def train_rule_only(
model_dir,
rule_trains,
max_steps,
hp=None,
ruleset='all',
seed=0,
):
'''Customized training function.
The network sequentially but only train rule for the second set.
First train the network to perform tasks in group 1, then train on group 2.
When training group 2, only rule connections are being trained.
Args:
model_dir: str, training directory
rule_trains: a list of list of tasks to train sequentially
hp: dictionary of hyperparameters
max_steps: int, maximum number of training steps for each list of tasks
display_step: int, display steps
ruleset: the set of rules to train
seed: int, random seed to be used
Returns:
model is stored at model_dir/model.ckpt
training configuration is stored at model_dir/hp.json
'''
tools.mkdir_p(model_dir)
# Network parameters
default_hp = get_default_hp(ruleset)
if hp is not None:
default_hp.update(hp)
hp = default_hp
hp['seed'] = seed
hp['rng'] = np.random.RandomState(seed)
hp['rule_trains'] = rule_trains
# Get all rules by flattening the list of lists
hp['rules'] = [r for rs in rule_trains for r in rs]
# Number of training iterations for each rule
if hasattr(max_steps, '__iter__'):
rule_train_iters = max_steps
else:
rule_train_iters = [len(r) * max_steps for r in rule_trains]
tools.save_hp(hp, model_dir)
# Display hp
for key, val in hp.items():
print('{:20s} = '.format(key) + str(val))
# Build the model
model = Model(model_dir, hp=hp)
# Store results
log = defaultdict(list)
log['model_dir'] = model_dir
# Record time
t_start = time.time()
# Use customized session that launches the graph as well
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# penalty on deviation from initial weight
if hp['l2_weight_init'] > 0:
raise NotImplementedError()
# Looping
step_total = 0
for i_rule_train, rule_train in enumerate(hp['rule_trains']):
step = 0
if i_rule_train == 0:
display_step = 200
else:
display_step = 50
if i_rule_train > 0:
# var_list = [v for v in model.var_list
# if ('input' in v.name) and ('rnn' not in v.name)]
var_list = [
v for v in model.var_list if 'rule_input' in v.name
]
model.set_optimizer(var_list=var_list)
# Keep training until reach max iterations
while (step * hp['batch_size_train'] <=
rule_train_iters[i_rule_train]):
# Validation
if step % display_step == 0:
trial = step_total * hp['batch_size_train']
log['trials'].append(trial)
log['times'].append(time.time() - t_start)
log['rule_now'].append(rule_train)
log = do_eval(sess, model, log, rule_train)
if log['perf_avg'][-1] > model.hp['target_perf']:
print('Perf reached the target: {:0.2f}'.format(
hp['target_perf']))
break
# Training
rule_train_now = hp['rng'].choice(rule_train)
# Generate a random batch of trials.
# Each batch has the same trial length
trial = generate_trials(rule_train_now,
hp,
'random',
batch_size=hp['batch_size_train'])
# Generating feed_dict.
feed_dict = tools.gen_feed_dict(model, trial, hp)
# This will compute the gradient BEFORE train step
_ = sess.run(model.train_step, feed_dict=feed_dict)
step += 1
step_total += 1
print("Optimization Finished!")
def train_sequential(
model_dir,
rule_trains,
hp=None,
max_steps=1e7,
display_step=500,
ruleset='mante',
seed=0,
):
'''Train the network sequentially.
