def primal_optimizer(hyperparam_vect, i_hyper):
def indexed_loss_fun(w, L2_vect, i_iter):
rs = RandomState(
(seed, i_hyper,
i_iter)) # Deterministic seed needed for backwards pass.
idxs = rs.randint(N_train, size=batch_size)
return loss_fun(w, train_data['X'][idxs], train_data['T'][idxs],
L2_vect)
learning_curve_dict = defaultdict(list)
def callback(x, v, g, i_iter):
if i_iter % thin == 0:
learning_curve_dict['learning_curve'].append(
loss_fun(x, **train_data))
learning_curve_dict['grad_norm'].append(np.linalg.norm(g))
learning_curve_dict['weight_norm'].append(np.linalg.norm(x))
learning_curve_dict['velocity_norm'].append(np.linalg.norm(v))
cur_hyperparams = hyperparams.new_vect(hyperparam_vect)
rs = RandomState((seed, i_hyper))
W0 = fill_parser(parser, np.exp(cur_hyperparams['log_param_scale']))
W0 *= rs.randn(W0.size)
alphas = np.exp(cur_hyperparams['log_alphas'])
betas = logit(cur_hyperparams['invlogit_betas'])
L2_reg = np.exp(cur_hyperparams['log_L2_reg'])
W_opt = sgd_parsed(grad(indexed_loss_fun),
kylist(W0, alphas, betas, L2_reg),
parser,
callback=callback)
cur_primal_results['weights'] = getval(W_opt).copy()
cur_primal_results['learning_curve'] = getval(learning_curve_dict)
return W_opt, learning_curve_dict
def primal_optimizer(hyperparam_vect, i_hyper):
def indexed_loss_fun(w, meta_vect, i_iter):
(train_data, train_labels, L2_vect) = meta
return loss_fun(w, train_data, train_labels, L2_vect)
#return loss_fun(w, train_data['X'], train_data['T'], L2_vect + np.sum(fake_data.ravel()))
learning_curve_dict = defaultdict(list)
def callback(x, v, g, i_iter):
if i_iter % thin == 0:
# learning_curve_dict['learning_curve'].append(loss_fun(x, getval(cur_hyperparams['fake_data']), fake_labels))
learning_curve_dict['grad_norm'].append(np.linalg.norm(g))
learning_curve_dict['weight_norm'].append(np.linalg.norm(x))
learning_curve_dict['velocity_norm'].append(np.linalg.norm(v))
cur_hyperparams = hyperparams.new_vect(hyperparam_vect)
fake_data = cur_hyperparams['fake_data']
rs = RandomState((seed, i_hyper))
W0 = fill_parser(parser, np.exp(fixed_hyperparams['log_param_scale']))
W0 *= rs.randn(W0.size)
alphas = np.exp(fixed_hyperparams['log_alphas'])
betas = logit(fixed_hyperparams['invlogit_betas'])
L2_reg = fill_parser(parser, np.exp(fixed_hyperparams['log_L2_reg']))
meta = kylist(fake_data, fake_labels, L2_reg)
W_opt = sgd_parsed(grad(indexed_loss_fun),
kylist(W0, alphas, betas, meta),
parser,
callback=callback)
cur_primal_results['weights'] = getval(W_opt).copy()
cur_primal_results['learning_curve'] = getval(learning_curve_dict)
return W_opt, learning_curve_dict
def primal_optimizer(hyperparam_vect, i_hyper):
def indexed_loss_fun(w, L2_vect, i_iter):
rs = RandomState((seed, i_hyper, i_iter)) # Deterministic seed needed for backwards pass.
