def run():
print "Running experiment..."
sgd_optimized_points = []
ed_optimized_points = []
for i in xrange(N_samples):
rs = RandomState((seed, i))
x0 = rs.randn(D) * x_init_scale
v0 = rs.randn(D) * v_init_scale
sgd_optimized_points.append(
sgd(grad(nllfunt),
x=x0,
v=v0,
learn_rate=alpha,
decay=decay,
iters=N_iter))
rs = RandomState((seed, i))
x0 = rs.randn(D) * x_init_scale
v0 = rs.randn(D) * v_init_scale
ed_optimized_points.append(
entropic_descent(grad(nllfunt),
x=x0,
v=v0,
learn_rate=alpha,
decay=decay,
iters=N_iter,
theta=theta,
rs=rs))
entropy = np.log(decay) * D * N_iter
return sgd_optimized_points, ed_optimized_points, entropy
def run():
print "Running experiment..."
sgd_optimized_points = []
ed_optimized_points = []
aed_optimized_points = []
asgd_optimized_points = []
for i in xrange(N_samples):
rs = RandomState((seed, i))
x0 = rs.randn(D) * x_init_scale
v0 = rs.randn(D) * v_init_scale
sgd_optimized_points.append(
sgd(grad(nllfunt), x=x0, v=v0, learn_rate=alpha, decay=decay, iters=N_iter))
rs = RandomState((seed, i))
x0 = rs.randn(D) * x_init_scale
v0 = rs.randn(D) * v_init_scale
ed_optimized_points.append(
entropic_descent(grad(nllfunt), x=x0, v=v0, learn_rate=alpha, decay=decay, iters=N_iter, theta=theta, rs=rs))
entropy = np.log(decay) * D * N_iter
rs = RandomState((seed, i))
x0 = rs.randn(D) * x_init_scale
v0 = rs.randn(D) * v_init_scale
aed_optimized_points.append(
adaptive_entropic_descent(grad(nllfunt), x=x0, v=v0, init_learn_rate=alpha, init_log_decay=np.log(decay), meta_learn_rate=meta_alpha, meta_decay=meta_decay, iters=N_iter))
rs = RandomState((seed, i))
x0 = rs.randn(D) * x_init_scale
v0 = rs.randn(D) * v_init_scale
asgd_optimized_points.append(
adaptive_sgd(grad(nllfunt), x=x0, v=v0, init_learn_rate=alpha, init_log_decay=np.log(decay), meta_learn_rate=meta_alpha, meta_decay=meta_decay, iters=N_iter))
return sgd_optimized_points, ed_optimized_points, aed_optimized_points, asgd_optimized_points, entropy
def run():
print "Running experiment..."
results = defaultdict(list)
for i in xrange(N_samples):
print i,
def callback(x, t, v, entropy, log_decay):
results[("x", i)].append(
x.copy()) # Replace this with a loop over kwargs?
results[("entropy", i)].append(entropy)
results[("velocity", i)].append(v)
results[("log decay", i)].append(log_decay[0])
results[("likelihood", i)].append(-nllfun(x))
rs = RandomState((seed, i))
x0 = rs.randn(D) * init_scale
v0 = rs.randn(D) # TODO: account for entropy of init.
init_entropy = 0.5 * D * (1 + np.log(2*np.pi))\
+ np.sum(np.log(init_scale))
aed3(gradfun,
callback=callback,
x=x0,
v=v0,
learn_rate=epsilon,
iters=N_iter,
init_log_decay=init_log_decay,
decay_learn_rate=decay_learn_rate,
entropy=init_entropy)
return results
def run():
print "Running experiment..."
sgd_optimized_points = []
for i in xrange(N_samples):
rs = RandomState((seed, i))
x0 = rs.randn(D) * x_init_scale
v0 = rs.randn(D) * v_init_scale
sgd_optimized_points.append(
sgd(grad(nllfunt), x=x0, v=v0, learn_rate=alpha, decay=decay, iters=N_iter))
return sgd_optimized_points, entropy
def run():
(train_images, train_labels),\
(tests_images, tests_labels) = load_data_subset(N_train, N_tests)
parser, pred_fun, nllfun, frac_err = make_nn_funs(layer_sizes)
N_param = len(parser.vect)
print "Running experiment..."
