def test_rms_prop():
N_weights = 5
W0 = 0.1 * npr.randn(N_weights)
(loss_fun, true_argmin) = make_optimization_problem(N_weights)
x_min = rms_prop(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():
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_L2_reg'] = np.full(N_weight_types, init_log_L2_reg)
hyperparams['log_param_scale'] = np.full(N_weight_types,
init_log_param_scale)
hyperparams['log_alphas'] = np.full(N_iters, init_log_alphas)
hyperparams['invlogit_betas'] = np.full(N_iters, init_invlogit_betas)
def primal_optimizer(hyperparam_vect, i_hyper):
def indexed_loss_fun(w, L2_vect, i_iter):
seed = i_hyper * 10**6 + i_iter
idxs = npr.RandomState(seed).randint(N_train, size=batch_size)
return loss_fun(w, train_data['X'][idxs], train_data['T'][idxs],
L2_vect)
learning_curve = []
def callback(x, v, g, i_iter):
if i_iter % N_batches == 0:
learning_curve.append(loss_fun(x, **train_data))
cur_hyperparams = hyperparams.new_vect(hyperparam_vect)
W0 = fill_parser(parser, np.exp(cur_hyperparams['log_param_scale']))
W0 *= npr.RandomState(i_hyper).randn(W0.size)
alphas = np.exp(cur_hyperparams['log_alphas'])
betas = logit(cur_hyperparams['invlogit_betas'])
L2_reg = fill_parser(parser, np.exp(cur_hyperparams['log_L2_reg']))
V0 = np.zeros(W0.size)
W_opt = sgd4(grad(indexed_loss_fun), kylist(W0, alphas, betas, L2_reg),
callback)
return W_opt, learning_curve
def hyperloss(hyperparam_vect, i_hyper):
W_opt, _ = primal_optimizer(hyperparam_vect, i_hyper)
return loss_fun(W_opt, **valid_data)
hyperloss_grad = grad(hyperloss)
meta_results = defaultdict(list)
def meta_callback(hyperparam_vect, i_hyper, g):
print "Epoch {0}".format(i_hyper)
x, learning_curve = 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['learning_curves'].append(learning_curve)
final_result = rms_prop(hyperloss_grad, hyperparams.vect, meta_callback,
N_meta_iter, meta_alpha)
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_L2_reg'] = np.full(N_weight_types, init_log_L2_reg)
hyperparams['log_param_scale'] = np.full(N_weight_types, init_log_param_scale)
hyperparams['log_alphas'] = np.full(N_iters, init_log_alphas)
hyperparams['invlogit_betas'] = np.full(N_iters, init_invlogit_betas)
#fixed_hyperparams = VectorParser()
#fixed_hyperparams['invlogit_betas'] = np.full(N_iters, init_invlogit_betas)
# TODO: memoize
def primal_optimizer(hyperparam_vect, i_hyper):
def indexed_loss_fun(w, L2_vect, i_iter):
rs = npr.RandomState(npr.RandomState(global_seed + i_hyper).randint(1000))
seed = i_hyper * 10**6 + 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 = []
def callback(x, i_iter):
if i_iter % N_batches == 0:
learning_curve.append(loss_fun(x, **train_data))
cur_hyperparams = hyperparams.new_vect(hyperparam_vect)
W0 = fill_parser(parser, np.exp(cur_hyperparams['log_param_scale']))
W0 *= npr.RandomState(global_seed + i_hyper).randn(W0.size)
alphas = np.exp(cur_hyperparams['log_alphas'])
betas = logit(cur_hyperparams['invlogit_betas'])
L2_reg = fill_parser(parser, np.exp(cur_hyperparams['log_L2_reg']))
V0 = np.zeros(W0.size)
W_opt = sgd4(grad(indexed_loss_fun), kylist(W0, alphas, betas, L2_reg), callback)
return W_opt, learning_curve
def hyperloss(hyperparam_vect, i_hyper):
W_opt, _ = primal_optimizer(hyperparam_vect, i_hyper)
# return loss_fun(W_opt, **valid_data)
return loss_fun(W_opt, **train_data)
hyperloss_grad = grad(hyperloss)
meta_results = defaultdict(list)
def meta_callback(hyperparam_vect, i_hyper):
print "Meta Epoch {0}".format(i_hyper)
x, learning_curve = 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['learning_curves'].append(learning_curve)
final_result = rms_prop(hyperloss_grad, hyperparams.vect,
