def train_task_sequence(model, sess, datasets, args):
"""
Train and evaluate LLL system such that we only see a example once
Args:
Returns:
dict A dictionary containing mean and stds for the experiment
"""
# List to store accuracy for each run
runs = []
task_labels_dataset = []
if model.imp_method == 'A-GEM' or model.imp_method == 'ER' or model.imp_method == 'MEGA' or model.imp_method == 'MEGAD':
use_episodic_memory = True
else:
use_episodic_memory = False
batch_size = args.batch_size
# Loop over number of runs to average over
for runid in range(args.num_runs):
print('\t\tRun %d:'%(runid))
# Initialize the random seeds
np.random.seed(args.random_seed+runid)
# Get the task labels from the total number of tasks and full label space
task_labels = []
classes_per_task = TOTAL_CLASSES// NUM_TASKS
total_classes = classes_per_task * model.num_tasks
if args.online_cross_val:
label_array = np.arange(total_classes)
else:
class_label_offset = K_FOR_CROSS_VAL * classes_per_task
label_array = np.arange(class_label_offset, total_classes+class_label_offset)
np.random.shuffle(label_array)
for tt in range(model.num_tasks):
tt_offset = tt*classes_per_task
task_labels.append(list(label_array[tt_offset:tt_offset+classes_per_task]))
print('Task: {}, Labels:{}'.format(tt, task_labels[tt]))
# Store the task labels
task_labels_dataset.append(task_labels)
# Set episodic memory size
episodic_mem_size = args.mem_size * total_classes
# Initialize all the variables in the model
sess.run(tf.global_variables_initializer())
# Run the init ops
model.init_updates(sess)
# List to store accuracies for a run
evals = []
# List to store the classes that we have so far - used at test time
test_labels = []
if use_episodic_memory:
# Reserve a space for episodic memory
episodic_images = np.zeros([episodic_mem_size, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS])
episodic_labels = np.zeros([episodic_mem_size, TOTAL_CLASSES])
episodic_filled_counter = 0
nd_logit_mask = np.zeros([model.num_tasks, TOTAL_CLASSES])
count_cls = np.zeros(TOTAL_CLASSES, dtype=np.int32)
episodic_filled_counter = 0
examples_seen_so_far = 0
# Mask for softmax
logit_mask = np.zeros(TOTAL_CLASSES)
if COUNT_VIOLATONS:
violation_count = np.zeros(model.num_tasks)
vc = 0
# Training loop for all the tasks
for task in range(len(task_labels)):
print('\t\tTask %d:'%(task))
# If not the first task then restore weights from previous task
if(task > 0 and model.imp_method != 'PNN'):
model.restore(sess)
if model.imp_method == 'PNN':
pnn_train_phase = np.array(np.zeros(model.num_tasks), dtype=np.bool)
pnn_train_phase[task] = True
pnn_logit_mask = np.zeros([model.num_tasks, TOTAL_CLASSES])
# If not in the cross validation mode then concatenate the train and validation sets
task_tr_images, task_tr_labels = load_task_specific_data(datasets[0]['train'], task_labels[task])
task_val_images, task_val_labels = load_task_specific_data(datasets[0]['validation'], task_labels[task])
task_train_images, task_train_labels = concatenate_datasets(task_tr_images, task_tr_labels, task_val_images, task_val_labels)
# If multi_task is set then train using all the datasets of all the tasks
if MULTI_TASK:
if task == 0:
for t_ in range(1, len(task_labels)):
task_tr_images, task_tr_labels = load_task_specific_data(datasets[0]['train'], task_labels[t_])
task_train_images = np.concatenate((task_train_images, task_tr_images), axis=0)
task_train_labels = np.concatenate((task_train_labels, task_tr_labels), axis=0)
else:
# Skip training for this task
continue
print('Received {} images, {} labels at task {}'.format(task_train_images.shape[0], task_train_labels.shape[0], task))
print('Unique labels in the task: {}'.format(np.unique(np.nonzero(task_train_labels)[1])))
# Test for the tasks that we've seen so far
test_labels += task_labels[task]
# Assign equal weights to all the examples
task_sample_weights = np.ones([task_train_labels.shape[0]], dtype=np.float32)
num_train_examples = task_train_images.shape[0]
logit_mask[:] = 0
# Train a task observing sequence of data
if args.train_single_epoch:
# Ceiling operation
num_iters = (num_train_examples + batch_size - 1) // batch_size
if args.cross_validate_mode:
logit_mask[task_labels[task]] = 1.0
else:
num_iters = args.train_iters
# Set the mask only once before starting the training for the task
logit_mask[task_labels[task]] = 1.0
if MULTI_TASK:
logit_mask[:] = 1.0
# Randomly suffle the training examples
perm = np.arange(num_train_examples)
np.random.shuffle(perm)
train_x = task_train_images[perm]
train_y = task_train_labels[perm]
task_sample_weights = task_sample_weights[perm]
# Array to store accuracies when training for task T
ftask = []
# Number of iterations after which convergence is checked
convergence_iters = int(num_iters * MEASURE_CONVERGENCE_AFTER)
# Training loop for task T
for iters in range(num_iters):
if args.train_single_epoch and not args.cross_validate_mode and not MULTI_TASK:
if (iters <= 20) or (iters > 20 and iters % 50 == 0):
# Snapshot the current performance across all tasks after each mini-batch
fbatch = test_task_sequence(model, sess, datasets[0]['test'], task_labels, task)
ftask.append(fbatch)
if model.imp_method == 'PNN':
pnn_train_phase[:] = False
pnn_train_phase[task] = True
pnn_logit_mask[:] = 0
pnn_logit_mask[task][task_labels[task]] = 1.0
elif model.imp_method == 'A-GEM' or model.imp_method == 'MEGA' or model.imp_method == 'MEGAD':
nd_logit_mask[:] = 0
nd_logit_mask[task][task_labels[task]] = 1.0
else:
# Set the output labels over which the model needs to be trained
logit_mask[:] = 0
logit_mask[task_labels[task]] = 1.0
if args.train_single_epoch:
offset = iters * batch_size
if (offset+batch_size <= num_train_examples):
residual = batch_size
else:
residual = num_train_examples - offset
if model.imp_method == 'PNN':
feed_dict = {model.x: train_x[offset:offset+residual], model.y_[task]: train_y[offset:offset+residual],
model.training_iters: num_iters, model.train_step: iters, model.keep_prob: 0.5}
train_phase_dict = {m_t: i_t for (m_t, i_t) in zip(model.train_phase, pnn_train_phase)}
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, pnn_logit_mask)}