Args:
model_dir: str, training directory
rule_trains: a list of list of tasks to train sequentially
hp: dictionary of hyperparameters
max_steps: int, maximum number of training steps for each list of tasks
display_step: int, display steps
ruleset: the set of rules to train
seed: int, random seed to be used
Returns:
model is stored at model_dir/model.ckpt
training configuration is stored at model_dir/hp.json
'''
tools.mkdir_p(model_dir)
# Network parameters
default_hp = get_default_hp(ruleset)
if hp is not None:
default_hp.update(hp)
hp = default_hp
hp['seed'] = seed
hp['rng'] = np.random.RandomState(seed)
hp['rule_trains'] = rule_trains
# Get all rules by flattening the list of lists
hp['rules'] = [r for rs in rule_trains for r in rs]
# Number of training iterations for each rule
rule_train_iters = [len(r) * max_steps for r in rule_trains]
tools.save_hp(hp, model_dir)
# Display hp
for key, val in hp.items():
print('{:20s} = '.format(key) + str(val))
# Using continual learning or not
c, ksi = hp['c_intsyn'], hp['ksi_intsyn']
# Build the model
model = Model(model_dir, hp=hp)
grad_unreg = tf.gradients(model.cost_lsq, model.var_list)
# Store results
log = defaultdict(list)
log['model_dir'] = model_dir
# Record time
t_start = time.time()
# tensorboard summaries
placeholders = list()
for v_name in ['Omega0', 'omega0', 'vdelta']:
for v in model.var_list:
placeholder = tf.placeholder(tf.float32, shape=v.shape)
tf.summary.histogram(v_name + '/' + v.name, placeholder)
placeholders.append(placeholder)
merged = tf.summary.merge_all()
test_writer = tf.summary.FileWriter(model_dir + '/tb')
def relu(x):
return x * (x > 0.)
# Use customized session that launches the graph as well
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# penalty on deviation from initial weight
if hp['l2_weight_init'] > 0:
raise NotImplementedError()
# Looping
step_total = 0
for i_rule_train, rule_train in enumerate(hp['rule_trains']):
step = 0
# At the beginning of new tasks
# Only if using intelligent synapses
v_current = sess.run(model.var_list)
if i_rule_train == 0:
v_anc0 = v_current
Omega0 = [np.zeros(v.shape, dtype='float32') for v in v_anc0]
omega0 = [np.zeros(v.shape, dtype='float32') for v in v_anc0]
v_delta = [np.zeros(v.shape, dtype='float32') for v in v_anc0]
elif c > 0:
v_anc0_prev = v_anc0
v_anc0 = v_current
v_delta = [
v - v_prev for v, v_prev in zip(v_anc0, v_anc0_prev)
]
# Make sure all elements in omega0 are non-negative
# Penalty
Omega0 = [
relu(O + o / (v_d**2 + ksi))
for O, o, v_d in zip(Omega0, omega0, v_delta)
]
# Update cost
model.cost_reg = tf.constant(0.)
for v, w, v_val in zip(model.var_list, Omega0, v_current):
model.cost_reg += c * tf.reduce_sum(
tf.multiply(tf.constant(w),
tf.square(v - tf.constant(v_val))))
model.set_optimizer()
# Store Omega0 to tf summary
feed_dict = dict(zip(placeholders, Omega0 + omega0 + v_delta))
summary = sess.run(merged, feed_dict=feed_dict)
test_writer.add_summary(summary, i_rule_train)
# Reset
omega0 = [np.zeros(v.shape, dtype='float32') for v in v_anc0]
# Keep training until reach max iterations
while (step * hp['batch_size_train'] <=
rule_train_iters[i_rule_train]):
# Validation
if step % display_step == 0:
trial = step_total * hp['batch_size_train']
log['trials'].append(trial)
log['times'].append(time.time() - t_start)
log['rule_now'].append(rule_train)
log = do_eval(sess, model, log, rule_train)
if log['perf_avg'][-1] > model.hp['target_perf']:
print('Perf reached the target: {:0.2f}'.format(
hp['target_perf']))
break
# Training
rule_train_now = hp['rng'].choice(rule_train)
# Generate a random batch of trials.
# Each batch has the same trial length
trial = generate_trials(rule_train_now,
hp,
'random',
batch_size=hp['batch_size_train'])
# Generating feed_dict.
feed_dict = tools.gen_feed_dict(model, trial, hp)
# Continual learning with intelligent synapses
v_prev = v_current
# This will compute the gradient BEFORE train step
_, v_grad = sess.run([model.train_step, grad_unreg],
feed_dict=feed_dict)
# Get the weight after train step
v_current = sess.run(model.var_list)
# Update synaptic importance
omega0 = [
o - (v_c - v_p) * v_g for o, v_c, v_p, v_g in zip(
omega0, v_current, v_prev, v_grad)
]
step += 1
step_total += 1
print("Optimization Finished!")
def train_sequential_orthogonalized(model_dir,
rule_trains,
hp=None,
max_steps=1e7,
display_step=500,
rich_output=False,
ruleset='mante',
applyProj='both',
seed=0,
nEpisodeBatches=100,
projGrad=True,
alpha=0.001,
fixReadout=False):
'''Train the network sequentially.
