode reward {}'.format(
total_numsteps, rollout_reward))
with contrib_summary.always_record_summaries():
contrib_summary.scalar(
'reward/scaled', (rollout_reward - random_reward) /
(reward_scale.numpy() - random_reward),
step=total_numsteps)
contrib_summary.scalar('reward', rollout_reward, step=total_numsteps)
contrib_summary.scalar('length', rollout_timesteps, step=total_numsteps)
if len(replay_buffer) >= FLAGS.min_samples_to_start:
for _ in range(rollout_timesteps):
time_step = replay_buffer.sample(batch_size=FLAGS.batch_size)
batch = TimeStep(*zip(*time_step))
time_step = expert_replay_buffer.sample(batch_size=FLAGS.batch_size)
expert_batch = TimeStep(*zip(*time_step))
lfd.update(batch, expert_batch)
for _ in range(FLAGS.updates_per_step * rollout_timesteps):
time_step = replay_buffer.sample(batch_size=FLAGS.batch_size)
batch = TimeStep(*zip(*time_step))
model.update(
batch,
update_actor=model.critic_step.numpy() >=
FLAGS.policy_updates_delay)
if total_numsteps - prev_save_timestep >= FLAGS.save_interval:
replay_buffer_var.assign(zlib.compress(pickle.dumps(replay_buffer)))
expert_replay_buffer_var.assign(
zlib.compress(pickle.dumps(expert_replay_buffer)))
gym_random_state_var.assign(
pickle.dumps(env.unwrapped.np_random.get_state()))
np_random_state_var.assign(pickle.dumps(np.random.get_state()))
py_random_state_var.assign(pickle.dumps(random.getstate()))
saver.save(
os.path.join(FLAGS.save_dir, 'checkpoint'),
global_step=total_numsteps)
prev_save_timestep = total_numsteps
if total_numsteps - prev_eval_save_timestep >= FLAGS.eval_save_interval:
eval_saver.save(
os.path.join(FLAGS.eval_save_dir, 'checkpoint'),
global_step=total_numsteps)
prev_eval_save_timestep = total_numsteps
def main(_):
"""Run td3/ddpg training."""
tfe.enable_eager_execution()
if FLAGS.use_gpu:
tf.device('/device:GPU:0').__enter__()
if FLAGS.expert_dir.find(FLAGS.env) == -1:
raise ValueError('Expert directory must contain the environment name')
tf.set_random_seed(FLAGS.seed)
np.random.seed(FLAGS.seed)
random.seed(FLAGS.seed)
env = gym.make(FLAGS.env)
env.seed(FLAGS.seed)
obs_shape = env.observation_space.shape
act_shape = env.action_space.shape
expert_replay_buffer_var = tfe.Variable('', name='expert_replay_buffer')
saver = tfe.Saver([expert_replay_buffer_var])
tf.gfile.MakeDirs(FLAGS.save_dir)
with tf.variable_scope('actor'):
actor = Actor(obs_shape[0], act_shape[0])
expert_saver = tfe.Saver(actor.variables)
best_checkpoint = None
best_reward = float('-inf')
checkpoint_state = tf.train.get_checkpoint_state(FLAGS.expert_dir)
for checkpoint in checkpoint_state.all_model_checkpoint_paths:
expert_saver.restore(checkpoint)
expert_reward, _ = do_rollout(env,
actor,
replay_buffer=None,
noise_scale=0.0,
num_trajectories=10)
if expert_reward > best_reward:
best_reward = expert_reward
best_checkpoint = checkpoint
expert_saver.restore(best_checkpoint)
expert_replay_buffer = ReplayBuffer()
expert_reward, _ = do_rollout(
env,
actor,
replay_buffer=expert_replay_buffer,
noise_scale=0.0,
num_trajectories=FLAGS.num_expert_trajectories)
logging.info('Expert reward %f', expert_reward)
print('Expert reward {}'.format(expert_reward))
expert_replay_buffer_var.assign(pickle.dumps(expert_replay_buffer))
saver.save(os.path.join(FLAGS.save_dir, 'expert_replay_buffer'))
def run(self,
training_dataset,
validation_dataset,
optimizer,
args,
tensorboard=False):
#The only option :) tensorboard = True
if tensorboard == True:
writer = Writer(args, path=args['exp_dir'] + '/runs')
else:
print('Please use tensorboard!')
return 0
with tf.contrib.summary.record_summaries_every_n_global_steps(1):
with tf.device(self._device):
for itx in tqdm(range(
0, args['num_iterations'] // args['val_freq']),
desc='iterations:'):
try:
print("training...")
