def run(n_runs):
N_iters = N_epochs
N_meta_iter = n_runs
parser, loss_fun = make_toy_funs()
N_weight_types = len(parser.names)
N_weights = parser.vect.size
hyperparams = VectorParser()
hyperparams['log_alphas'] = np.full(N_iters, init_log_alphas)
hyperparams['invlogit_betas'] = np.full(N_iters, init_invlogit_betas)
hyperparams['V0'] = np.full(N_weights, init_V0)
all_learning_curves = []
all_param_curves = []
all_x = []
def hyperloss_grad(hyperparam_vect, ii):
learning_curve = []
params_curve = []
def callback(x, i):
params_curve.append(x)
learning_curve.append(loss_fun(x))
def indexed_loss_fun(w, log_L2_reg, j):
return loss_fun(w)
cur_hyperparams = hyperparams.new_vect(hyperparam_vect)
W0 = init_params
V0 = cur_hyperparams['V0']
alphas = np.exp(cur_hyperparams['log_alphas'])
betas = logit(cur_hyperparams['invlogit_betas'])
log_L2_reg = 0.0
results = sgd3(indexed_loss_fun, loss_fun, W0, V0,
alphas, betas, log_L2_reg, callback=callback)
hypergrads = hyperparams.new_vect(np.zeros(hyperparams.vect.shape))
hypergrads['V0'] = results['dMd_v'] * 0
hypergrads['log_alphas'] = results['dMd_alphas'] * alphas
hypergrads['invlogit_betas'] = (results['dMd_betas'] *
d_logit(cur_hyperparams['invlogit_betas']))
all_x.append(results['x_final'])
all_learning_curves.append(learning_curve)
all_param_curves.append(params_curve)
return hypergrads.vect
add_fields = ['train_loss', 'valid_loss', 'tests_loss', 'iter_num']
meta_results = {field : [] for field in add_fields + hyperparams.names}
def meta_callback(hyperparam_vect, i, g):
if i % N_meta_thin == 0:
print "Meta iter {0}".format(i)
x = all_x[-1]
cur_hyperparams = hyperparams.new_vect(hyperparam_vect.copy())
for field in cur_hyperparams.names:
meta_results[field].append(cur_hyperparams[field])
meta_results['train_loss'].append(loss_fun(x))
meta_results['iter_num'].append(i)
final_result = simple_sgd(hyperloss_grad, hyperparams.vect,
meta_callback, N_meta_iter, meta_alpha, meta_gamma)
meta_results['all_learning_curves'] = all_learning_curves
meta_results['all_param_curves'] = all_param_curves
parser.vect = None # No need to pickle zeros
return meta_results, parser
def test_simple_sgd():
N_weights = 5
W0 = 0.1 * npr.randn(N_weights)
(loss_fun, true_argmin) = make_optimization_problem(N_weights)
x_min = simple_sgd(grad(loss_fun), W0)
assert np.allclose(x_min, true_argmin, rtol=1e-3, atol=1e-4), \
"Diffs are: {0}".format(x_min - true_argmin)
def run():
N_iters = N_epochs
parser, loss_fun = make_toy_funs()
N_weight_types = len(parser.names)
N_weights = parser.vect.size
hyperparams = VectorParser()
hyperparams['log_alphas'] = np.full(N_iters, init_log_alphas)
hyperparams['invlogit_betas'] = np.full(N_iters, init_invlogit_betas)
hyperparams['V0'] = np.full(N_weights, init_V0)
all_learning_curves = []
all_param_curves = []
all_x = []
def hyperloss_grad(hyperparam_vect, ii):
learning_curve = []
params_curve = []
def callback(x, i):
params_curve.append(x)
learning_curve.append(loss_fun(x))
def indexed_loss_fun(w, log_L2_reg, j):
return loss_fun(w)
cur_hyperparams = hyperparams.new_vect(hyperparam_vect)
W0 = init_params
V0 = cur_hyperparams['V0']
alphas = np.exp(cur_hyperparams['log_alphas'])
betas = logit(cur_hyperparams['invlogit_betas'])
log_L2_reg = 0.0
results = sgd3(indexed_loss_fun, loss_fun, W0, V0,
alphas, betas, log_L2_reg, callback=callback)
hypergrads = hyperparams.copy()
hypergrads['V0'] = results['dMd_v'] * 0