idxs = rs.randint(N_train, size=batch_size)
return loss_fun(w, train_data['X'][idxs], train_data['T'][idxs], L2_vect)
learning_curve_dict = defaultdict(list)
def callback(x, v, g, i_iter):
if i_iter % thin == 0:
learning_curve_dict['learning_curve'].append(loss_fun(x, **train_data))
learning_curve_dict['grad_norm'].append(np.linalg.norm(g))
learning_curve_dict['weight_norm'].append(np.linalg.norm(x))
learning_curve_dict['velocity_norm'].append(np.linalg.norm(v))
cur_hyperparams = hyperparams.new_vect(hyperparam_vect)
rs = RandomState((seed, i_hyper))
W0 = fill_parser(parser, np.exp(cur_hyperparams['log_param_scale']))
W0 *= rs.randn(W0.size)
alphas = np.exp(cur_hyperparams['log_alphas'])
betas = logit(cur_hyperparams['invlogit_betas'])
L2_reg = fill_parser(parser, np.exp(fixed_hyperparams['log_L2_reg']))
W_opt = sgd_parsed(grad(indexed_loss_fun), kylist(W0, alphas, betas, L2_reg),
parser, callback=callback)
cur_primal_results['weights'] = getval(W_opt).copy()
cur_primal_results['learning_curve'] = getval(learning_curve_dict)
return W_opt, learning_curve_dict
def primal_optimizer(hyperparam_vect, i_hyper):
def indexed_loss_fun(w, meta_vect, i_iter):
(train_data, train_labels, L2_vect) = meta
return loss_fun(w, train_data, train_labels, L2_vect)
#return loss_fun(w, train_data['X'], train_data['T'], L2_vect + np.sum(fake_data.ravel()))
learning_curve_dict = defaultdict(list)
def callback(x, v, g, i_iter):
if i_iter % thin == 0:
# learning_curve_dict['learning_curve'].append(loss_fun(x, getval(cur_hyperparams['fake_data']), fake_labels))
learning_curve_dict['grad_norm'].append(np.linalg.norm(g))
learning_curve_dict['weight_norm'].append(np.linalg.norm(x))
learning_curve_dict['velocity_norm'].append(np.linalg.norm(v))
cur_hyperparams = hyperparams.new_vect(hyperparam_vect)
fake_data = cur_hyperparams['fake_data']
rs = RandomState((seed, i_hyper))
W0 = fill_parser(parser, np.exp(fixed_hyperparams['log_param_scale']))
W0 *= rs.randn(W0.size)
alphas = np.exp(fixed_hyperparams['log_alphas'])
betas = logit(fixed_hyperparams['invlogit_betas'])
L2_reg = fill_parser(parser, np.exp(fixed_hyperparams['log_L2_reg']))
meta = kylist(fake_data, fake_labels, L2_reg)
W_opt = sgd_parsed(grad(indexed_loss_fun), kylist(W0, alphas, betas, meta),
parser, callback=callback)
cur_primal_results['weights'] = getval(W_opt).copy()
cur_primal_results['learning_curve'] = getval(learning_curve_dict)
return W_opt, learning_curve_dict
def primal_loss(z_vect, transform_vect, i_primal, record_results=False):
w_vect = transform_weights(z_vect, transform_vect)
loss = total_loss(w_vect, train_data)
reg = regularization(z_vect)
if VERBOSE and record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}, valid: {2}, reg: {3}".format(
i_primal, getval(loss) / N_scripts, total_loss(getval(w_vect), valid_data) / N_scripts, getval(reg)
)
return loss + reg
def primal_loss(z_vect, transform_vect, i_primal, record_results=False):
w_vect = transform_weights(z_vect, transform_vect)
loss = total_loss(w_vect, train_data)
reg = regularization(z_vect)
if VERBOSE and record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}, valid: {2}, reg: {3}".format(
i_primal,
getval(loss) / N_scripts,
total_loss(getval(w_vect), valid_data) / N_scripts,
getval(reg))
return loss + reg
def sub_primal_stochastic_loss(z_vect, transform_vect, i_primal, i_script):
RS = RandomState((seed, i_hyper, i_primal, i_script))
N_train = train_data[i_script]['X'].shape[0]
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data[i_script], idxs)
loss = loss_from_latents(z_vect, transform_vect, i_script, minibatch)
reg = regularization(z_vect) if i_script == 0 else 0.0
if i_primal % N_thin == 0 and i_script == 0:
print "Iter {0}, full losses: train: {1}, valid: {2}, reg: {3}".format(
i_primal,
total_loss(train_data, getval(z_vect)),
total_loss(valid_data, getval(z_vect)),
getval(reg) / N_scripts_per_iter)
return loss + reg
def sub_primal_stochastic_loss(z_vect, transform_vect, i_primal,
i_script):
RS = RandomState((seed, i_hyper, i_primal, i_script))
N_train = train_data[i_script]['X'].shape[0]
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data[i_script], idxs)
loss = loss_from_latents(z_vect, transform_vect, i_script,
minibatch)
reg = regularization(z_vect) if i_script == 0 else 0.0
if i_primal % N_thin == 0 and i_script == 0:
print "Iter {0}, full losses: train: {1}, valid: {2}, reg: {3}".format(
i_primal, total_loss(train_data, getval(z_vect)),
total_loss(valid_data, getval(z_vect)),
getval(reg) / N_scripts_per_iter)
return loss + reg
def primal_optimizer(hyperparams_vect, meta_epoch):
def indexed_loss_fun(w, L2_vect, i_iter):
rs = RandomState(
(seed, meta_epoch,
i_iter)) # Deterministic seed needed for backwards pass.
