def train(self, num_steps_limit, checkpoint_steps, checkpoint_path):
global_step_ev = tf.compat.v1.train.global_step(self.sess, self.global_step)
best_eval_metric = (0.0 if self.eval_metric_type == 'acc' else 1.e16)
while global_step_ev <= num_steps_limit:
if global_step_ev % checkpoint_steps == 0:
# evaluating model when checkpointing
eval_tr_metric_ev, eval_val_metric_ev = utils.evaluate_and_average(
self.sess, [self.eval_tr_metric, self.eval_val_metric], 10)
print("[ Step: {1} meta-valid context_{0}: {2:.5f}, "
"meta-valid target_{0}: {3:.5f} ]".format(self.eval_metric_type,
global_step_ev, eval_tr_metric_ev, eval_val_metric_ev))
# copy best checkpoints for early stopping
if self.eval_metric_type == 'acc':
if eval_val_metric_ev > best_eval_metric:
utils.copy_checkpoint(checkpoint_path, global_step_ev,
eval_val_metric_ev, eval_metric_type=self.eval_metric_type)
best_eval_metric = eval_val_metric_ev
else:
if eval_val_metric_ev < best_eval_metric:
utils.copy_checkpoint(checkpoint_path, global_step_ev,
eval_val_metric_ev, eval_metric_type=self.eval_metric_type)
best_eval_metric = eval_val_metric_ev
self.visualise(save_name="{0}-{1}".format(self.name, global_step_ev))
if global_step_ev == num_steps_limit:
global_step_ev += 1
continue
# train step
_, train_tr_metric_ev, train_val_metric_ev = self.sess.run([self.train_op, self.train_tr_metric, self.train_val_metric])
global_step_ev = tf.compat.v1.train.global_step(self.sess, self.global_step)
def run_training_loop(checkpoint_path, a, b, layers):
"""Runs the training loop, either saving a checkpoint or evaluating it."""
outer_model_config = config.get_outer_model_config()
tf.logging.set_verbosity(tf.logging.INFO)
tf.logging.info("outer_model_config: {}".format(outer_model_config))
(train_op, global_step, metatrain_accuracy, metavalid_accuracy,
metatest_accuracy) = construct_graph(outer_model_config, a, b, layers)
num_steps_limit = outer_model_config["num_steps_limit"]
best_metavalid_accuracy = 0.
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.train.MonitoredTrainingSession(
checkpoint_dir=checkpoint_path,
save_summaries_steps=FLAGS.checkpoint_steps,
log_step_count_steps=FLAGS.checkpoint_steps,
save_checkpoint_steps=FLAGS.checkpoint_steps,
# summary_dir=checkpoint_path,
config=tf_config) as sess:
if not FLAGS.evaluation_mode:
global_step_ev = sess.run(global_step)
while global_step_ev < num_steps_limit:
if global_step_ev % FLAGS.checkpoint_steps == 0:
# Just after saving checkpoint, calculate accuracy 10 times and save
# the best checkpoint for early stopping.
metavalid_accuracy_ev = utils.evaluate_and_average(
sess, metavalid_accuracy, 10)
tf.logging.info("Step: {} meta-valid accuracy: {}".format(
global_step_ev, metavalid_accuracy_ev))
if metavalid_accuracy_ev > best_metavalid_accuracy:
utils.copy_checkpoint(checkpoint_path, global_step_ev,
metavalid_accuracy_ev)
best_metavalid_accuracy = metavalid_accuracy_ev
_, global_step_ev, metatrain_accuracy_ev = sess.run(
[train_op, global_step, metatrain_accuracy])
if global_step_ev % (FLAGS.checkpoint_steps // 2) == 0:
tf.logging.info("Step: {} meta-train accuracy: {}".format(
global_step_ev, metatrain_accuracy_ev))
else:
assert not FLAGS.checkpoint_steps
num_metatest_estimates = (
10000 // outer_model_config["metatest_batch_size"])
test_accuracy = utils.evaluate_and_average(sess,
metatest_accuracy,
num_metatest_estimates,
has_std=True)
return test_accuracy
def run_training_loop(checkpoint_path):
"""Runs the training loop, either saving a checkpoint or evaluating it."""
outer_model_config = config.get_outer_model_config()
tf.logging.info("outer_model_config: {}".format(outer_model_config))
(train_op, global_step, metatrain_accuracy, metavalid_accuracy,
metatest_accuracy) = construct_graph(outer_model_config)
num_steps_limit = outer_model_config["num_steps_limit"]
best_metavalid_accuracy = 0.