feed_dict.update(train_phase_dict)
feed_dict.update(logit_mask_dict)
else:
feed_dict = {model.x: train_x[offset:offset+residual], model.y_: train_y[offset:offset+residual],
model.sample_weights: task_sample_weights[offset:offset+residual],
model.training_iters: num_iters, model.train_step: iters, model.keep_prob: 0.5,
model.train_phase: True}
else:
offset = (iters * batch_size) % (num_train_examples - batch_size)
if model.imp_method == 'PNN':
feed_dict = {model.x: train_x[offset:offset+batch_size], model.y_[task]: train_y[offset:offset+batch_size],
model.training_iters: num_iters, model.train_step: iters, model.keep_prob: 0.5}
train_phase_dict = {m_t: i_t for (m_t, i_t) in zip(model.train_phase, pnn_train_phase)}
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, pnn_logit_mask)}
feed_dict.update(train_phase_dict)
feed_dict.update(logit_mask_dict)
else:
feed_dict = {model.x: train_x[offset:offset+batch_size], model.y_: train_y[offset:offset+batch_size],
model.sample_weights: task_sample_weights[offset:offset+batch_size],
model.training_iters: num_iters, model.train_step: iters, model.keep_prob: 0.5,
model.train_phase: True}
if model.imp_method == 'VAN':
feed_dict[model.output_mask] = logit_mask
_, loss = sess.run([model.train, model.reg_loss], feed_dict=feed_dict)
elif model.imp_method == 'PNN':
_, loss = sess.run([model.train[task], model.unweighted_entropy[task]], feed_dict=feed_dict)
elif model.imp_method == 'FTR_EXT':
feed_dict[model.output_mask] = logit_mask
if task == 0:
_, loss = sess.run([model.train, model.reg_loss], feed_dict=feed_dict)
else:
_, loss = sess.run([model.train_classifier, model.reg_loss], feed_dict=feed_dict)
elif model.imp_method == 'EWC' or model.imp_method == 'M-EWC':
feed_dict[model.output_mask] = logit_mask
# If first iteration of the first task then set the initial value of the running fisher
if task == 0 and iters == 0:
sess.run([model.set_initial_running_fisher], feed_dict=feed_dict)
# Update fisher after every few iterations
if (iters + 1) % model.fisher_update_after == 0:
sess.run(model.set_running_fisher)
sess.run(model.reset_tmp_fisher)
if (iters >= convergence_iters) and (model.imp_method == 'M-EWC'):
_, _, _, _, loss = sess.run([model.weights_old_ops_grouped, model.set_tmp_fisher, model.train, model.update_small_omega,
model.reg_loss], feed_dict=feed_dict)
else:
_, _, loss = sess.run([model.set_tmp_fisher, model.train, model.reg_loss], feed_dict=feed_dict)
elif model.imp_method == 'PI':
feed_dict[model.output_mask] = logit_mask
_, _, _, loss = sess.run([model.weights_old_ops_grouped, model.train, model.update_small_omega,
model.reg_loss], feed_dict=feed_dict)
elif model.imp_method == 'MAS':
feed_dict[model.output_mask] = logit_mask
_, loss = sess.run([model.train, model.reg_loss], feed_dict=feed_dict)
elif model.imp_method == 'A-GEM':
if task == 0:
nd_logit_mask[:] = 0
nd_logit_mask[task][task_labels[task]] = 1.0
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, nd_logit_mask)}
feed_dict.update(logit_mask_dict)
feed_dict[model.mem_batch_size] = batch_size
# Normal application of gradients
_, loss = sess.run([model.train_first_task, model.agem_loss], feed_dict=feed_dict)
else:
## Compute and store the reference gradients on the previous tasks
# Set the mask for all the previous tasks so far
nd_logit_mask[:] = 0
for tt in range(task):
nd_logit_mask[tt][task_labels[tt]] = 1.0
if episodic_filled_counter <= args.eps_mem_batch:
mem_sample_mask = np.arange(episodic_filled_counter)
else:
# Sample a random subset from episodic memory buffer
mem_sample_mask = np.random.choice(episodic_filled_counter, args.eps_mem_batch, replace=False) # Sample without replacement so that we don't sample an example more than once
# Store the reference gradient
ref_feed_dict = {model.x: episodic_images[mem_sample_mask], model.y_: episodic_labels[mem_sample_mask],
model.keep_prob: 1.0, model.train_phase: True}
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, nd_logit_mask)}
ref_feed_dict.update(logit_mask_dict)
ref_feed_dict[model.mem_batch_size] = float(len(mem_sample_mask))
sess.run([model.store_ref_grads], feed_dict=ref_feed_dict)
# Compute the gradient for current task and project if need be
nd_logit_mask[:] = 0
nd_logit_mask[task][task_labels[task]] = 1.0
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, nd_logit_mask)}
feed_dict.update(logit_mask_dict)
feed_dict[model.mem_batch_size] = batch_size
if COUNT_VIOLATONS:
vc, _, loss = sess.run([model.violation_count, model.train_subseq_tasks, model.agem_loss], feed_dict=feed_dict)
else:
_, loss = sess.run([model.train_subseq_tasks, model.agem_loss], feed_dict=feed_dict)
# Put the batch in the ring buffer
for er_x, er_y_ in zip(train_x[offset:offset+residual], train_y[offset:offset+residual]):
cls = np.unique(np.nonzero(er_y_))[-1]
# Write the example at the location pointed by count_cls[cls]
cls_to_index_map = np.where(np.array(task_labels[task]) == cls)[0][0]
with_in_task_offset = args.mem_size * cls_to_index_map
mem_index = count_cls[cls] + with_in_task_offset + episodic_filled_counter
episodic_images[mem_index] = er_x
episodic_labels[mem_index] = er_y_
count_cls[cls] = (count_cls[cls] + 1) % args.mem_size
elif model.imp_method == 'MEGA':
if task == 0:
nd_logit_mask[:] = 0
nd_logit_mask[task][task_labels[task]] = 1.0
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, nd_logit_mask)}
feed_dict.update(logit_mask_dict)
feed_dict[model.mem_batch_size] = batch_size
# Normal application of gradients
_, loss = sess.run([model.train_first_task, model.agem_loss], feed_dict=feed_dict)
else:
## Compute and store the reference gradients on the previous tasks
# Set the mask for all the previous tasks so far
nd_logit_mask[:] = 0
for tt in range(task):
nd_logit_mask[tt][task_labels[tt]] = 1.0
if episodic_filled_counter <= args.eps_mem_batch:
mem_sample_mask = np.arange(episodic_filled_counter)
else:
# Sample a random subset from episodic memory buffer
mem_sample_mask = np.random.choice(episodic_filled_counter, args.eps_mem_batch, replace=False) # Sample without replacement so that we don't sample an example more than once