Args:
model_dir: str, training directory
rule_trains: a list of list of tasks to train sequentially
hp: dictionary of hyperparameters
max_steps: int, maximum number of training steps for each list of tasks
display_step: int, display steps
ruleset: the set of rules to train
seed: int, random seed to be used
Returns:
model is stored at model_dir/model.ckpt
training configuration is stored at model_dir/hp.json
'''
tools.mkdir_p(model_dir)
# Network parameters
default_hp = get_default_hp(ruleset)
if hp is not None:
default_hp.update(hp)
hp = default_hp
hp['seed'] = seed
hp['rng'] = np.random.RandomState(seed)
hp['rule_trains'] = rule_trains
# Get all rules by flattening the list of lists
# hp['rules'] = [r for rs in rule_trains for r in rs]
hp['rules'] = rule_trains
# save some other parameters
hp['alpha_projection'] = alpha
hp['max_steps'] = max_steps
# Number of training iterations for each rule
rule_train_iters = [max_steps for _ in rule_trains]
tools.save_hp(hp, model_dir)
# Display hp
for key, val in hp.items():
print('{:20s} = '.format(key) + str(val))
# Build the model
model = Sequential_Model(model_dir,
projGrad=projGrad,
applyProj=applyProj,
hp=hp)
# Store results
log = defaultdict(list)
log['model_dir'] = model_dir
# Record time
t_start = time.time()
def relu(x):
return x * (x > 0.)
# -------------------------------------------------------
# Use customized session that launches the graph as well
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# penalty on deviation from initial weight
if hp['l2_weight_init'] > 0:
raise NotImplementedError()
# Looping
step_total = 0
taskNumber = 0
if fixReadout is True:
my_var_list = [
var for var in model.var_list
if 'rnn/leaky_rnn_cell/kernel:0' in var.name
]
else:
my_var_list = [
var for var in model.var_list
if 'rnn/leaky_rnn_cell/kernel:0' in var.name
or 'output/weights:0' in var.name
]
# initialise projection matrices
input_proj = tf.zeros(
(hp['n_rnn'] + hp['n_input'], hp['n_rnn'] + hp['n_input']))
activity_proj = tf.zeros((hp['n_rnn'], hp['n_rnn']))
output_proj = tf.zeros((hp['n_output'], hp['n_output']))
recurrent_proj = tf.zeros((hp['n_rnn'], hp['n_rnn']))
for i_rule_train, rule_train in enumerate(hp['rule_trains']):
step = 0
model.set_optimizer(activity_proj=activity_proj,
input_proj=input_proj,
output_proj=output_proj,
recurrent_proj=recurrent_proj,
taskNumber=taskNumber,
var_list=my_var_list,
alpha=alpha)
# Keep training until reach max iterations
while (step * hp['batch_size_train'] <=
rule_train_iters[i_rule_train]):
# Validation
if step % display_step == 0:
trial = step_total * hp['batch_size_train']
log['trials'].append(trial)
log['times'].append(time.time() - t_start)
log['rule_now'].append(rule_train)
log = do_eval(sess, model, log, rule_train)
if log['perf_avg'][-1] > model.hp['target_perf']:
print('Perf reached the target: {:0.2f}'.format(
hp['target_perf']))