meta_callback, N_meta_iter, meta_alpha, gamma=0.0)
parser.vect = None # No need to pickle zeros
return meta_results, parser
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 = np.ones(N_weights) * init_param_scale
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']
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):
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 = rms_prop(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():
parser, loss_fun = make_parabola(dimension)
N_weight_types = len(parser.names)
hyperparams = VectorParser()
hyperparams['log_L2_reg'] = np.full(N_weight_types, init_log_L2_reg)
hyperparams['log_param_scale'] = np.full(N_weight_types, init_log_param_scale)
hyperparams['log_alphas'] = np.full(N_iters, init_log_alphas)
hyperparams['invlogit_betas'] = np.full(N_iters, init_invlogit_betas)
def primal_optimizer(hyperparam_vect, i_hyper):
learning_curve = []
def callback(x, i_iter):
learning_curve.append(loss_fun(x))
cur_hyperparams = hyperparams.new_vect(hyperparam_vect)
W0 = fill_parser(parser, np.exp(cur_hyperparams['log_param_scale']))
W0 *= npr.RandomState(hash(i_hyper)).randn(W0.size)
alphas = np.exp(cur_hyperparams['log_alphas'])
betas = logit(cur_hyperparams['invlogit_betas'])
L2_reg = fill_parser(parser, np.exp(cur_hyperparams['log_L2_reg']))
W_opt = sgd4(grad(loss_fun), kylist(W0, alphas, betas, L2_reg), callback)
callback(W_opt, N_iters)
return W_opt, learning_curve
def hyperloss(hyperparam_vect, i_hyper):
W_opt, _ = primal_optimizer(hyperparam_vect, i_hyper)
return loss_fun(W_opt)
hyperloss_grad = grad(hyperloss)
meta_results = defaultdict(list)
def meta_callback(hyperparam_vect, i_hyper):
print "Meta Epoch {0}".format(i_hyper)
x, learning_curve = 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))
meta_results['learning_curves'].append(learning_curve)
final_result = rms_prop(hyperloss_grad, hyperparams.vect,
meta_callback, N_meta_iter, meta_alpha, gamma=0.0)
meta_callback(final_result, N_meta_iter)
parser.vect = None # No need to pickle zeros
return meta_results, parser
def run():
parser, loss_fun = make_parabola(dimension)
N_weight_types = len(parser.names)
hyperparams = VectorParser()
hyperparams['log_L2_reg'] = np.full(N_weight_types, init_log_L2_reg)
hyperparams['log_param_scale'] = np.full(N_weight_types, init_log_param_scale)
hyperparams['log_alphas'] = np.full(N_iters, init_log_alphas)
hyperparams['invlogit_betas'] = np.full(N_iters, init_invlogit_betas)
def primal_optimizer(hyperparam_vect, i_hyper):
learning_curve = []
def callback(x, i_iter):
learning_curve.append(loss_fun(x))
cur_hyperparams = hyperparams.new_vect(hyperparam_vect)
W0 = fill_parser(parser, np.exp(cur_hyperparams['log_param_scale']))
W0 *= npr.RandomState(hash(i_hyper)).randn(W0.size)
alphas = np.exp(cur_hyperparams['log_alphas'])
betas = logit(cur_hyperparams['invlogit_betas'])
L2_reg = fill_parser(parser, np.exp(cur_hyperparams['log_L2_reg']))
W_opt = sgd4(grad(loss_fun), kylist(W0, alphas, betas, L2_reg), callback)
callback(W_opt, N_iters)
return W_opt, learning_curve
def hyperloss(hyperparam_vect, i_hyper):
W_opt, _ = primal_optimizer(hyperparam_vect, i_hyper)
return loss_fun(W_opt)
hyperloss_grad = grad(hyperloss)
meta_results = defaultdict(list)
def meta_callback(hyperparam_vect, i_hyper):
print "Meta Epoch {0}".format(i_hyper)
x, learning_curve = 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))
meta_results['learning_curves'].append(learning_curve)
final_result = rms_prop(hyperloss_grad, hyperparams.vect,
meta_callback, N_meta_iter, meta_alpha, gamma=0.0)
meta_callback(final_result, N_meta_iter)
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_L2_reg'] = np.full(N_weight_types, init_log_L2_reg)