# Store the reference gradient
ref_feed_dict = {model.x: episodic_images[mem_sample_mask], model.y_: episodic_labels[mem_sample_mask],
model.keep_prob: 1.0, model.train_phase: True}
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, nd_logit_mask)}
ref_feed_dict.update(logit_mask_dict)
ref_feed_dict[model.mem_batch_size] = float(len(mem_sample_mask))
sess.run([model.store_ref_grads, model.store_ref_loss], feed_dict=ref_feed_dict)
# Compute the gradient for current task and project if need be
nd_logit_mask[:] = 0
nd_logit_mask[task][task_labels[task]] = 1.0
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, nd_logit_mask)}
feed_dict.update(logit_mask_dict)
feed_dict[model.mem_batch_size] = batch_size
# Training
_, loss, theta = sess.run([model.train_subseq_tasks, model.agem_loss, model.theta], feed_dict=feed_dict)
# Put the batch in the ring buffer
for er_x, er_y_ in zip(train_x[offset:offset+residual], train_y[offset:offset+residual]):
cls = np.unique(np.nonzero(er_y_))[-1]
# Write the example at the location pointed by count_cls[cls]
cls_to_index_map = np.where(np.array(task_labels[task]) == cls)[0][0]
with_in_task_offset = args.mem_size * cls_to_index_map
mem_index = count_cls[cls] + with_in_task_offset + episodic_filled_counter
episodic_images[mem_index] = er_x
episodic_labels[mem_index] = er_y_
count_cls[cls] = (count_cls[cls] + 1) % args.mem_size
elif model.imp_method == 'MEGAD':
if task == 0:
nd_logit_mask[:] = 0
nd_logit_mask[task][task_labels[task]] = 1.0
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, nd_logit_mask)}
feed_dict.update(logit_mask_dict)
feed_dict[model.mem_batch_size] = batch_size
# Normal application of gradients
_, loss = sess.run([model.train_first_task, model.agem_loss], feed_dict=feed_dict)
else:
## Compute and store the reference gradients on the previous tasks
# Set the mask for all the previous tasks so far
nd_logit_mask[:] = 0
for tt in range(task):
nd_logit_mask[tt][task_labels[tt]] = 1.0
if episodic_filled_counter <= args.eps_mem_batch:
mem_sample_mask = np.arange(episodic_filled_counter)
else:
# Sample a random subset from episodic memory buffer
mem_sample_mask = np.random.choice(episodic_filled_counter, args.eps_mem_batch, replace=False) # Sample without replacement so that we don't sample an example more than once
# Store the reference gradient
ref_feed_dict = {model.x: episodic_images[mem_sample_mask], model.y_: episodic_labels[mem_sample_mask],
model.keep_prob: 1.0, model.train_phase: True}
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, nd_logit_mask)}
ref_feed_dict.update(logit_mask_dict)
ref_feed_dict[model.mem_batch_size] = float(len(mem_sample_mask))
sess.run([model.store_ref_grads, model.store_ref_loss], feed_dict=ref_feed_dict)
# Compute the gradient for current task and project if need be
nd_logit_mask[:] = 0
nd_logit_mask[task][task_labels[task]] = 1.0
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, nd_logit_mask)}
feed_dict.update(logit_mask_dict)
feed_dict[model.mem_batch_size] = batch_size
# Training
_, loss = sess.run([model.train_subseq_tasks, model.agem_loss], feed_dict=feed_dict)
# Put the batch in the ring buffer
for er_x, er_y_ in zip(train_x[offset:offset+residual], train_y[offset:offset+residual]):
cls = np.unique(np.nonzero(er_y_))[-1]
# Write the example at the location pointed by count_cls[cls]
cls_to_index_map = np.where(np.array(task_labels[task]) == cls)[0][0]
with_in_task_offset = args.mem_size * cls_to_index_map
mem_index = count_cls[cls] + with_in_task_offset + episodic_filled_counter
episodic_images[mem_index] = er_x
episodic_labels[mem_index] = er_y_
count_cls[cls] = (count_cls[cls] + 1) % args.mem_size
elif model.imp_method == 'RWALK':
feed_dict[model.output_mask] = logit_mask
# If first iteration of the first task then set the initial value of the running fisher
if task == 0 and iters == 0:
sess.run([model.set_initial_running_fisher], feed_dict=feed_dict)
# Store the current value of the weights
sess.run(model.weights_delta_old_grouped)
# Update fisher and importance score after every few iterations
if (iters + 1) % model.fisher_update_after == 0:
# Update the importance score using distance in riemannian manifold
sess.run(model.update_big_omega_riemann)
# Now that the score is updated, compute the new value for running Fisher
sess.run(model.set_running_fisher)
# Store the current value of the weights
sess.run(model.weights_delta_old_grouped)
# Reset the delta_L
sess.run([model.reset_small_omega])
_, _, _, _, loss = sess.run([model.set_tmp_fisher, model.weights_old_ops_grouped,
model.train, model.update_small_omega, model.reg_loss], feed_dict=feed_dict)
elif model.imp_method == 'ER':
mem_filled_so_far = examples_seen_so_far if (examples_seen_so_far < episodic_mem_size) else episodic_mem_size
if mem_filled_so_far < args.eps_mem_batch:
er_mem_indices = np.arange(mem_filled_so_far)
else:
er_mem_indices = np.random.choice(mem_filled_so_far, args.eps_mem_batch, replace=False)
np.random.shuffle(er_mem_indices)
nd_logit_mask[:] = 0
for tt in range(task+1):
nd_logit_mask[tt][task_labels[tt]] = 1.0
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, nd_logit_mask)}
er_train_x_batch = np.concatenate((episodic_images[er_mem_indices], train_x[offset:offset+residual]), axis=0)
er_train_y_batch = np.concatenate((episodic_labels[er_mem_indices], train_y[offset:offset+residual]), axis=0)
feed_dict = {model.x: er_train_x_batch, model.y_: er_train_y_batch,
model.training_iters: num_iters, model.train_step: iters, model.keep_prob: 1.0,
model.train_phase: True}
feed_dict.update(logit_mask_dict)
feed_dict[model.mem_batch_size] = float(er_train_x_batch.shape[0])
_, loss = sess.run([model.train, model.reg_loss], feed_dict=feed_dict)
# Reservoir update
for er_x, er_y_ in zip(train_x[offset:offset+residual], train_y[offset:offset+residual]):
update_reservior(er_x, er_y_, episodic_images, episodic_labels, episodic_mem_size, examples_seen_so_far)
examples_seen_so_far += 1
if (iters % 100 == 0):
print('Step {:d} {:.3f}'.format(iters, loss))
if (math.isnan(loss)):
print('ERROR: NaNs NaNs NaNs!!!')
