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)
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 or i_iter == N_iters or i_iter == 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))
learning_curve_dict['iteration'].append(i_iter + 1)
print "iteration", i_iter
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)
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, 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 full_loss(params):
(W0, alphas, betas, meta) = params
result = sgd_parsed(grad(loss_fun), kylist(W0, alphas, betas, meta), parser)
return loss_fun(result, meta)