break
# Training
# rule_train_now = hp['rng'].choice(rule_train)
# Generate a random batch of trials.
# Each batch has the same trial length
trial = generate_trials(rule_train,
hp,
'random',
batch_size=hp['batch_size_train'],
delay_fac=hp['delay_fac'])
# Generating feed_dict.
feed_dict = tools.gen_feed_dict(model, trial, hp)
# update model
sess.run(model.train_step, feed_dict=feed_dict)
# # Get the weight after train step
# v_current = sess.run(model.var_list)
step += 1
step_total += 1
if step % display_step == 0:
model.save_ckpt(step_total)
# ---------- save model after its completed training the current task ----------
model.save_after_task(taskNumber)
# ---------- generate task activity for continual learning -------
trial = generate_trials(rule_train,
hp,
'random',
batch_size=hp['batch_size_test'],
delay_fac=hp['delay_fac'])
# Generating feed_dict.
feed_dict = tools.gen_feed_dict(model, trial, hp)
eval_h, eval_x, eval_y, Wrec, Win = sess.run(
[model.h, model.x, model.y, model.w_rec, model.w_in],
feed_dict=feed_dict)
full_state = np.concatenate([eval_x, eval_h], -1)
# get weight matrix after current task
Wfull = np.concatenate([Win, Wrec], 0)
# joint covariance matrix of input and activity
Shx_task = compute_covariance(
np.reshape(full_state, (-1, hp['n_rnn'] + hp['n_input'])).T)
# covariance matrix of output
Sy_task = compute_covariance(
np.reshape(eval_y, (-1, hp['n_output'])).T)
# get block matrices from Shx_task
# Sh_task = Shx_task[-hp['n_rnn']:, -hp['n_rnn']:]
Sh_task = np.matmul(np.matmul(Wfull.T, Shx_task), Wfull)
# ---------- update stored covariance matrices for continual learning -------
if taskNumber == 0:
input_cov = Shx_task
activity_cov = Sh_task
output_cov = Sy_task
else:
input_cov = taskNumber / (
taskNumber + 1) * input_cov + Shx_task / (taskNumber + 1)
activity_cov = taskNumber / (
taskNumber + 1) * activity_cov + Sh_task / (taskNumber + 1)
output_cov = taskNumber / (
taskNumber + 1) * output_cov + Sy_task / (taskNumber + 1)
# ---------- update projection matrices for continual learning ----------
activity_proj, input_proj, output_proj, recurrent_proj = compute_projection_matrices(
activity_cov, input_cov, output_cov,
input_cov[-hp['n_rnn']:, -hp['n_rnn']:], alpha)
# update task number
taskNumber += 1
print("Optimization Finished!")
def train(
model_dir,
hp=None,
max_steps=1e7,
display_step=500,
ruleset='mante',
rule_trains=None,
rule_prob_map=None,
seed=0,
rich_output=False,
load_dir=None,
trainables=None,
):
"""Train the network.
Args:
model_dir: str, training directory
hp: dictionary of hyperparameters
max_steps: int, maximum number of training steps
display_step: int, display steps
ruleset: the set of rules to train
rule_trains: list of rules to train, if None then all rules possible
rule_prob_map: None or dictionary of relative rule probability
seed: int, random seed to be used
Returns:
model is stored at model_dir/model.ckpt
training configuration is stored at model_dir/hp.json
"""
tools.mkdir_p(model_dir)
# Network parameters
default_hp = get_default_hp(ruleset)
if hp is not None:
default_hp.update(hp)
hp = default_hp
hp['seed'] = seed
hp['rng'] = np.random.RandomState(seed)
# Rules to train and test. Rules in a set are trained together
if rule_trains is None:
# By default, training all rules available to this ruleset
hp['rule_trains'] = task.rules_dict[ruleset]
else:
hp['rule_trains'] = rule_trains
hp['rules'] = hp['rule_trains']
# Assign probabilities for rule_trains.
if rule_prob_map is None:
rule_prob_map = dict()
# Turn into rule_trains format
hp['rule_probs'] = None
if hasattr(hp['rule_trains'], '__iter__'):