sys.exit(0)
print('\t\t\t\tTraining for Task%d done!'%(task))
if use_episodic_memory:
episodic_filled_counter += args.mem_size * classes_per_task
if model.imp_method == 'A-GEM':
if COUNT_VIOLATONS:
violation_count[task] = vc
print('Task {}: Violation Count: {}'.format(task, violation_count))
sess.run(model.reset_violation_count, feed_dict=feed_dict)
# Compute the inter-task updates, Fisher/ importance scores etc
# Don't calculate the task updates for the last task
if (task < (len(task_labels) - 1)) or MEASURE_PERF_ON_EPS_MEMORY:
model.task_updates(sess, task, task_train_images, task_labels[task]) # TODO: For MAS, should the gradients be for current task or all the previous tasks
print('\t\t\t\tTask updates after Task%d done!'%(task))
if VISUALIZE_IMPORTANCE_MEASURE:
if runid == 0:
for i in range(len(model.fisher_diagonal_at_minima)):
if i == 0:
flatten_fisher = np.array(model.fisher_diagonal_at_minima[i].eval()).flatten()
else:
flatten_fisher = np.concatenate((flatten_fisher,
np.array(model.fisher_diagonal_at_minima[i].eval()).flatten()))
#flatten_fisher [flatten_fisher > 0.1] = 0.1
if args.train_single_epoch:
plot_histogram(flatten_fisher, 100, '/private/home/arslanch/Dropbox/LLL_experiments/Single_Epoch/importance_vis/single_epoch/m_ewc/hist_fisher_task%s.png'%(task))
else:
plot_histogram(flatten_fisher, 100, '/private/home/arslanch/Dropbox/LLL_experiments/Single_Epoch/importance_vis/single_epoch/m_ewc/hist_fisher_task%s.png'%(task))
if args.train_single_epoch and not args.cross_validate_mode:
fbatch = test_task_sequence(model, sess, datasets[0]['test'], task_labels, task)
print('Task: {}, Acc: {}'.format(task, fbatch))
ftask.append(fbatch)
ftask = np.array(ftask)
if model.imp_method == 'PNN':
pnn_train_phase[:] = False
pnn_train_phase[task] = True
pnn_logit_mask[:] = 0
pnn_logit_mask[task][task_labels[task]] = 1.0
else:
if MEASURE_PERF_ON_EPS_MEMORY:
eps_mem = {
'images': episodic_images,
'labels': episodic_labels,
}
# Measure perf on episodic memory
ftask = test_task_sequence(model, sess, eps_mem, task_labels, task, classes_per_task=classes_per_task)
else:
# List to store accuracy for all the tasks for the current trained model
ftask = test_task_sequence(model, sess, datasets[0]['test'], task_labels, task)
print('Task: {}, Acc: {}'.format(task, ftask))
# Store the accuracies computed at task T in a list
evals.append(ftask)
# Reset the optimizer
model.reset_optimizer(sess)
#-> End for loop task
runs.append(np.array(evals))
# End for loop runid
runs = np.array(runs)
return runs, task_labels_dataset
def train_task_sequence(model, sess, args):
"""
Train and evaluate LLL system such that we only see a example once
Args:
Returns:
dict A dictionary containing mean and stds for the experiment
"""
# List to store accuracy for each run
runs = []
batch_size = args.batch_size
if model.imp_method in {'A-GEM', 'MER'} or 'ER-' in model.imp_method:
use_episodic_memory = True
else:
use_episodic_memory = False
# Loop over number of runs to average over
for runid in range(args.num_runs):
print('\t\tRun %d:' % (runid))
# Initialize the random seeds
np.random.seed(args.random_seed + runid)
time_start = time.time()
# Load the permute mnist dataset
datasets = construct_permute_mnist(model.num_tasks)
print('Data loading time: {}'.format(time.time() - time_start))
episodic_mem_size = args.mem_size * model.num_tasks * TOTAL_CLASSES
# Initialize all the variables in the model
sess.run(tf.global_variables_initializer())
# Run the init ops
model.init_updates(sess)
# List to store accuracies for a run
evals = []
# List to store the classes that we have so far - used at test time
test_labels = np.arange(TOTAL_CLASSES)
if use_episodic_memory:
# Reserve a space for episodic memory
episodic_images = np.zeros([episodic_mem_size, INPUT_FEATURE_SIZE])
episodic_labels = np.zeros([episodic_mem_size, TOTAL_CLASSES])
count_cls = np.zeros(TOTAL_CLASSES, dtype=np.int32)
episodic_filled_counter = 0
examples_seen_so_far = 0
# Mask for softmax
# Since all the classes are present in all the tasks so nothing to mask
logit_mask = np.ones(TOTAL_CLASSES)
if model.imp_method == 'PNN':
pnn_train_phase = np.array(np.zeros(model.num_tasks),
dtype=np.bool)
pnn_logit_mask = np.ones([model.num_tasks, TOTAL_CLASSES])
if COUNT_VIOLATIONS:
violation_count = np.zeros(model.num_tasks)
vc = 0
# Store the projection matrices for each task
proj_matrices = generate_projection_matrix(
model.num_tasks,
feature_dim=model.subspace_proj.get_shape()[0],
qr=QR)
# Check the sanity of the generated matrices
unit_test_projection_matrices(proj_matrices)
# TODO: Temp for gradients check
prev_task_grads = []
# Training loop for all the tasks
for task in range(len(datasets)):
print('\t\tTask %d:' % (task))
# If not the first task then restore weights from previous task
if (task > 0 and model.imp_method != 'PNN'):
model.restore(sess)
if MULTI_TASK:
if task == 0:
# Extract training images and labels for the current task
task_train_images = datasets[task]['train']['images']
task_train_labels = datasets[task]['train']['labels']
sample_weights = np.ones([task_train_labels.shape[0]],
dtype=np.float32)
total_train_examples = task_train_images.shape[0]
# Randomly suffle the training examples
perm = np.arange(total_train_examples)
np.random.shuffle(perm)
train_x = task_train_images[perm][:args.examples_per_task]
train_y = task_train_labels[perm][:args.examples_per_task]
task_sample_weights = sample_weights[
perm][:args.examples_per_task]
for t_ in range(1, len(datasets)):
task_train_images = datasets[t_]['train']['images']
task_train_labels = datasets[t_]['train']['labels']
sample_weights = np.ones([task_train_labels.shape[0]],
dtype=np.float32)
total_train_examples = task_train_images.shape[0]
# Randomly suffle the training examples
perm = np.arange(total_train_examples)
np.random.shuffle(perm)
train_x = np.concatenate(
(train_x,
task_train_images[perm][:args.examples_per_task]),
axis=0)
train_y = np.concatenate(
(train_y,
task_train_labels[perm][:args.examples_per_task]),
axis=0)
task_sample_weights = np.concatenate(
(task_sample_weights,
sample_weights[perm][:args.examples_per_task]),
axis=0)
perm = np.arange(train_x.shape[0])
np.random.shuffle(perm)
train_x = train_x[perm]
train_y = train_y[perm]
task_sample_weights = task_sample_weights[perm]
else:
# Skip training for this task
continue
else:
# Extract training images and labels for the current task
task_train_images = datasets[task]['train']['images']
task_train_labels = datasets[task]['train']['labels']