with tf.train.MonitoredTrainingSession(
checkpoint_dir=checkpoint_path,
save_summaries_steps=FLAGS.checkpoint_steps,
log_step_count_steps=FLAGS.checkpoint_steps,
save_checkpoint_steps=FLAGS.checkpoint_steps,
summary_dir=checkpoint_path) as sess:
if not FLAGS.evaluation_mode:
global_step_ev = sess.run(global_step)
while global_step_ev < num_steps_limit:
if global_step_ev % FLAGS.checkpoint_steps == 0:
# Just after saving checkpoint, calculate accuracy 10 times and save
# the best checkpoint for early stopping.
metavalid_accuracy_ev = utils.evaluate_and_average(
sess, metavalid_accuracy, 10)
tf.logging.info("Step: {} meta-valid accuracy: {}".format(
global_step_ev, metavalid_accuracy_ev))
if metavalid_accuracy_ev > best_metavalid_accuracy:
utils.copy_checkpoint(checkpoint_path, global_step_ev,
metavalid_accuracy_ev)
best_metavalid_accuracy = metavalid_accuracy_ev
_, global_step_ev, metatrain_accuracy_ev = sess.run(
[train_op, global_step, metatrain_accuracy])
if global_step_ev % (FLAGS.checkpoint_steps // 2) == 0:
tf.logging.info("Step: {} meta-train accuracy: {}".format(
global_step_ev, metatrain_accuracy_ev))
global_step_ev += 1
else:
# assert not FLAGS.checkpoint_steps
num_metatest_estimates = (
10000 // outer_model_config["metatest_batch_size"])
# num_metatest_estimates = 10
test_accuracy = utils.evaluate_and_average(sess, metatest_accuracy,
num_metatest_estimates)
tf.logging.info("Metatest accuracy: %f", test_accuracy)
with tf.gfile.Open(os.path.join(checkpoint_path, "test_accuracy"),
"wb") as f:
pickle.dump(test_accuracy, f)
def run_training_loop(checkpoint_path):
"""Runs the training loop, either saving a checkpoint or evaluating it."""
outer_model_config = config.get_outer_model_config()
tf.logging.info("outer_model_config: {}".format(outer_model_config))
(train_op, global_step, metatrain_accuracy, metavalid_accuracy,
metatest_accuracy, kl_components, adapted_kl_components, kl_zn, adapted_kl_zn, kl, adapted_kl, latents, adapted_latents, spurious) = construct_graph(outer_model_config)
num_steps_limit = outer_model_config["num_steps_limit"]
best_metavalid_accuracy = 0.
# curate summary
classes_seen = {}
kl_components_hist = []
adapted_kl_components_hist = []
kl_zn_hist = []
adapted_kl_zn_hist = []
kl_hist = []
adapted_kl_hist = []
latents_hist = []
metavalid_accuracy_hist = []
for i in range(5):
latents_hist.append([])
kl_components_hist.append([])
adapted_kl_components_hist.append([])
kl_zn_hist.append([])
adapted_kl_zn_hist.append([])
for j in range(64):
kl_components_hist[i].append([])
adapted_kl_components_hist[i].append([])
latents_hist[i].append([])
with tf.train.MonitoredTrainingSession(
checkpoint_dir=checkpoint_path,
save_summaries_steps=FLAGS.checkpoint_steps,
log_step_count_steps=FLAGS.checkpoint_steps,
save_checkpoint_steps=FLAGS.checkpoint_steps,
summary_dir=checkpoint_path) as sess:
# hooks=[wandb.tensorflow.WandbHook(steps_per_log=10)]) as sess:
if not FLAGS.evaluation_mode:
global_step_ev = sess.run(global_step)
while global_step_ev < num_steps_limit:
if global_step_ev % FLAGS.checkpoint_steps == 0:
# Just after saving checkpoint, calculate accuracy 10 times and save
# the best checkpoint for early stopping.
metavalid_accuracy_ev = utils.evaluate_and_average(
sess, metavalid_accuracy, 1) #runs the session for validation
# kl_components_ev = utils.evaluate(sess, kl_components)
# adapted_kl_components_ev = utils.evaluate(sess, adapted_kl_components)
#
# kl_zn_ev = utils.evaluate(sess, kl_zn)
# adapted_kl_zn_ev = utils.evaluate(sess, adapted_kl_zn)
#
# # why is there only one kl divergence score for eatch batch. The divergence should be per class per component.
#
# kl_ev = utils.evaluate(sess, kl)
# adapted_kl_ev = utils.evaluate(sess, adapted_kl)
#
latents_ev = utils.evaluate(sess, latents)
adapted_latents_ev = utils.evaluate(sess, adapted_latents)
spurious_ev = utils.evaluate(sess, spurious)
# for batch in kl_components_ev:
# for c in batch:
# for components in c:
# for i, component in enumerate(components):
# cl = int(component[0])
# kl_val = component[1]
# if (cl <= 5): # collect data for sampled classes
# # for each component
# kl_components_hist[cl][i].append(kl_val)
# if cl not in classes_seen:
# classes_seen[cl] = 1
#
# for batch in adapted_kl_components_ev:
# for c in batch:
# for components in c:
# for i, component in enumerate(components):
# cl = int(component[0])
# kl_val = component[1]
# if (cl <= 5): # collect data for sampled classes
# # for each class and component
# adapted_kl_components_hist[cl][i].append(kl_val)
#
# for batch in kl_zn_ev: # batch, 5, 2
# for component in batch:
# cl = int(component[0])
# kl_zn_val = component[1]
# if (cl <= 5): # collect data for sampled classes
# kl_zn_hist[cl].append(kl_zn_val)
#
# for batch in adapted_kl_zn_ev: # batch, 5, 2
# for component in batch:
# cl = int(component[0])
# adapted_kl_zn_val = component[1]
# if (cl <= 5): # collect data for sampled classes
# adapted_kl_zn_hist[cl].append(adapted_kl_zn_val)
#
for batch_change, batch_latents in zip(latents_ev - spurious_ev, latents_ev):
for k, c in enumerate(batch_change):
for j, components in enumerate(c):
for i, component in enumerate(components):
cl = int(batch_latents[k][j][i][0])
latent_val = component[1]
if (cl <= 5): # collect data for sampled classes
latents_hist[cl][i].append(latent_val)
#
# ########## Visualize kl history
# _, ax = plt.subplots(5, 2, sharex='col', sharey='row', figsize=(20, 20))
#
# for i in range(5):
# color = iter(cm.rainbow(np.linspace(0, 1, 64)))
# for j in range(64):
# c = next(color)
# val = kl_components_hist[i][j]
# step = range(global_step_ev, global_step_ev+len(val))
# ax[i][0].plot(step, val, c=c) #adds values for each component using a different color
# ax[i][1].plot(step, adapted_kl_components_hist[i][j], c=c)
#
# ax[i][0].set_title('N=' + str(i) + ' log(q(zn|x) / p(z)) ratio for Initial Factors')
# ax[i][1].set_title('N=' + str(i) + ' log(q(zn|x) / p(z)) ratio for Adapted Factors')
# # ax[i][0].legend(list(range(64)))
# ax[i][0].set_ylabel('kl divergence')
#
# ax[4][0].set_xlabel('step')
# ax[4][1].set_xlabel('step')
#
# ######### Visualize kl_zn history
# _, ax_zn = plt.subplots(5, 2, sharex='col', sharey='row', figsize=(20, 20))
#
# for i in range(5):
# color = iter(cm.rainbow(np.linspace(0, 1, 5)))
# c = next(color)
# val = kl_zn_hist[i]
# step = range(global_step_ev, global_step_ev+ len(val))
# ax_zn[i][0].plot(step, val, c=c)
# ax_zn[i][1].plot(step, adapted_kl_zn_hist[i], c=c)
#
# ax_zn[i][0].set_title('N=' + str(i) + ' KL Divergence for Initial Zn for q(zn|x) and p(z)')
# ax_zn[i][1].set_title('N=' + str(i) + ' KL Divergence for Adapted Zn for q(zn|x) and p(z)')
#
# ax_zn[4][0].set_xlabel('step')
# ax_zn[4][1].set_xlabel('step')
#
# ########### Visualize kl divergence for batches
# kl_hist.append(kl_ev.flatten())
# adapted_kl_hist.append(adapted_kl_ev.flatten())
# _, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
# ax1.plot(range(global_step_ev, global_step_ev+ len(kl_hist)), kl_hist)
# ax1.set_title('KL Divergence for Initial q(z|x) and p(z)')
# ########### Visualize adapted kl divergence for batches
# ax2.plot(range(global_step_ev, global_step_ev+ len(adapted_kl_hist)), adapted_kl_hist)
# ax2.set_title('KL Divergence for Adapted q(z|x) and p(z)')
#
# metavalid_accuracy_hist.append(metavalid_accuracy_ev)
# _, metavalid_accuracy_plot = plt.subplots()
# metavalid_accuracy_plot.plot(range(0, len(metavalid_accuracy_hist)), metavalid_accuracy_hist)
# metavalid_accuracy_plot.set_title('Metavalidation Accuracy')
#
#
# Visualize latent history, additionally examine the gradients for the latents
_, ax_latent = plt.subplots(5, sharex='col', figsize=(20, 20))
for i in range(5):
color = iter(cm.rainbow(np.linspace(0, 1, 64)))
for j in range(64):
c = next(color)
step = range(0, len(latents_hist[i][j]))
ax_latent[i].plot(step, latents_hist[i][j], c=c)
ax_latent[i].set_title('class=' + str(i) + ' Change in Latents')
ax_latent[4].set_xlabel('step')
plt.show();
tf.logging.info("Step: {} meta-valid accuracy: {}".format(
global_step_ev, metavalid_accuracy_ev))
if metavalid_accuracy_ev > best_metavalid_accuracy:
utils.copy_checkpoint(checkpoint_path, global_step_ev,
metavalid_accuracy_ev)
best_metavalid_accuracy = metavalid_accuracy_ev
_, global_step_ev, metatrain_accuracy_ev = sess.run(
[train_op, global_step, metatrain_accuracy]) #runs the session for training
if global_step_ev % (FLAGS.checkpoint_steps // 2) == 0:
tf.logging.info("Step: {} meta-train accuracy: {}".format(
global_step_ev, metatrain_accuracy_ev))
else:
assert not FLAGS.checkpoint_steps
num_metatest_estimates = (
10000 // outer_model_config["metatest_batch_size"])
test_accuracy = utils.evaluate_and_average(sess, metatest_accuracy,
num_metatest_estimates) #runs the session for testing
tf.logging.info("Metatest accuracy: %f", test_accuracy)
with tf.gfile.Open(
os.path.join(checkpoint_path, "test_accuracy"), "wb") as f:
pickle.dump(test_accuracy, f)
def run_training_loop(checkpoint_path):
"""Runs the training loop, either saving a checkpoint or evaluating it."""