hyperparams['log_param_scale'] = np.full(N_weight_types,
init_log_param_scale)
hyperparams['log_alphas'] = np.full(N_iters, init_log_alphas)
hyperparams['invlogit_betas'] = np.full(N_iters, init_invlogit_betas)
fixed_hyperparams = VectorParser()
fixed_hyperparams['log_param_scale'] = np.full(N_iters,
init_log_param_scale)
# TODO: memoize
def primal_optimizer(hyperparam_vect, i_hyper):
def indexed_loss_fun(w, L2_vect, i_iter):
rs = npr.RandomState(
npr.RandomState(global_seed + i_hyper).randint(1000))
seed = i_hyper * 10**6 + 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 % N_batches == 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)
W0 = fill_parser(parser, np.exp(fixed_hyperparams['log_param_scale']))
W0 *= npr.RandomState(global_seed + i_hyper).randn(W0.size)
alphas = np.exp(cur_hyperparams['log_alphas'])
betas = logit(cur_hyperparams['invlogit_betas'])
L2_reg = fill_parser(parser, np.exp(cur_hyperparams['log_L2_reg']))
W_opt = sgd4(grad(indexed_loss_fun), kylist(W0, alphas, betas, L2_reg),
callback)
#callback(W_opt, N_iters)
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, **valid_data)
hyperloss_grad = grad(hyperloss)
meta_results = defaultdict(list)
def meta_callback(hyperparam_vect, i_hyper):
print "Meta Epoch {0}".format(i_hyper)
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['learning_curves'].append(learning_curve_dict)
final_result = rms_prop(hyperloss_grad,
hyperparams.vect,
meta_callback,
N_meta_iter,
meta_alpha,
gamma=0.0)
meta_callback(final_result, N_meta_iter)
parser.vect = None # No need to pickle zeros
return meta_results, parser
def run():
(train_images, train_labels),\
(valid_images, valid_labels),\
(tests_images, tests_labels) = load_data_subset(N_train, N_valid, N_tests)
batch_idxs = BatchList(N_train, batch_size)
N_iters = N_epochs * len(batch_idxs)
parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes)
N_weight_types = len(parser.names)
hyperparams = VectorParser()
hyperparams['log_L2_reg'] = np.full(N_weight_types, init_log_L2_reg)
hyperparams['log_param_scale'] = np.full(N_weight_types,
init_log_param_scale)
hyperparams['log_alphas'] = np.full(N_iters, init_log_alphas)
hyperparams['invlogit_betas'] = np.full(N_iters, init_invlogit_betas)
def indexed_loss_fun(w, log_L2_reg, i):
idxs = batch_idxs[i % len(batch_idxs)]
partial_vects = [
np.full(parser[name].size, np.exp(log_L2_reg[i]))
for i, name in enumerate(parser.names)
]
L2_reg_vect = np.concatenate(partial_vects, axis=0)
return loss_fun(w,
X=train_images[idxs],
T=train_labels[idxs],
L2_reg=L2_reg_vect)
def train_loss_fun(w, log_L2_reg=0.0):
return loss_fun(w, X=train_images, T=train_labels)
def valid_loss_fun(w, log_L2_reg=0.0):
return loss_fun(w, X=valid_images, T=valid_labels)
def tests_loss_fun(w, log_L2_reg=0.0):
return loss_fun(w, X=tests_images, T=tests_labels)
all_learning_curves = []
all_x = []
def hyperloss_grad(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']
results = sgd3(indexed_loss_fun,
valid_loss_fun,
W0,
V0,
alphas,
betas,
log_L2_reg,
callback=callback)
hypergrads = hyperparams.copy()
hypergrads['log_L2_reg'] = results['dMd_meta']
weights_grad = parser.new_vect(W0 * results['dMd_x'])
hypergrads['log_param_scale'] = [
np.sum(weights_grad[name]) for name in parser.names
]
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)
return hypergrads.vect
add_fields = ['train_loss', 'valid_loss', 'tests_loss']
meta_results = {field: [] for field in add_fields + hyperparams.names}
def meta_callback(hyperparam_vect, i):
print "Meta iter {0}".format(i)
x = all_x[-1]
cur_hyperparams = hyperparams.new_vect(hyperparam_vect.copy())