# Assign equal weights to all the examples
task_sample_weights = np.ones([task_train_labels.shape[0]],
dtype=np.float32)
total_train_examples = task_train_images.shape[0]
# Randomly suffle the training examples
perm = np.arange(total_train_examples)
np.random.shuffle(perm)
train_x = task_train_images[perm][:args.examples_per_task]
train_y = task_train_labels[perm][:args.examples_per_task]
task_sample_weights = task_sample_weights[
perm][:args.examples_per_task]
print('Received {} images, {} labels at task {}'.format(
train_x.shape[0], train_y.shape[0], task))
# Array to store accuracies when training for task T
ftask = []
num_train_examples = train_x.shape[0]
# Train a task observing sequence of data
if args.train_single_epoch:
num_iters = (num_train_examples + batch_size - 1) // batch_size
else:
num_iters = args.train_iters
# Training loop for task T
for iters in range(num_iters):
if args.train_single_epoch and not args.cross_validate_mode:
if (iters < 10) or (iters < 100
and iters % 10 == 0) or (iters % 100
== 0):
# Snapshot the current performance across all tasks after each mini-batch
fbatch = test_task_sequence(model, sess, datasets,
args.online_cross_val,
proj_matrices)
ftask.append(fbatch)
offset = (iters * batch_size) % num_train_examples
if (offset + batch_size <= num_train_examples):
residual = batch_size
else:
residual = num_train_examples - offset
if model.imp_method == 'PNN':
pnn_train_phase[:] = False
pnn_train_phase[task] = True
feed_dict = {
model.x:
train_x[offset:offset + batch_size],
model.y_[task]:
train_y[offset:offset + batch_size],
model.sample_weights:
task_sample_weights[offset:offset + batch_size],
model.training_iters:
num_iters,
model.train_step:
iters,
model.keep_prob:
1.0,
model.learning_rate:
args.learning_rate
}
train_phase_dict = {
m_t: i_t
for (m_t,
i_t) in zip(model.train_phase, pnn_train_phase)
}
logit_mask_dict = {
m_t: i_t
for (m_t,
i_t) in zip(model.output_mask, pnn_logit_mask)
}
feed_dict.update(train_phase_dict)
feed_dict.update(logit_mask_dict)
else:
feed_dict = {
model.x:
train_x[offset:offset + batch_size],
model.y_:
train_y[offset:offset + batch_size],
model.sample_weights:
task_sample_weights[offset:offset + batch_size],
model.training_iters:
num_iters,
model.train_step:
iters,
model.keep_prob:
1.0,
model.output_mask:
logit_mask,
model.train_phase:
True,
model.learning_rate:
args.learning_rate
}
if model.imp_method == 'VAN':
_, loss = sess.run([model.train, model.reg_loss],
feed_dict=feed_dict)
elif model.imp_method == 'PNN':
feed_dict[model.task_id] = task
_, loss = sess.run(
[model.train[task], model.unweighted_entropy[task]],
feed_dict=feed_dict)
elif model.imp_method == 'FTR_EXT':
if task == 0:
_, loss = sess.run([model.train, model.reg_loss],
feed_dict=feed_dict)
else:
_, loss = sess.run(
[model.train_classifier, model.reg_loss],
feed_dict=feed_dict)
elif model.imp_method == 'EWC':
# If first iteration of the first task then set the initial value of the running fisher
if task == 0 and iters == 0:
sess.run([model.set_initial_running_fisher],
feed_dict=feed_dict)
# Update fisher after every few iterations
if (iters + 1) % model.fisher_update_after == 0:
sess.run(model.set_running_fisher)
sess.run(model.reset_tmp_fisher)
_, _, loss = sess.run(
[model.set_tmp_fisher, model.train, model.reg_loss],
feed_dict=feed_dict)
elif model.imp_method == 'PI':
_, _, _, loss = sess.run([
model.weights_old_ops_grouped, model.train,
model.update_small_omega, model.reg_loss
],
feed_dict=feed_dict)
elif model.imp_method == 'MAS':
_, loss = sess.run([model.train, model.reg_loss],
feed_dict=feed_dict)
elif model.imp_method == 'PROJ-ANCHOR':
if task == 0:
feed_dict[model.subspace_proj] = proj_matrices[task]
_, loss = sess.run([model.train, model.reg_loss],
feed_dict=feed_dict)
reg = 0.0
else:
# Store the gradients in the orthogonal compliment
feed_dict[model.subspace_proj] = np.eye(
proj_matrices[task].shape[0]) - proj_matrices[task]
_, reg = sess.run(
[model.store_ref_grads, model.anchor_loss],
feed_dict=feed_dict)
feed_dict[model.subspace_proj] = proj_matrices[task]
_, loss = sess.run(
[model.train_subspace_proj, model.reg_loss],
feed_dict=feed_dict)
elif model.imp_method == 'PROJ-SUBSPACE-GP':
if task == 0:
feed_dict[model.subspace_proj] = proj_matrices[task]
_, loss = sess.run([model.train, model.reg_loss],
feed_dict=feed_dict)
reg = 0.0
else:
# Store the gradients in the orthogonal compliment
feed_dict[model.subspace_proj] = np.eye(
proj_matrices[task].shape[0]) - proj_matrices[task]
sess.run(model.store_ref_grads, feed_dict=feed_dict)
feed_dict[model.subspace_proj] = proj_matrices[task]
_, loss = sess.run(
[model.train_gp, model.gp_total_loss],
feed_dict=feed_dict)
elif model.imp_method == 'SUBSPACE-PROJ':
feed_dict[model.subspace_proj] = proj_matrices[task]
_, loss = sess.run([model.train, model.reg_loss],
feed_dict=feed_dict)
elif model.imp_method == 'A-GEM':
if task == 0:
# Normal application of gradients
_, loss = sess.run(
[model.train_first_task, model.agem_loss],
feed_dict=feed_dict)
else:
## Compute and store the reference gradients on the previous tasks
if episodic_filled_counter <= args.eps_mem_batch:
mem_sample_mask = np.arange(
episodic_filled_counter)
else:
# Sample a random subset from episodic memory buffer
mem_sample_mask = np.random.choice(
episodic_filled_counter,
args.eps_mem_batch,
replace=False
) # Sample without replacement so that we don't sample an example more than once
# Store the reference gradient
sess.run(model.store_ref_grads,
feed_dict={
model.x: episodic_images[mem_sample_mask],
model.y_:
episodic_labels[mem_sample_mask],
model.keep_prob: 1.0,
model.output_mask: logit_mask,
model.train_phase: True,
model.learning_rate: args.learning_rate
})
if COUNT_VIOLATIONS:
vc, _, loss = sess.run([
model.violation_count,
model.train_subseq_tasks, model.agem_loss
],
feed_dict=feed_dict)
else:
# Compute the gradient for current task and project if need be
_, loss = sess.run(
[model.train_subseq_tasks, model.agem_loss],
feed_dict=feed_dict)
# Put the batch in the ring buffer
update_fifo_buffer(train_x[offset:offset + residual],
train_y[offset:offset + residual],
episodic_images, episodic_labels,
np.arange(TOTAL_CLASSES), args.mem_size,
count_cls, episodic_filled_counter)
elif model.imp_method == 'RWALK':
# If first iteration of the first task then set the initial value of the running fisher
if task == 0 and iters == 0:
sess.run([model.set_initial_running_fisher],
feed_dict=feed_dict)
# Store the current value of the weights
sess.run(model.weights_delta_old_grouped)