idxs = rs.randint(N_train, size=batch_size)
return loss_fun(w, train_data['X'][idxs], train_data['T'][idxs],
L2_vect)
cur_hyperparams = hyperparams.new_vect(hyperparams_vect)
rs = RandomState((seed, meta_epoch))
# Randomly initialize weights
W0 = fill_parser(parser, np.exp(fixed_hyperparams['log_param_scale']))
W0 *= rs.randn(W0.size)
# Init regularization term
L2_reg = fill_parser(parser, np.exp(fixed_hyperparams['log_L2_reg']))
# Set step sizes
alphas = np.exp(cur_hyperparams['log_alphas'])
# Momentum terms
betas = logit(cur_hyperparams['invlogit_betas'])
# Train model
W_opt = sgd_parsed(grad(indexed_loss_fun), kylist(W0, alphas, betas,
L2_reg), parser)
cur_primal_results['weights'] = getval(W_opt).copy()
return W_opt
def hyperloss(hyperparam_vect, i):
learning_curve = []
def callback(x, i):
if i % len(batch_idxs) == 0:
learning_curve.append(
loss_fun(x, X=train_images, T=train_labels))
npr.seed(i)
N_weights = parser.vect.size
V0 = np.zeros(N_weights)
cur_hyperparams = hyperparams.new_vect(hyperparam_vect)
layer_param_scale = [
np.full(parser[name].size,
np.exp(cur_hyperparams['log_param_scale'][i]))
for i, name in enumerate(parser.names)
]
W0 = npr.randn(N_weights) * np.concatenate(layer_param_scale, axis=0)
alphas = np.exp(cur_hyperparams['log_alphas'])
betas = logit(cur_hyperparams['invlogit_betas'])
log_L2_reg = cur_hyperparams['log_L2_reg']
W_opt = sgd5(grad(indexed_loss_fun),
kylist(W0, alphas, betas, log_L2_reg), callback)
all_x.append(getval(W_opt))
all_learning_curves.append(learning_curve)
return valid_loss_fun(W_opt)
def primal_stochastic_loss(z_vect, transform_vect, i_primal):
RS = RandomState((seed, i_hyper, i_primal))
loss = 0.0
for _ in range(N_scripts_per_iter):
i_script = RS.randint(N_scripts)
N_train = train_data[i_script]['X'].shape[0]
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data[i_script], idxs)
loss += loss_from_latents(z_vect, transform_vect, i_script, minibatch)
reg = regularization(z_vect)
if i_primal % 20 == 0:
print "Iter {0}, loss {1}, reg {2}".format(i_primal, getval(loss), getval(reg))
print "Full losses: train: {0}, valid: {1}".format(
total_loss(train_data, getval(z_vect)),
total_loss(valid_data, getval(z_vect)))
return loss + reg
def primal_stochastic_loss(z_vect, transform_vect, i_primal):
RS = RandomState((seed, i_hyper, i_primal))
loss = 0.0
for _ in range(N_scripts_per_iter):
i_script = RS.randint(N_scripts)
N_train = train_data[i_script]['X'].shape[0]
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data[i_script], idxs)
loss += loss_from_latents(z_vect, transform_vect, i_script, minibatch)
reg = regularization(z_vect)
if i_primal % 1 == 0:
print "Iter {0}, loss {1}, reg {2}".format(i_primal, getval(loss), getval(reg))
print "Full losses: train: {0}, valid: {1}".format(
total_loss(train_data, getval(z_vect)),
total_loss(valid_data, getval(z_vect)))
return loss + reg
def primal_loss(z_vect, transform_vect, i_primal, record_results=False):
w_vect = transform_weights(z_vect, transform_vect)
loss = likelihood_loss(w_vect, data)
reg = regularization(z_vect)
if record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}".format(i_primal, getval(loss) / N_scripts)
return loss + reg
def primal_loss(z_vect, transform_vect, i_primal, record_results=False):
w_vect = transform_weights(z_vect, transform_vect)
loss = likelihood_loss(w_vect, data)
reg = regularization(z_vect)
if record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}".format(i_primal, getval(loss) / N_scripts)
return loss + reg
def primal_loss(W, hyperparam_vect, i_primal, reg_penalty=True):
RS = RandomState((seed, i_hyper, i_primal))
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data, idxs)