outer_model_config = config.get_outer_model_config()
tf.logging.info("outer_model_config: {}".format(outer_model_config))
(train_op, global_step, metatrain_accuracy, metavalid_accuracy,
metatest_accuracy, metatrain_dacc, metavalid_dacc, metatest_dacc,
hardness, correct) = construct_graph(outer_model_config)
num_steps_limit = outer_model_config["num_steps_limit"]
best_metavalid_accuracy = 0.
best_metavalid_dacc = 0.
with tf.train.MonitoredTrainingSession(
checkpoint_dir=checkpoint_path,
save_summaries_steps=FLAGS.checkpoint_steps,
log_step_count_steps=FLAGS.checkpoint_steps,
save_checkpoint_steps=FLAGS.checkpoint_steps,
summary_dir=checkpoint_path) as sess:
if not FLAGS.evaluation_mode:
global_step_ev = sess.run(global_step)
while global_step_ev < num_steps_limit:
if global_step_ev % FLAGS.checkpoint_steps == 0:
# Just after saving checkpoint, calculate accuracy 10 times and save
# the best checkpoint for early stopping.
#metavalid_accuracy_ev = utils.evaluate_and_average(
#sess, metavalid_accuracy, 10)
metavalid_accuracy_ev, metavalid_dacc_ev = utils.evaluate_and_average_acc_dacc(
sess, metavalid_accuracy, metavalid_dacc, 10)
tf.logging.info(
"Step: {} meta-valid accuracy: {}, dacc: {} best acc: {} best dacc: {}"
.format(global_step_ev, metavalid_accuracy_ev,
metavalid_dacc_ev, best_metavalid_accuracy,
best_metavalid_dacc))
if metavalid_accuracy_ev > best_metavalid_accuracy:
utils.copy_checkpoint(checkpoint_path, global_step_ev,
metavalid_accuracy_ev)
best_metavalid_accuracy = metavalid_accuracy_ev
if metavalid_dacc_ev > best_metavalid_dacc:
best_metavalid_dacc = metavalid_dacc_ev
_, global_step_ev, metatrain_accuracy_ev = sess.run(
[train_op, global_step, metatrain_accuracy])
if global_step_ev % (FLAGS.checkpoint_steps // 2) == 0:
tf.logging.info("Step: {} meta-train accuracy: {}".format(
global_step_ev, metatrain_accuracy_ev))
else:
if not FLAGS.hacc:
assert not FLAGS.checkpoint_steps
num_metatest_estimates = (
2000 // outer_model_config["metatest_batch_size"])
# Not changed to dacc yet
test_accuracy = utils.evaluate_and_average(
sess, metatest_accuracy, num_metatest_estimates)
tf.logging.info("Metatest accuracy: %f", test_accuracy)
with tf.gfile.Open(
os.path.join(checkpoint_path, "test_accuracy"),
"wb") as f:
pickle.dump(test_accuracy, f)
else:
all_hardness = []
all_correct = []
for i in range(2000):
hardness_ev, correct_ev = sess.run([hardness, correct])
hardness_ev = [hardness_ev[i, :, i] for i in range(5)]
hardness_ev = np.array(hardness_ev).flatten()
correct_ev = np.array(correct_ev).flatten()
all_hardness.append(hardness_ev)
all_correct.append(correct_ev)
all_hardness = np.array(all_hardness).flatten()
all_correct = np.array(all_correct).flatten()
save_file = {"hardness": all_hardness, "correct": all_correct}
print(all_correct.sum() / len(all_correct))
pickle.dump(save_file, open("hacc/" + FLAGS.config, "wb"))