# Update fisher and importance score after every few iterations
if (iters + 1) % model.fisher_update_after == 0:
# Update the importance score using distance in riemannian manifold
sess.run(model.update_big_omega_riemann)
# Now that the score is updated, compute the new value for running Fisher
sess.run(model.set_running_fisher)
# Store the current value of the weights
sess.run(model.weights_delta_old_grouped)
# Reset the delta_L
sess.run([model.reset_small_omega])
_, _, _, _, loss = sess.run([
model.set_tmp_fisher, model.weights_old_ops_grouped,
model.train, model.update_small_omega, model.reg_loss
],
feed_dict=feed_dict)
elif model.imp_method == 'MER':
mem_filled_so_far = examples_seen_so_far if (
examples_seen_so_far < episodic_mem_size
) else episodic_mem_size
if mem_filled_so_far < args.eps_mem_batch:
er_mem_indices = np.arange(mem_filled_so_far)
else:
er_mem_indices = np.random.choice(mem_filled_so_far,
args.eps_mem_batch,
replace=False)
np.random.shuffle(er_mem_indices)
mer_episodic_x_batch, mer_episodic_y_batch = episodic_images[
er_mem_indices], episodic_labels[er_mem_indices]
sess.run(model.store_theta_i_not_w)
for mer_x, mer_y in zip(mer_episodic_x_batch,
mer_episodic_y_batch):
feed_dict = {
model.x: np.expand_dims(mer_x, axis=0),
model.y_: np.expand_dims(mer_y, axis=0),
model.training_iters: num_iters,
model.train_step: iters,
model.keep_prob: 1.0,
model.output_mask: logit_mask,
model.train_phase: True,
model.learning_rate: args.learning_rate
}
sess.run(model.train, feed_dict=feed_dict)
feed_dict = {
model.x: train_x[offset:offset + residual],
model.y_: train_y[offset:offset + residual],
model.training_iters: num_iters,
model.train_step: iters,
model.keep_prob: 1.0,
model.output_mask: logit_mask,
model.train_phase: True,
model.learning_rate: MER_S * args.learning_rate
}
_, loss = sess.run([model.train, model.reg_loss],
feed_dict=feed_dict)
sess.run(model.with_in_batch_reptile_update,
feed_dict={model.mer_beta: MER_GAMMA})
# Store the examples in episodic memory using reservior sampling
for er_x, er_y_ in zip(train_x[offset:offset + residual],
train_y[offset:offset + residual]):
update_reservior(er_x, er_y_, episodic_images,
episodic_labels, episodic_mem_size,
examples_seen_so_far)
examples_seen_so_far += 1
elif model.imp_method == 'ER-Reservoir':
mem_filled_so_far = examples_seen_so_far if (
examples_seen_so_far < episodic_mem_size
) else episodic_mem_size
if mem_filled_so_far < args.eps_mem_batch:
er_mem_indices = np.arange(mem_filled_so_far)
else:
er_mem_indices = np.random.choice(mem_filled_so_far,
args.eps_mem_batch,
replace=False)
np.random.shuffle(er_mem_indices)
er_train_x_batch = np.concatenate(
(episodic_images[er_mem_indices],
train_x[offset:offset + residual]),
axis=0)
er_train_y_batch = np.concatenate(
(episodic_labels[er_mem_indices],
train_y[offset:offset + residual]),
axis=0)
feed_dict = {
model.x: er_train_x_batch,
model.y_: er_train_y_batch,
model.training_iters: num_iters,
model.train_step: iters,
model.keep_prob: 1.0,
model.output_mask: logit_mask,
model.train_phase: True,
model.learning_rate: args.learning_rate
}
_, loss = sess.run([model.train, model.reg_loss],
feed_dict=feed_dict)
for er_x, er_y_ in zip(train_x[offset:offset + residual],
train_y[offset:offset + residual]):
update_reservior(er_x, er_y_, episodic_images,
episodic_labels, episodic_mem_size,
examples_seen_so_far)
examples_seen_so_far += 1
elif model.imp_method == 'ER-Ringbuffer':
mem_filled_so_far = episodic_filled_counter if (
episodic_filled_counter <= episodic_mem_size
) else episodic_mem_size
er_mem_indices = np.arange(mem_filled_so_far) if (
mem_filled_so_far <= args.eps_mem_batch
) else np.random.choice(
mem_filled_so_far, args.eps_mem_batch, replace=False)
er_train_x_batch = np.concatenate(
(episodic_images[er_mem_indices],
train_x[offset:offset + residual]),
axis=0)
er_train_y_batch = np.concatenate(
(episodic_labels[er_mem_indices],
train_y[offset:offset + residual]),
axis=0)
feed_dict = {
model.x: er_train_x_batch,
model.y_: er_train_y_batch,
model.training_iters: num_iters,
model.train_step: iters,
model.keep_prob: 1.0,
model.output_mask: logit_mask,
model.learning_rate: args.learning_rate
}
_, loss = sess.run([model.train, model.reg_loss],
feed_dict=feed_dict)
# Put the batch in the FIFO ring buffer
update_fifo_buffer(train_x[offset:offset + residual],
train_y[offset:offset + residual],
episodic_images, episodic_labels,
np.arange(TOTAL_CLASSES), args.mem_size,
count_cls, episodic_filled_counter)
elif model.imp_method == 'ER-SUBSPACE':
# Zero out all the grads
sess.run([model.reset_er_subspace_grads])
# Accumulate grads for all the tasks
if task > 0:
# Randomly pick a task to replay
tt = np.squeeze(
np.random.choice(np.arange(task), 1,
replace=False))
mem_offset = tt * args.mem_size * TOTAL_CLASSES
er_mem_indices = np.arange(
mem_offset,
mem_offset + args.mem_size * TOTAL_CLASSES)
np.random.shuffle(er_mem_indices)
er_train_x_batch = episodic_images[er_mem_indices]
er_train_y_batch = episodic_labels[er_mem_indices]
feed_dict = {
model.x: er_train_x_batch,
model.y_: er_train_y_batch,
model.training_iters: num_iters,
model.train_step: iters,
model.keep_prob: 1.0,
model.output_mask: logit_mask,
model.task_id: task + 1,
model.learning_rate: args.learning_rate
}
feed_dict[model.subspace_proj] = proj_matrices[tt]
sess.run(model.accum_er_subspace_grads,
feed_dict=feed_dict)
# Train on the current task
feed_dict = {
model.x: train_x[offset:offset + residual],
model.y_: train_y[offset:offset + residual],
model.training_iters: num_iters,
model.train_step: iters,
model.keep_prob: 1.0,
model.output_mask: logit_mask,
model.task_id: task + 1,
model.learning_rate: args.learning_rate
}
feed_dict[model.subspace_proj] = proj_matrices[task]
if args.maintain_orthogonality:
_, loss = sess.run(
[model.accum_er_subspace_grads, model.reg_loss],
feed_dict=feed_dict)
sess.run(model.train_stiefel,
feed_dict={
model.learning_rate: args.learning_rate
})
else:
_, _, loss = sess.run([
model.train_er_subspace,
model.accum_er_subspace_grads, model.reg_loss
],
feed_dict=feed_dict)
# Put the batch in the FIFO ring buffer
update_fifo_buffer(train_x[offset:offset + residual],
train_y[offset:offset + residual],
episodic_images, episodic_labels,
np.arange(TOTAL_CLASSES), args.mem_size,
count_cls, episodic_filled_counter)
if (iters % 100 == 0):
print('Step {:d} {:.3f}'.format(iters, loss))
#print('Step {:d}\t CE: {:.3f}\t Reg: {:.3f}\t TL: {:.3f}'.format(iters, entropy, reg, loss))
#print('Step {:d}\t Reg: {:.3f}\t TL: {:.3f}'.format(iters, reg, loss))
if (math.isnan(loss)):
print('ERROR: NaNs NaNs Nans!!!')