# Training iterations.
train_loss = 0.0
for i in range(args['val_freq']):
left_patches, right_patches, labels = training_dataset.iterator.get_next(
)
batch = Batch(left_patches, right_patches, labels)
grads, t_loss = self.compute(batch, training=True)
optimizer.apply_gradients(
zip(grads, self.variables))
train_loss += t_loss
writer.log_to_tensorboard('train_loss',
train_loss /
args['val_freq'],
step=self._global_step)
print('validating...')
# Validation iterations.
error_pixel_2 = 0.0
error_pixel_3 = 0.0
error_pixel_4 = 0.0
error_pixel_5 = 0.0
for i, idx in enumerate(validation_dataset.sample_ids):
left_image, right_image, disparity_ground_truth = validation_dataset.left_images[
idx], validation_dataset.right_images[
idx], validation_dataset.disparity_images[
idx]
paddings = validation_dataset.get_paddings()
left_image = tf.pad(tf.expand_dims(left_image, 0),
paddings, "CONSTANT")
right_image = tf.pad(
tf.expand_dims(right_image, 0), paddings,
"CONSTANT")
batch = Batch(left_image, right_image,
disparity_ground_truth)
disparity_prediction = self.compute(batch,
training=False)
valid_gt_pixels = (disparity_ground_truth !=
0).astype('float')
masked_prediction_valid = disparity_prediction * valid_gt_pixels
num_valid_gt_pixels = valid_gt_pixels.sum()
error_pixel_2 += (np.abs(masked_prediction_valid -
disparity_ground_truth) >
2).sum() / num_valid_gt_pixels
error_pixel_3 += (np.abs(masked_prediction_valid -
disparity_ground_truth) >
3).sum() / num_valid_gt_pixels
error_pixel_4 += (np.abs(masked_prediction_valid -
disparity_ground_truth) >
4).sum() / num_valid_gt_pixels
error_pixel_5 += (np.abs(masked_prediction_valid -
disparity_ground_truth) >
5).sum() / num_valid_gt_pixels
#print('----------validation summary--------------')
#print(error_pixel_2 / len(validation_dataset.sample_ids))
#print(error_pixel_3 / len(validation_dataset.sample_ids))
#print(error_pixel_4 / len(validation_dataset.sample_ids))
#print(error_pixel_5 / len(validation_dataset.sample_ids))
writer.log_to_tensorboard(
'error', (error_pixel_2, error_pixel_3,
error_pixel_4, error_pixel_5,
len(validation_dataset.sample_ids)),
step=self._global_step)
print('Saving one random prediciton...')
random_img_idx = np.random.choice(
validation_dataset.sample_ids)
sample_left_image, sample_right_image, disparity_prediction = validation_dataset.left_images[
random_img_idx], validation_dataset.right_images[
random_img_idx], validation_dataset.disparity_images[
random_img_idx]
paddings = validation_dataset.get_paddings()
sample_left_image = tf.pad(
tf.expand_dims(sample_left_image, 0), paddings,
"CONSTANT")
sample_right_image = tf.pad(
tf.expand_dims(sample_right_image, 0), paddings,
"CONSTANT")
batch = Batch(sample_left_image, sample_right_image,
disparity_prediction)
disparity_prediction = self.compute(batch,
training=False)
writer.log_to_tensorboard(
'qualitative',
(disparity_prediction, sample_left_image,
sample_right_image),
step=self._global_step)
self._global_step.assign_add(args['val_freq'])
# Save checkpoint.
tfe.Saver(self.variables).save(
join(self._exp_dir, 'checkpoints', 'checkpoints'),
global_step=self._global_step)
except tf.errors.OutOfRangeError:
break
break
# evaluate after epoch
iterator = test_dataset.make_one_shot_iterator(
) # dont repeat any values from test set
for x, y in iterator:
preds = model(x)
loss = tf.losses.mean_squared_error(y, preds[:, 0])
test_loss(loss)
print("Epoch %d: Train Loss = %0.4f | Test Loss = %0.4f\n" %
(e + 1, train_loss.result(), test_loss.result()))
# Make sure to add not just the "model" variables, but also the custom variables we added !
saver = tfe.Saver(model.variables + list(model.custom_variables.values()))
saver.save('weights/10_01_custom_models/weights.ckpt')
print("Model saved")
# Here we need to reset the keras internal backend first
tf.keras.backend.clear_session()
# Now we restore the model and predict again on test set
model2 = CustomRegressor()
# we need to run the model at least once to build all of the variables and the custom variables
# make sure to build the model the same way, otherwise it wont find the weights in the checkpoints properly
# safest option is to call model.call(tf_input_batch) explicitly
model2.call(dummy_x)
# ensure that you are loading both the Keras variables AND the custom variables