loss = reg_loss_fun(W, minibatch, hyperparam_vect, reg_penalty)
if verbose and i_primal % 10 == 0:
print "Iter {0}, loss, {1}".format(i_primal, getval(loss))
return loss
def primal_loss(w_vect, reg, i_primal, record_results=False):
RS = RandomState((seed, i_primal, "primal"))
idxs = RS.randint(N_data, size=batch_size)
minibatch = dictslice(data, idxs)
loss = loss_fun(w_vect, **minibatch)
reg = regularization(w_vect, reg)
if record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}".format(i_primal, getval(loss))
return loss + reg
def primal_loss(W, hyperparam_vect, i_primal, reg_penalty=True):
RS = RandomState((seed, i_hyper, i_primal))
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data, idxs)
loss = reg_loss_fun(W, minibatch, hyperparam_vect, reg_penalty)
if verbose and i_primal % 30 == 0:
print "Iter {0}, loss, {1}".format(i_primal, getval(loss))
return loss
def primal_loss(w_vect, reg, i_primal, record_results=False):
RS = RandomState((seed, i_primal, "primal"))
idxs = RS.randint(N_data, size=batch_size)
minibatch = dictslice(data, idxs)
loss = loss_fun(w_vect, **minibatch)
reg = regularization(w_vect, reg)
if record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}".format(i_primal, getval(loss))
return loss + reg
def hyperloss(transform_vect, i_hyper, record_results=True):
RS = RandomState((seed, i_hyper, "hyperloss"))
def primal_loss(z_vect, transform_vect, i_primal, record_results=False):
w_vect = transform_weights(z_vect, transform_vect)
loss = total_loss(w_vect, train_data)
reg = regularization(z_vect)
if VERBOSE and record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}, valid: {2}, reg: {3}".format(
i_primal, getval(loss) / N_scripts, total_loss(getval(w_vect), valid_data) / N_scripts, getval(reg)
)
return loss + reg
z_vect_0 = RS.randn(script_parser.vect.size) * np.exp(log_initialization_scale)
z_vect_final = sgd(grad(primal_loss), transform_vect, z_vect_0, alpha, beta, N_iters, callback=None)
w_vect_final = transform_weights(z_vect_final, transform_vect)
valid_loss = total_loss(w_vect_final, valid_data)
if record_results:
results["valid_loss"].append(getval(valid_loss) / N_scripts)
results["train_loss"].append(total_loss(getval(w_vect_final), train_data) / N_scripts)
results["tests_loss"].append(total_loss(getval(w_vect_final), tests_data) / N_scripts)
return valid_loss
def hyperloss(transform_vect, i_hyper, record_results=True):
RS = RandomState((seed, i_hyper, "hyperloss"))
def primal_loss(z_vect,
transform_vect,
i_primal,
record_results=False):
w_vect = transform_weights(z_vect, transform_vect)
loss = total_loss(w_vect, train_data)
reg = regularization(z_vect)
if VERBOSE and record_results and i_primal % N_thin == 0:
print "Iter {0}: train: {1}, valid: {2}, reg: {3}".format(
i_primal,
getval(loss) / N_scripts,
total_loss(getval(w_vect), valid_data) / N_scripts,
getval(reg))
return loss + reg
z_vect_0 = RS.randn(
script_parser.vect.size) * np.exp(log_initialization_scale)
z_vect_final = sgd(grad(primal_loss),
transform_vect,
z_vect_0,
alpha,
beta,
N_iters,
callback=None)
w_vect_final = transform_weights(z_vect_final, transform_vect)
valid_loss = total_loss(w_vect_final, valid_data)
if record_results:
results['valid_loss'].append(getval(valid_loss) / N_scripts)
results['train_loss'].append(
total_loss(getval(w_vect_final), train_data) / N_scripts)
results['tests_loss'].append(
total_loss(getval(w_vect_final), tests_data) / N_scripts)
return valid_loss
def hyperloss(transform, i_hyper, cur_train_data, cur_valid_data, cur_tests_data, exact_metagrad):
RS = RandomState((seed, i_top, i_hyper, "hyperloss"))
z_vect_0 = RS.randn(N_weights) * np.exp(log_init_scale)