sys.exit(0)
print('\t\t\t\tTraining for Task%d done!' % (task))
if model.imp_method == 'SUBSPACE-PROJ' and GRAD_CHECK:
# TODO: Compute the average gradient for the task at \theta^*
bbatch_size = 100
grad_sum = []
for iiters in range(train_x.shape[0] // bbatch_size):
offset = iiters * bbatch_size
feed_dict = {
model.x: train_x[offset:offset + bbatch_size],
model.y_: train_y[offset:offset + bbatch_size],
model.keep_prob: 1.0,
model.train_phase: True,
model.subspace_proj: proj_matrices[task],
model.output_mask: logit_mask,
model.learning_rate: args.learning_rate
}
grad_vars, train_vars = sess.run(
[model.reg_gradients_vars, model.trainable_vars],
feed_dict=feed_dict)
for v in range(len(train_vars)):
if iiters == 0:
grad_sum.append(grad_vars[v][0])
else:
grad_sum[v] += (grad_vars[v][0] -
grad_sum[v]) / iiters
prev_task_grads.append(grad_sum)
# Upaate the episodic memory filled counter
if use_episodic_memory:
episodic_filled_counter += args.mem_size * TOTAL_CLASSES
if model.imp_method == 'A-GEM' and COUNT_VIOLATIONS:
violation_count[task] = vc
print('Task {}: Violation Count: {}'.format(
task, violation_count))
sess.run(model.reset_violation_count, feed_dict=feed_dict)
# Compute the inter-task updates, Fisher/ importance scores etc
# Don't calculate the task updates for the last task
if (task < (len(datasets) - 1)) or MEASURE_PERF_ON_EPS_MEMORY:
model.task_updates(sess, task, task_train_images,
np.arange(TOTAL_CLASSES))
print('\t\t\t\tTask updates after Task%d done!' % (task))
if args.train_single_epoch and not args.cross_validate_mode:
fbatch = test_task_sequence(model, sess, datasets, False,
proj_matrices)
ftask.append(fbatch)
ftask = np.array(ftask)
else:
if MEASURE_PERF_ON_EPS_MEMORY:
eps_mem = {
'images': episodic_images,
'labels': episodic_labels,
}
# Measure perf on episodic memory
ftask = test_task_sequence(model, sess, eps_mem,
args.online_cross_val,
proj_matrices)
else:
# List to store accuracy for all the tasks for the current trained model
ftask = test_task_sequence(model, sess, datasets,
args.online_cross_val,
proj_matrices)
print('Task: {}, Acc: {}'.format(task, ftask))
# Store the accuracies computed at task T in a list
evals.append(ftask)
# Reset the optimizer
model.reset_optimizer(sess)
#-> End for loop task
runs.append(np.array(evals))
# End for loop runid
runs = np.array(runs)
return runs
def train_task_sequence(model, sess, args):
"""
Train and evaluate LLL system such that we only see a example once
Args:
Returns:
dict A dictionary containing mean and stds for the experiment
"""
# List to store accuracy for each run
runs = []
batch_size = args.batch_size
if model.imp_method == 'A-GEM' or model.imp_method == 'ER':
use_episodic_memory = True
else:
use_episodic_memory = False
# Loop over number of runs to average over
for runid in range(args.num_runs):
print('\t\tRun %d:'%(runid))
# Initialize the random seeds
np.random.seed(args.random_seed+runid)
# Load the permute mnist dataset
datasets = construct_permute_mnist(model.num_tasks)
episodic_mem_size = args.mem_size*model.num_tasks*TOTAL_CLASSES
# Initialize all the variables in the model
sess.run(tf.global_variables_initializer())
# Run the init ops
model.init_updates(sess)
# List to store accuracies for a run
evals = []
# List to store the classes that we have so far - used at test time
test_labels = np.arange(TOTAL_CLASSES)
if use_episodic_memory:
# Reserve a space for episodic memory
episodic_images = np.zeros([episodic_mem_size, INPUT_FEATURE_SIZE])
episodic_labels = np.zeros([episodic_mem_size, TOTAL_CLASSES])
count_cls = np.zeros(TOTAL_CLASSES, dtype=np.int32)
episodic_filled_counter = 0
examples_seen_so_far = 0
# Mask for softmax
# Since all the classes are present in all the tasks so nothing to mask
logit_mask = np.ones(TOTAL_CLASSES)
if model.imp_method == 'PNN':
pnn_train_phase = np.array(np.zeros(model.num_tasks), dtype=np.bool)
pnn_logit_mask = np.ones([model.num_tasks, TOTAL_CLASSES])
if COUNT_VIOLATIONS:
violation_count = np.zeros(model.num_tasks)
vc = 0
# Training loop for all the tasks
for task in range(len(datasets)):
print('\t\tTask %d:'%(task))
# If not the first task then restore weights from previous task
if(task > 0 and model.imp_method != 'PNN'):
model.restore(sess)
# Extract training images and labels for the current task
task_train_images = datasets[task]['train']['images']
task_train_labels = datasets[task]['train']['labels']
# If multi_task is set the train using datasets of all the tasks
if MULTI_TASK:
if task == 0:
for t_ in range(1, len(datasets)):
task_train_images = np.concatenate((task_train_images, datasets[t_]['train']['images']), axis=0)
task_train_labels = np.concatenate((task_train_labels, datasets[t_]['train']['labels']), axis=0)
else:
# Skip training for this task
continue
# Assign equal weights to all the examples
task_sample_weights = np.ones([task_train_labels.shape[0]], dtype=np.float32)
total_train_examples = task_train_images.shape[0]
# Randomly suffle the training examples
perm = np.arange(total_train_examples)
np.random.shuffle(list(perm))
train_x = task_train_images[perm][:args.examples_per_task]
train_y = task_train_labels[perm][:args.examples_per_task]
task_sample_weights = task_sample_weights[perm][:args.examples_per_task]
print('Received {} images, {} labels at task {}'.format(train_x.shape[0], train_y.shape[0], task))
# Array to store accuracies when training for task T
ftask = []
num_train_examples = train_x.shape[0]
# Train a task observing sequence of data
if args.train_single_epoch:
num_iters = num_train_examples // batch_size
else:
num_iters = args.train_iters
# Training loop for task T
for iters in range(num_iters):
if args.train_single_epoch and not args.cross_validate_mode:
if (iters < 10) or (iters < 100 and iters % 10 == 0) or (iters % 100 == 0):
# Snapshot the current performance across all tasks after each mini-batch
fbatch = test_task_sequence(model, sess, datasets, args.online_cross_val)
ftask.append(fbatch)
offset = (iters * batch_size) % (num_train_examples - batch_size)
residual = batch_size
if model.imp_method == 'PNN':
pnn_train_phase[:] = False
pnn_train_phase[task] = True
feed_dict = {model.x: train_x[offset:offset+batch_size], model.y_[task]: train_y[offset:offset+batch_size],
model.sample_weights: task_sample_weights[offset:offset+batch_size],
model.training_iters: num_iters, model.train_step: iters, model.keep_prob: 1.0}
train_phase_dict = {m_t: i_t for (m_t, i_t) in zip(model.train_phase, pnn_train_phase)}
logit_mask_dict = {m_t: i_t for (m_t, i_t) in zip(model.output_mask, pnn_logit_mask)}
feed_dict.update(train_phase_dict)
feed_dict.update(logit_mask_dict)
else:
feed_dict = {model.x: train_x[offset:offset+batch_size], model.y_: train_y[offset:offset+batch_size],