log_L2_reg = cur_hyperparams['log_L2_reg']
for field in cur_hyperparams.names:
meta_results[field].append(cur_hyperparams[field])
meta_results['train_loss'].append(train_loss_fun(x))
meta_results['valid_loss'].append(valid_loss_fun(x))
meta_results['tests_loss'].append(tests_loss_fun(x))
final_result = rms_prop(hyperloss_grad, hyperparams.vect, meta_callback,
N_meta_iter, meta_alpha)
meta_results['all_learning_curves'] = all_learning_curves
parser.vect = None # No need to pickle zeros
return meta_results, parser
def run():
RS = RandomState((seed, "top_rs"))
all_alphabets = omniglot.load_data()
RS.shuffle(all_alphabets)
train_alphabets = all_alphabets[:-N_test_alphabets]
tests_alphabets = all_alphabets[-N_test_alphabets:]
w_parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes)
N_weights = w_parser.vect.size
hyperparams_0 = VectorParser()
hyperparams_0['log_scale'] = log_scale_init * np.ones(N_weights)
hyperparams_0['offset'] = offset_init_std * RS.randn(N_weights)
def reg_loss_fun(W, data, hyperparam_vect, reg_penalty):
hyperparams = hyperparams_0.new_vect(hyperparam_vect)
Z = np.exp(hyperparams['log_scale']) * W + hyperparams['offset']
return loss_fun(Z, **data) + np.dot(W, W) * reg_penalty
def hyperloss(hyperparam_vect, i_hyper, alphabets, verbose=True, report_train_loss=False):
RS = RandomState((seed, i_hyper, "hyperloss"))
alphabet = shuffle_alphabet(RS.choice(alphabets), RS)
N_train = alphabet['X'].shape[0] - N_valid_dpts
train_data = dictslice(alphabet, slice(None, N_train))
if report_train_loss:
valid_data = dictslice(alphabet, slice(None, N_valid_dpts))
else:
valid_data = dictslice(alphabet, slice(N_train, None))
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
W0 = RS.randn(N_weights) * initialization_scale
W_final = sgd(grad(primal_loss), hyperparam_vect, W0, alpha, beta, N_iters, callback=None)
return reg_loss_fun(W_final, valid_data, hyperparam_vect, reg_penalty=False)
results = defaultdict(list)
def record_results(hyperparam_vect, i_hyper, g):
print "Meta iter {0}. Recording results".format(i_hyper)
# RS = RandomState((seed, i_hyper, "evaluation"))
def loss_fun(alphabets, report_train_loss):
RS = RandomState((seed, "evaluation")) # Same alphabet with i_hyper now
return np.mean([hyperloss(hyperparam_vect, RS.int32(), alphabets=alphabets,
verbose=False, report_train_loss=report_train_loss)
for i in range(N_alphabets_eval)])
cur_hyperparams = hyperparams_0.new_vect(hyperparam_vect.copy())
if i_hyper % N_hyper_thin == 0:
# Storing O(N_weights) is a bit expensive so we thin it out and store in low precision
for field in cur_hyperparams.names:
results[field].append(cur_hyperparams[field].astype(np.float16))
results['train_loss'].append(loss_fun(train_alphabets, report_train_loss=True))
results['valid_loss'].append(loss_fun(train_alphabets, report_train_loss=False))
results['tests_loss'].append(loss_fun(tests_alphabets, report_train_loss=False))
print "Train:", results['train_loss']
print "Valid:", results['valid_loss']
print "Tests:", results['tests_loss']
train_hyperloss = partial(hyperloss, alphabets=train_alphabets)
rms_prop(grad(train_hyperloss), hyperparams_0.vect, record_results, N_meta_iter, meta_alpha, gamma=0)
return results
def run():
(train_images, train_labels),\
(valid_images, valid_labels),\
(tests_images, tests_labels) = load_data_subset(N_train, N_valid, N_tests)
batch_idxs = BatchList(N_train, batch_size)
N_iters = N_epochs * len(batch_idxs)
parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes)
N_weight_types = len(parser.names)
hyperparams = VectorParser()
hyperparams['log_L2_reg'] = np.full(N_weight_types, init_log_L2_reg)