# Set default as 1.
rule_prob = np.array(
[rule_prob_map.get(r, 1.) for r in hp['rule_trains']])
hp['rule_probs'] = list(rule_prob / np.sum(rule_prob))
tools.save_hp(hp, model_dir)
# Build the model
model = Model(model_dir, hp=hp)
# Display hp
for key, val in hp.items():
print('{:20s} = '.format(key) + str(val))
# Store results
log = defaultdict(list)
log['model_dir'] = model_dir
# Record time
t_start = time.time()
with tf.Session() as sess:
if load_dir is not None:
model.restore(load_dir) # complete restore
else:
# Assume everything is restored
sess.run(tf.global_variables_initializer())
# Set trainable parameters
if trainables is None or trainables == 'all':
var_list = model.var_list # train everything
elif trainables == 'input':
# train all nputs
var_list = [
v for v in model.var_list
if ('input' in v.name) and ('rnn' not in v.name)
]
elif trainables == 'rule':
# train rule inputs only
var_list = [v for v in model.var_list if 'rule_input' in v.name]
else:
raise ValueError('Unknown trainables')
model.set_optimizer(var_list=var_list)
# penalty on deviation from initial weight
if hp['l2_weight_init'] > 0:
anchor_ws = sess.run(model.weight_list)
for w, w_val in zip(model.weight_list, anchor_ws):
model.cost_reg += (hp['l2_weight_init'] *
tf.nn.l2_loss(w - w_val))
model.set_optimizer(var_list=var_list)
# partial weight training
if ('p_weight_train' in hp and (hp['p_weight_train'] is not None)
and hp['p_weight_train'] < 1.0):
for w in model.weight_list:
w_val = sess.run(w)
w_size = sess.run(tf.size(w))
w_mask_tmp = np.linspace(0, 1, w_size)
hp['rng'].shuffle(w_mask_tmp)
ind_fix = w_mask_tmp > hp['p_weight_train']
w_mask = np.zeros(w_size, dtype=np.float32)
w_mask[ind_fix] = 1e-1 # will be squared in l2_loss
w_mask = tf.constant(w_mask)
w_mask = tf.reshape(w_mask, w.shape)
model.cost_reg += tf.nn.l2_loss((w - w_val) * w_mask)
model.set_optimizer(var_list=var_list)
step = 0
while step * hp['batch_size_train'] <= max_steps:
try:
# Validation
if step % display_step == 0:
log['trials'].append(step * hp['batch_size_train'])
log['times'].append(time.time() - t_start)
log = do_eval(sess, model, log, hp['rule_trains'])
#if log['perf_avg'][-1] > model.hp['target_perf']:
#check if minimum performance is above target
if log['perf_min'][-1] > model.hp['target_perf']:
print('Perf reached the target: {:0.2f}'.format(
hp['target_perf']))
break
if rich_output:
display_rich_output(model, sess, step, log, model_dir)
# Training
rule_train_now = hp['rng'].choice(hp['rule_trains'],
p=hp['rule_probs'])
# Generate a random batch of trials.
# Each batch has the same trial length
trial = generate_trials(rule_train_now,
hp,
'random',
batch_size=hp['batch_size_train'])
# Generating feed_dict.
feed_dict = tools.gen_feed_dict(model, trial, hp)
sess.run(model.train_step, feed_dict=feed_dict)
step += 1
except KeyboardInterrupt:
print("Optimization interrupted by user")
break
print("Optimization finished!")
def train(
model_dir,
hp=None,
max_steps=1e7,
display_step=500,
ruleset='mante',
rule_trains=None,
rule_prob_map=None,
seed=0,
rich_output=True,
load_dir=None,
trainables=None,
fixReadoutandBias=False,
fixBias=False,
):
"""Train the network.
Args:
model_dir: str, training directory
hp: dictionary of hyperparameters
max_steps: int, maximum number of training steps
display_step: int, display steps
ruleset: the set of rules to train
rule_trains: list of rules to train, if None then all rules possible
rule_prob_map: None or dictionary of relative rule probability
seed: int, random seed to be used
Returns:
model is stored at model_dir/model.ckpt
training configuration is stored at model_dir/hp.json
"""
tools.mkdir_p(model_dir)
# Network parameters
default_hp = get_default_hp(ruleset)
if hp is not None:
default_hp.update(hp)
hp = default_hp
hp['seed'] = seed
hp['rng'] = np.random.RandomState(seed)
# Rules to train and test. Rules in a set are trained together
if rule_trains is None:
# By default, training all rules available to this ruleset
hp['rule_trains'] = task.rules_dict[ruleset]
else:
hp['rule_trains'] = rule_trains
hp['rules'] = hp['rule_trains']