model.sample_weights: task_sample_weights[offset:offset+batch_size],
model.training_iters: num_iters, model.train_step: iters, model.keep_prob: 1.0,
model.output_mask: logit_mask, model.train_phase: True}
if model.imp_method == 'VAN':
_, loss = sess.run([model.train, model.reg_loss], feed_dict=feed_dict)
elif model.imp_method == 'PNN':
feed_dict[model.task_id] = task
_, loss = sess.run([model.train[task], model.unweighted_entropy[task]], feed_dict=feed_dict)
elif model.imp_method == 'FTR_EXT':
if task == 0:
_, loss = sess.run([model.train, model.reg_loss], feed_dict=feed_dict)
else:
_, loss = sess.run([model.train_classifier, model.reg_loss], feed_dict=feed_dict)
elif model.imp_method == 'EWC':
# If first iteration of the first task then set the initial value of the running fisher
if task == 0 and iters == 0:
sess.run([model.set_initial_running_fisher], feed_dict=feed_dict)
# Update fisher after every few iterations
if (iters + 1) % model.fisher_update_after == 0:
sess.run(model.set_running_fisher)
sess.run(model.reset_tmp_fisher)
_, _, loss = sess.run([model.set_tmp_fisher, model.train, model.reg_loss], feed_dict=feed_dict)
elif model.imp_method == 'PI':
_, _, _, loss = sess.run([model.weights_old_ops_grouped, model.train, model.update_small_omega,
model.reg_loss], feed_dict=feed_dict)
elif model.imp_method == 'MAS':
_, loss = sess.run([model.train, model.reg_loss], feed_dict=feed_dict)
elif model.imp_method == 'A-GEM':
if task == 0:
# Normal application of gradients
_, loss = sess.run([model.train_first_task, model.agem_loss], feed_dict=feed_dict)
else:
## Compute and store the reference gradients on the previous tasks
if episodic_filled_counter <= args.eps_mem_batch:
mem_sample_mask = np.arange(episodic_filled_counter)
else:
# Sample a random subset from episodic memory buffer
mem_sample_mask = np.random.choice(episodic_filled_counter, args.eps_mem_batch, replace=False) # Sample without replacement so that we don't sample an example more than once
# Store the reference gradient
sess.run(model.store_ref_grads, feed_dict={model.x: episodic_images[mem_sample_mask], model.y_: episodic_labels[mem_sample_mask],
model.keep_prob: 1.0, model.output_mask: logit_mask, model.train_phase: True})
if COUNT_VIOLATIONS:
vc, _, loss = sess.run([model.violation_count, model.train_subseq_tasks, model.agem_loss], feed_dict=feed_dict)
else:
# Compute the gradient for current task and project if need be
_, loss = sess.run([model.train_subseq_tasks, model.agem_loss], feed_dict=feed_dict)
# Put the batch in the ring buffer
for er_x, er_y_ in zip(train_x[offset:offset+residual], train_y[offset:offset+residual]):
cls = np.unique(np.nonzero(er_y_))[-1]
# Write the example at the location pointed by count_cls[cls]
cls_to_index_map = cls
with_in_task_offset = args.mem_size * cls_to_index_map
mem_index = count_cls[cls] + with_in_task_offset + episodic_filled_counter
episodic_images[mem_index] = er_x
episodic_labels[mem_index] = er_y_
count_cls[cls] = (count_cls[cls] + 1) % args.mem_size
elif model.imp_method == 'RWALK':
# If first iteration of the first task then set the initial value of the running fisher
if task == 0 and iters == 0:
sess.run([model.set_initial_running_fisher], feed_dict=feed_dict)
# Store the current value of the weights
sess.run(model.weights_delta_old_grouped)
# Update fisher and importance score after every few iterations
if (iters + 1) % model.fisher_update_after == 0:
# Update the importance score using distance in riemannian manifold
sess.run(model.update_big_omega_riemann)
# Now that the score is updated, compute the new value for running Fisher
sess.run(model.set_running_fisher)
# Store the current value of the weights
sess.run(model.weights_delta_old_grouped)
# Reset the delta_L
sess.run([model.reset_small_omega])
_, _, _, _, loss = sess.run([model.set_tmp_fisher, model.weights_old_ops_grouped,
model.train, model.update_small_omega, model.reg_loss], feed_dict=feed_dict)
elif model.imp_method == 'ER':
mem_filled_so_far = examples_seen_so_far if (examples_seen_so_far < episodic_mem_size) else episodic_mem_size
if mem_filled_so_far < args.eps_mem_batch:
er_mem_indices = np.arange(mem_filled_so_far)
else:
er_mem_indices = np.random.choice(mem_filled_so_far, args.eps_mem_batch, replace=False)
np.random.shuffle(er_mem_indices)
# Train on a batch of episodic memory first
er_train_x_batch = np.concatenate((episodic_images[er_mem_indices], train_x[offset:offset+residual]), axis=0)
er_train_y_batch = np.concatenate((episodic_labels[er_mem_indices], train_y[offset:offset+residual]), axis=0)
feed_dict = {model.x: er_train_x_batch, model.y_: er_train_y_batch,
model.training_iters: num_iters, model.train_step: iters, model.keep_prob: 1.0,
model.output_mask: logit_mask, model.train_phase: True}
_, loss = sess.run([model.train, model.reg_loss], feed_dict=feed_dict)
for er_x, er_y_ in zip(train_x[offset:offset+residual], train_y[offset:offset+residual]):
update_reservior(er_x, er_y_, episodic_images, episodic_labels, episodic_mem_size, examples_seen_so_far)
examples_seen_so_far += 1
if (iters % 100 == 0):
print('Step {:d} {:.3f}'.format(iters, loss))
if (math.isnan(loss)):
print('ERROR: NaNs NaNs Nans!!!')
sys.exit(0)
print('\t\t\t\tTraining for Task%d done!'%(task))
# Upaate the episodic memory filled counter
if use_episodic_memory:
episodic_filled_counter += args.mem_size * TOTAL_CLASSES
if model.imp_method == 'A-GEM' and COUNT_VIOLATIONS:
violation_count[task] = vc
print('Task {}: Violation Count: {}'.format(task, violation_count))
sess.run(model.reset_violation_count, feed_dict=feed_dict)
# Compute the inter-task updates, Fisher/ importance scores etc
# Don't calculate the task updates for the last task
if (task < (len(datasets) - 1)) or MEASURE_PERF_ON_EPS_MEMORY:
model.task_updates(sess, task, task_train_images, np.arange(TOTAL_CLASSES))
print('\t\t\t\tTask updates after Task%d done!'%(task))
if args.train_single_epoch and not args.cross_validate_mode:
fbatch = test_task_sequence(model, sess, datasets, False)
ftask.append(fbatch)
ftask = np.array(ftask)
else:
if MEASURE_PERF_ON_EPS_MEMORY:
eps_mem = {
'images': episodic_images,
'labels': episodic_labels,
}
# Measure perf on episodic memory
ftask = test_task_sequence(model, sess, eps_mem, args.online_cross_val)
else:
# List to store accuracy for all the tasks for the current trained model
ftask = test_task_sequence(model, sess, datasets, args.online_cross_val)
# Store the accuracies computed at task T in a list
evals.append(ftask)
# Reset the optimizer
model.reset_optimizer(sess)
#-> End for loop task
runs.append(np.array(evals))
# End for loop runid
runs = np.array(runs)
return runs