hyperparams['log_param_scale'] = np.full(N_weight_types, init_log_param_scale)
hyperparams['log_alphas'] = np.full(N_iters, init_log_alphas)
hyperparams['invlogit_betas'] = np.full(N_iters, init_invlogit_betas)
def indexed_loss_fun(w, log_L2_reg, i):
idxs = batch_idxs[i % len(batch_idxs)]
partial_vects = [np.full(parser[name].size, np.exp(log_L2_reg[i]))
for i, name in enumerate(parser.names)]
L2_reg_vect = np.concatenate(partial_vects, axis=0)
return loss_fun(w, X=train_images[idxs], T=train_labels[idxs], L2_reg=L2_reg_vect)
def train_loss_fun(w, log_L2_reg=0.0):
return loss_fun(w, X=train_images, T=train_labels)
def valid_loss_fun(w, log_L2_reg=0.0):
return loss_fun(w, X=valid_images, T=valid_labels)
def tests_loss_fun(w, log_L2_reg=0.0):
return loss_fun(w, X=tests_images, T=tests_labels)
all_learning_curves = []
all_x = []
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)
hyperloss_grad = grad(hyperloss)
add_fields = ['train_loss', 'valid_loss', 'tests_loss']
meta_results = {field : [] for field in add_fields + hyperparams.names}
def meta_callback(hyperparam_vect, i):
x = all_x[-1]
cur_hyperparams = hyperparams.new_vect(hyperparam_vect.copy())
log_L2_reg = cur_hyperparams['log_L2_reg']
for field in cur_hyperparams.names:
meta_results[field].append(cur_hyperparams[field])
meta_results['train_loss'].append(train_loss_fun(x))
meta_results['valid_loss'].append(valid_loss_fun(x))
meta_results['tests_loss'].append(tests_loss_fun(x))
final_result = rms_prop(hyperloss_grad, hyperparams.vect, meta_callback, N_meta_iter, meta_alpha)
meta_results['all_learning_curves'] = all_learning_curves
parser.vect = None # No need to pickle zeros
return meta_results, parser
def run():
(train_images, train_labels),\
(valid_images, valid_labels),\
(tests_images, tests_labels) = load_data_subset(N_train, N_valid, N_tests)
batch_idxs = BatchList(N_train, batch_size)
N_iters = N_epochs * len(batch_idxs)
parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes)
N_weight_types = len(parser.names)
hyperparams = VectorParser()
hyperparams['log_L2_reg'] = np.full(N_weight_types, init_log_L2_reg)
hyperparams['log_param_scale'] = np.full(N_weight_types, init_log_param_scale)
hyperparams['log_alphas'] = np.full(N_iters, init_log_alphas)
hyperparams['invlogit_betas'] = np.full(N_iters, init_invlogit_betas)
def train_loss_fun(w, log_L2_reg=0.0):
return loss_fun(w, X=train_images, T=train_labels)
def valid_loss_fun(w, log_L2_reg=0.0):
return loss_fun(w, X=valid_images, T=valid_labels)
def tests_loss_fun(w, log_L2_reg=0.0):
return loss_fun(w, X=tests_images, T=tests_labels)
all_learning_curves = []
all_x = []
def hyperloss_grad(hyperparam_vect, ii):
learning_curve = []
def callback(x, i):
if i % len(batch_idxs) == 0:
learning_curve.append(loss_fun(x, X=train_images, T=train_labels))
def indexed_loss_fun(w, log_L2_reg, j):
# idxs = batch_idxs[i % len(batch_idxs)]
npr.seed(1000 * ii + j)
idxs = npr.randint(N_train, size=len(batch_idxs))
partial_vects = [np.full(parser[name].size, np.exp(log_L2_reg[i]))
for i, name in enumerate(parser.names)]
L2_reg_vect = np.concatenate(partial_vects, axis=0)
return loss_fun(w, X=train_images[idxs], T=train_labels[idxs], L2_reg=L2_reg_vect)
npr.seed(ii)
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']
results = sgd3(indexed_loss_fun, valid_loss_fun, W0, V0,
alphas, betas, log_L2_reg, callback=callback)
hypergrads = hyperparams.copy()
hypergrads['log_L2_reg'] = results['dMd_meta']
weights_grad = parser.new_vect(W0 * results['dMd_x'])
hypergrads['log_param_scale'] = [np.sum(weights_grad[name])
for name in parser.names]
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)