results = defaultdict(list)
for i in xrange(N_samples):
def indexed_loss_fun(w, i_iter):
rs = RandomState((seed, i, i_iter))
idxs = rs.randint(N_train, size=batch_size)
nll = nllfun(w, train_images[idxs], train_labels[idxs]) * N_train
nlp = neg_log_prior(w)
return nll + nlp
gradfun = grad(indexed_loss_fun)
def callback(x, t, v, entropy):
results[("entropy", i)].append(entropy / N_train)
results[("v_norm", i)].append(norm(v) / np.sqrt(N_param))
results[("minibatch_likelihood",
i)].append(-indexed_loss_fun(x, t))
results[("log_prior_per_dpt",
i)].append(-neg_log_prior(x) / N_train)
if t % thin != 0 and t != N_iter and t != 0: return
results[('iterations', i)].append(t)
results[("train_likelihood",
i)].append(-nllfun(x, train_images, train_labels))
results[("tests_likelihood",
i)].append(-nllfun(x, tests_images, tests_labels))
results[("tests_error",
i)].append(frac_err(x, tests_images, tests_labels))
print "Iteration {0:5} Train likelihood {1:2.4f} Test likelihood {2:2.4f}" \
" Test Err {3:2.4f}".format(t, results[("train_likelihood", i)][-1],
results[("tests_likelihood", i)][-1],
results[("tests_error", i)][-1])
rs = RandomState((seed, i))
x0 = rs.randn(N_param) * init_scale
v0 = rs.randn(N_param) # TODO: account for entropy of init.
#entropy = 0.5 * D * (1 + np.log(2*np.pi)) + np.sum(np.log(x_scale)) + 0.5 * (D - norm(v) **2)
aed3(gradfun,
callback=callback,
x=x0,
v=v0,
learn_rate=epsilon,
iters=N_iter,
init_log_decay=init_log_decay,
decay_learn_rate=decay_learn_rate)
return results
def run():
(train_images, train_labels), (tests_images, tests_labels) = load_data_subset(N_train, N_tests)
parser, pred_fun, nllfun, frac_err = make_nn_funs(layer_sizes)
N_param = len(parser.vect)
print "Running experiment..."
results = defaultdict(list)
for i in xrange(N_samples):
def indexed_loss_fun(w, i_iter):
rs = RandomState((seed, i, i_iter))
idxs = rs.randint(N_train, size=batch_size)
nll = nllfun(w, train_images[idxs], train_labels[idxs]) * N_train
nlp = neg_log_prior(w)
return nll + nlp
gradfun = grad(indexed_loss_fun)
def callback(x, t, v, entropy):
results[("entropy", i)].append(entropy / N_train)
results[("v_norm", i)].append(norm(v) / np.sqrt(N_param))
results[("minibatch_likelihood", i)].append(-indexed_loss_fun(x, t))
results[("log_prior_per_dpt", i)].append(-neg_log_prior(x) / N_train)
if t % thin != 0 and t != N_iter and t != 0:
return
results[("iterations", i)].append(t)
results[("train_likelihood", i)].append(-nllfun(x, train_images, train_labels))
results[("tests_likelihood", i)].append(-nllfun(x, tests_images, tests_labels))
results[("tests_error", i)].append(frac_err(x, tests_images, tests_labels))
print "Iteration {0:5} Train likelihood {1:2.4f} Test likelihood {2:2.4f}" " Test Err {3:2.4f}".format(
t,
results[("train_likelihood", i)][-1],
results[("tests_likelihood", i)][-1],
results[("tests_error", i)][-1],
)
rs = RandomState((seed, i))
x0 = rs.randn(N_param) * init_scale
v0 = rs.randn(N_param) # TODO: account for entropy of init.
# entropy = 0.5 * D * (1 + np.log(2*np.pi)) + np.sum(np.log(x_scale)) + 0.5 * (D - norm(v) **2)
aed3(
gradfun,
callback=callback,
x=x0,
v=v0,
learn_rate=epsilon,
iters=N_iter,
init_log_decay=init_log_decay,
decay_learn_rate=decay_learn_rate,
)
return results
def run():
print "Running experiment..."
results = defaultdict(list)
for i in xrange(N_samples):
print i,
def callback(x, t, v, entropy, log_decay):
results[("x", i)].append(x.copy()) # Replace this with a loop over kwargs?
results[("entropy", i)].append(entropy)
results[("velocity", i)].append(v)
results[("log decay", i)].append(log_decay[0])
results[("likelihood", i)].append(-nllfun(x))
rs = RandomState((seed, i))
x0 = rs.randn(D) * init_scale
v0 = rs.randn(D) # TODO: account for entropy of init.
init_entropy = 0.5 * D * (1 + np.log(2*np.pi))\
+ np.sum(np.log(init_scale))
aed3(gradfun, callback=callback, x=x0, v=v0, learn_rate=epsilon, iters=N_iter,
init_log_decay=init_log_decay, decay_learn_rate=decay_learn_rate,
entropy=init_entropy)
return results