hypergrads['log_alphas'] = results['dMd_alphas'] * alphas
hypergrads['invlogit_betas'] = (results['dMd_betas'] *
d_logit(cur_hyperparams['invlogit_betas']))
all_x.append(results['x_final'])
all_learning_curves.append(learning_curve)
all_param_curves.append(params_curve)
return hypergrads.vect
add_fields = ['train_loss', 'valid_loss', 'tests_loss', 'iter_num']
meta_results = {field : [] for field in add_fields + hyperparams.names}
def meta_callback(hyperparam_vect, i, g):
if i % N_meta_thin == 0:
print "Meta iter {0}".format(i)
x = all_x[-1]
cur_hyperparams = hyperparams.new_vect(hyperparam_vect.copy())
for field in cur_hyperparams.names:
meta_results[field].append(cur_hyperparams[field])
meta_results['train_loss'].append(loss_fun(x))
meta_results['iter_num'].append(i)
final_result = simple_sgd(hyperloss_grad, hyperparams.vect,
meta_callback, N_meta_iter, meta_alpha, meta_gamma)
meta_results['all_learning_curves'] = all_learning_curves
meta_results['all_param_curves'] = all_param_curves
parser.vect = None # No need to pickle zeros
return meta_results, parser
def run():
train_data, valid_data, tests_data = load_data_dicts(N_train, N_valid, N_tests)
parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes)
N_weight_types = len(parser.names)
hyperparams = VectorParser()
hyperparams['log_param_scale'] = np.full(N_weight_types, init_log_param_scale)
hyperparams['log_alphas'] = np.full((N_iters, N_weight_types), init_log_alphas)
hyperparams['invlogit_betas'] = np.full((N_iters, N_weight_types), init_invlogit_betas)
fixed_hyperparams = VectorParser()
fixed_hyperparams['log_L2_reg'] = np.full(N_weight_types, init_log_L2_reg)
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 hyperloss(hyperparam_vect, i_hyper):
W_opt, _ = primal_optimizer(hyperparam_vect, i_hyper)
return loss_fun(W_opt, **train_data)
hyperloss_grad = grad(hyperloss)
meta_results = defaultdict(list)
old_metagrad = [np.ones(hyperparams.vect.size)]
def meta_callback(hyperparam_vect, i_hyper, metagrad=None):
x, learning_curve_dict = primal_optimizer(hyperparam_vect, i_hyper)
cur_hyperparams = hyperparams.new_vect(hyperparam_vect.copy())
for field in cur_hyperparams.names:
meta_results[field].append(cur_hyperparams[field])
meta_results['train_loss'].append(loss_fun(x, **train_data))
meta_results['valid_loss'].append(loss_fun(x, **valid_data))
meta_results['tests_loss'].append(loss_fun(x, **tests_data))
meta_results['test_err'].append(frac_err(x, **tests_data))
meta_results['learning_curves'].append(learning_curve_dict)
if metagrad is not None:
meta_results['meta_grad_magnitude'].append(np.linalg.norm(metagrad))
meta_results['meta_grad_angle'].append(np.dot(old_metagrad[0], metagrad) \
/ (np.linalg.norm(metagrad)*
np.linalg.norm(old_metagrad[0])))
old_metagrad[0] = metagrad
print "Meta Epoch {0} Train loss {1:2.4f} Valid Loss {2:2.4f}" \
" Test Loss {3:2.4f} Test Err {4:2.4f}".format(
i_hyper, meta_results['train_loss'][-1], meta_results['valid_loss'][-1],
meta_results['train_loss'][-1], meta_results['test_err'][-1])
initial_hypergrad = hyperloss_grad( hyperparams.vect, 0)
parsed_init_hypergrad = hyperparams.new_vect(initial_hypergrad.copy())
final_result = simple_sgd(hyperloss_grad, hyperparams.vect, meta_callback, N_meta_iter, meta_alpha)
meta_callback(final_result, N_meta_iter)
parser.vect = None # No need to pickle zeros
return meta_results, parser, parsed_init_hypergrad