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):
if i % N_meta_thin == 0:
print "Meta iter {0}".format(i)
x = all_x[-1]
cur_hyperparams = hyperparams.new_vect(hyperparam_vect.copy())
log_L2_reg = cur_hyperparams['log_L2_reg']
for field in cur_hyperparams.names:
meta_results[field].append(cur_hyperparams[field])
meta_results['train_loss'].append(train_loss_fun(x))
meta_results['valid_loss'].append(valid_loss_fun(x))
meta_results['tests_loss'].append(tests_loss_fun(x))
meta_results['iter_num'].append(i)
final_result = rms_prop(hyperloss_grad, hyperparams.vect,
meta_callback, N_meta_iter, meta_alpha, meta_gamma)
meta_results['all_learning_curves'] = all_learning_curves
parser.vect = None # No need to pickle zeros
return meta_results, parser
def run():
RS = RandomState((seed, "top_rs"))
all_alphabets = omniglot.load_data()
RS.shuffle(all_alphabets)
train_alphabets = all_alphabets[:-N_test_alphabets]
tests_alphabets = all_alphabets[-N_test_alphabets:]
w_parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes)
N_weights = w_parser.vect.size
hyperparams_0 = VectorParser()
hyperparams_0['log_scale'] = log_scale_init * np.ones(N_weights)
hyperparams_0['offset'] = offset_init_std * RS.randn(N_weights)
def reg_loss_fun(W, data, hyperparam_vect, reg_penalty):
hyperparams = hyperparams_0.new_vect(hyperparam_vect)
Z = np.exp(hyperparams['log_scale']) * W + hyperparams['offset']
return loss_fun(Z, **data) + np.dot(W, W) * reg_penalty
def hyperloss(hyperparam_vect, i_hyper, alphabets, verbose=True, report_train_loss=False):
RS = RandomState((seed, i_hyper, "hyperloss"))
alphabet = shuffle_alphabet(RS.choice(alphabets), RS)
N_train = alphabet['X'].shape[0] - N_valid_dpts
train_data = dictslice(alphabet, slice(None, N_train))
if report_train_loss:
valid_data = dictslice(alphabet, slice(None, N_valid_dpts))
else:
valid_data = dictslice(alphabet, slice(N_train, None))
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
W0 = np.zeros(N_weights)
W_final = sgd(grad(primal_loss), hyperparam_vect, W0, alpha, beta, N_iters, callback=None)
return reg_loss_fun(W_final, valid_data, hyperparam_vect, reg_penalty=False)
results = defaultdict(list)
def record_results(hyperparam_vect, i_hyper, g):
print "Meta iter {0}. Recording results".format(i_hyper)
RS = RandomState((seed, i_hyper, "evaluation"))
def loss_fun(alphabets, report_train_loss):
return np.mean([hyperloss(hyperparam_vect, RS.int32(), alphabets=alphabets,
verbose=False, report_train_loss=report_train_loss)
for i in range(N_alphabets_eval)])
cur_hyperparams = hyperparams_0.new_vect(hyperparam_vect.copy())
if i_hyper % N_hyper_thin == 0:
# Storing O(N_weights) is a bit expensive so we thin it out and store in low precision
for field in cur_hyperparams.names:
results[field].append(cur_hyperparams[field].astype(np.float16))
results['train_loss'].append(loss_fun(train_alphabets, report_train_loss=True))
results['valid_loss'].append(loss_fun(train_alphabets, report_train_loss=False))
results['tests_loss'].append(loss_fun(tests_alphabets, report_train_loss=False))
print "Train:", results['train_loss']
print "Valid:", results['valid_loss']
print "Tests:", results['tests_loss']
train_hyperloss = partial(hyperloss, alphabets=train_alphabets)
rms_prop(grad(train_hyperloss), hyperparams_0.vect, record_results, N_meta_iter, meta_alpha, gamma=0)
return results
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 = np.ones(N_weights) * init_param_scale
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']
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):
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 = rms_prop(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