# Assign probabilities for rule_trains.
if rule_prob_map is None:
rule_prob_map = dict()
# Turn into rule_trains format
hp['rule_probs'] = None
if hasattr(hp['rule_trains'], '__iter__'):
# Set default as 1.
rule_prob = np.array(
[rule_prob_map.get(r, 1.) for r in hp['rule_trains']])
hp['rule_probs'] = list(rule_prob / np.sum(rule_prob))
tools.save_hp(hp, model_dir)
# Build the model
with tf.device('gpu:0'):
model = Model(model_dir, hp=hp)
# Display hp
for key, val in hp.items():
print('{:20s} = '.format(key) + str(val))
if fixReadoutandBias is True:
my_var_list = [
var for var in model.var_list
if 'rnn/leaky_rnn_cell/kernel:0' in var.name
]
print(my_var_list)
elif fixBias is True:
my_var_list = [
var for var in model.var_list
if 'rnn/leaky_rnn_cell/kernel:0' in var.name
or 'output/weights:0' in var.name
]
else:
my_var_list = model.var_list
model.set_optimizer(var_list=my_var_list)
# Store results
log = defaultdict(list)
log['model_dir'] = model_dir
# Record time
t_start = time.time()
# Use customized session that launches the graph as well
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# penalty on deviation from initial weight
if hp['l2_weight_init'] > 0:
anchor_ws = sess.run(model.weight_list)
for w, w_val in zip(model.weight_list, anchor_ws):
model.cost_reg += (hp['l2_weight_init'] *
tf.nn.l2_loss(w - w_val))
model.set_optimizer(var_list=my_var_list)
# partial weight training
if ('p_weight_train' in hp and (hp['p_weight_train'] is not None)
and hp['p_weight_train'] < 1.0):
for w in model.weight_list:
w_val = sess.run(w)
w_size = sess.run(tf.size(w))
w_mask_tmp = np.linspace(0, 1, w_size)
hp['rng'].shuffle(w_mask_tmp)
ind_fix = w_mask_tmp > hp['p_weight_train']
w_mask = np.zeros(w_size, dtype=np.float32)
w_mask[ind_fix] = 1e-1 # will be squared in l2_loss
w_mask = tf.constant(w_mask)
w_mask = tf.reshape(w_mask, w.shape)
model.cost_reg += tf.nn.l2_loss((w - w_val) * w_mask)
model.set_optimizer(var_list=my_var_list)
step = 0
run_ave_time = []
while step * hp['batch_size_train'] <= max_steps:
try:
# Validation
if step % display_step == 0:
grad_norm = tf.global_norm(model.clipped_gs)
grad_norm_np = sess.run(grad_norm)
# import pdb
# pdb.set_trace()
log['grad_norm'].append(grad_norm_np.item())
log['trials'].append(step * hp['batch_size_train'])
log['times'].append(time.time() - t_start)
log = do_eval(sess, model, log, hp['rule_trains'])
# if log['perf_avg'][-1] > model.hp['target_perf']:
# check if minimum performance is above target
if log['perf_min'][-1] > model.hp['target_perf']:
print('Perf reached the target: {:0.2f}'.format(
hp['target_perf']))
break
if rich_output:
display_rich_output(model, sess, step, log,
model_dir)
# Training
dtStart = datetime.now()
sess.run(model.train_step)
dtEnd = datetime.now()
if len(run_ave_time) is 0:
run_ave_time = np.expand_dims(
(dtEnd - dtStart).total_seconds(), axis=0)
else:
run_ave_time = np.concatenate(
(run_ave_time,
np.expand_dims((dtEnd - dtStart).total_seconds(),
axis=0)))
# print(np.mean(run_ave_time))
# print((dtEnd-dtStart).total_seconds())
step += 1
if step < 10:
model.save_ckpt(step)
if step < 1000:
if step % display_step / 10 == 0:
model.save_ckpt(step)
if step % display_step == 0:
model.save_ckpt(step)
except KeyboardInterrupt:
print("Optimization interrupted by user")
break
print("Optimization finished!")