z_vect_final = train_z(cur_train_data, z_vect_0, transform, exact_metagrad)
w_vect_final = transform_weights(z_vect_final, transform)
#TODO: print/store losses and error rates here
print "Training loss (unregularized) = " +str(getval(loss_fun(w_vect_final, **cur_train_data)))
print "Validation loss = " +str(getval(loss_fun(w_vect_final, **cur_valid_data)))
print "Test loss = " +str(getval(loss_fun(w_vect_final, **tests_data)))
print "Training error = "+ str(getval(frac_err(w_vect_final, **cur_train_data)))
print "Validation error = "+ str(getval(frac_err(w_vect_final, **cur_valid_data)))
print "Test error = "+ str(getval(frac_err(w_vect_final, **tests_data)))
return loss_fun(w_vect_final, **cur_valid_data)
def hyperloss(transform, i_hyper, cur_train_data, cur_valid_data, cur_tests_data):
RS = RandomState((seed, i_top, i_hyper, "hyperloss"))
z_vect_0 = RS.randn(N_weights) * np.exp(log_init_scale)
z_vect_final = train_z(cur_train_data, z_vect_0, transform)
w_vect_final = transform_weights(z_vect_final, transform) #TODO: initial scale AND regularization
train_loss = getval(loss_fun(w_vect_final, **cur_train_data))
print "Training loss (unregularized) = " +str(train_loss)
all_train_loss.append(train_loss)
valid_loss = getval(loss_fun(w_vect_final, **cur_valid_data))
print "Validation loss = " +str(valid_loss)
all_valid_loss.append(valid_loss)
tests_loss = getval(loss_fun(w_vect_final, **cur_tests_data))
print "Test loss = " +str(tests_loss)
all_tests_loss.append(tests_loss)
plt.plot(all_train_loss, label="training loss (unregularized)")
plt.plot(all_valid_loss, label="validation loss")
plt.plot(all_tests_loss, label="test loss")
plt.title("loss vs meta iteration")
plt.xlabel("meta iteration")
plt.ylabel("loss")
plt.legend()
plt.savefig("loss2000_corrected.png")
plt.clf()
train_rate = getval(frac_err(w_vect_final, **cur_train_data))
print "Training error rate = " +str(train_rate)
all_train_rates.append(train_rate)
valid_rate = getval(frac_err(w_vect_final, **cur_valid_data))
print "Validation error rate = " +str(valid_rate)
all_valid_rates.append(valid_rate)
tests_rate = getval(frac_err(w_vect_final, **cur_tests_data))
print "Test error rate = " +str(tests_rate)
all_tests_rates.append(tests_rate)
plt.plot(all_train_rates, label="training error rate")
plt.plot(all_valid_rates, label="validation error rate")
plt.plot(all_tests_rates, label="test error rate")
plt.title("error rate vs meta iteration")
plt.xlabel("meta iteration")
plt.ylabel("error rate")
plt.legend()
plt.savefig("error2000_corrected.png")
plt.clf()
return loss_fun(w_vect_final, **cur_valid_data)
def hyperloss(hyperparam_vect, i):
learning_curve = []
def callback(x, i):
if i % len(batch_idxs) == 0:
learning_curve.append(loss_fun(x, X=train_images, T=train_labels))
npr.seed(i)
N_weights = parser.vect.size
V0 = np.zeros(N_weights)
cur_hyperparams = hyperparams.new_vect(hyperparam_vect)
layer_param_scale = [np.full(parser[name].size,
np.exp(cur_hyperparams['log_param_scale'][i]))
for i, name in enumerate(parser.names)]
W0 = npr.randn(N_weights) * np.concatenate(layer_param_scale, axis=0)
alphas = np.exp(cur_hyperparams['log_alphas'])
betas = logit(cur_hyperparams['invlogit_betas'])
log_L2_reg = cur_hyperparams['log_L2_reg']
W_opt = sgd5(grad(indexed_loss_fun), kylist(W0, alphas, betas, log_L2_reg), callback)
all_x.append(getval(W_opt))
all_learning_curves.append(learning_curve)
return valid_loss_fun(W_opt)
def frac_err(W_vect, X, T):
#print getval(predictions(W_vect, X))
#preds = np.argmax(predictions(W_vect, X), axis=1)
# TODO: above doesn't work through gradients
preds = np.argmax(getval(predictions(W_vect, X)), axis=1)
return 100.0 * np.mean(np.argmax(T, axis=1) != preds)
