def callback(params, t, g):
print("iteration {} Log likelihood {}".format(t, objective(params, t)))
if plot_during:
plt.cla()
x_plot = np.reshape(np.linspace(-8, 8, 400), (400, 1))
pred_mean, pred_cov = predict(params, X, y, x_plot) # shapes [N_data], [N_data, N_data]
std = np.sqrt(np.diag(pred_cov)) # shape [N_data]
ax.plot(x_plot, pred_mean, 'b')
ax.fill_between(x_plot.ravel(),
pred_mean - 1.96*std,
pred_mean + 1.96*std,
color=sns.xkcd_rgb["sky blue"])
# Show sampled functions from posterior.
sf = mvnorm(pred_mean, pred_cov, size=5) # shape = [samples, N_data]
ax.plot(x_plot, sf.T)
ax.plot(X, y, 'k.')
ax.set_ylim([-2, 3])
ax.set_xticks([])
ax.set_yticks([])
plt.draw()
plt.pause(1.0/60.0)
if t == 1:
D = X, y[:, None]
p = sample_f(params, X, y, x_plot, samples)
plotting.plot_deciles(x_plot.ravel(), p, D, save_dir, plot="gp")
def callback(params, t, g, objective):
# Sample functions from posterior f ~ p(f|phi) or p(f|varphi)
N_samples = 5
plot_inputs = np.linspace(-8, 8, num=400)
f_bnn = sample_bnn(plot_inputs, N_samples, params)
# Plot data and functions.
if plot_during_:
plt.cla()
ax.plot(inputs.ravel(), targets.ravel(), 'k.')
ax.plot(plot_inputs, f_bnn, color='r')
ax.set_title("fitting to toy data")
ax.set_ylim([-5, 5])
plt.draw()
plt.pause(1.0 / 60.0)
if t > 25:
D = (inputs.ravel(), targets.ravel())
plotting.plot_deciles(plot_inputs,
f_bnn,
D,
save_dir + "iter {}".format(t),
plot="bnn")
print("Iteration {} | vlb {}".format(t, -objective(params, t)))
def save(params, t):
D = (inputs.ravel(), targets.ravel())
x_plot = np.linspace(-8, 8, num=400)
save_title = "exp-" + str(exp_num) + "iter " + str(t)
# predictions from posterior of bnn
p = sample_bnn(x_plot, 5, params)
save_dir = os.path.join(os.getcwd(), 'plots', 'gpp-bnn', save_title + data)
plotting.plot_deciles(x_plot, p, D, save_dir, plot="gpp")
def callback(params, t, g):
plot_inputs = np.linspace(-8, 8, num=400)[:, None]
f_bnn = sample_bnn(params, plot_inputs, 5, arch, act)
# Plot data and functions.
p.plot_iter(ax, inputs, plot_inputs, targets, f_bnn)
print("ITER {} | LOSS {}".format(t, -loss(params, t)))
if t > 50:
D = inputs, targets
x_plot = np.reshape(np.linspace(-8, 8, 400), (400, 1))
pred = sample_bnn(params, x_plot, 5, arch, act)
p.plot_deciles(x_plot.ravel(), pred.T, D, str(t) + "bnnpostfullprior", plot="gpp")
def train_bnn(data='expx', n_data=20, n_samples=5, arch=[1,20,20,1],
prior_params=None, prior_type=None, act='rbf',
iters=65, lr=0.07, plot=True, save=False):
if type(data) == str:
inputs, targets = build_toy_dataset()
else:
inputs, targets = data
if plot: fig, ax = p.setup_plot()
def loss(params, t):
return vlb_objective(params, inputs, targets, arch, n_samples, act=act,
prior_params=prior_params, prior_type=prior_type)
def callback(params, t, g):
plot_inputs = np.linspace(-8, 8, num=400)[:, None]
f_bnn = sample_bnn(params, plot_inputs, 5, arch, act)
# Plot data and functions.
p.plot_iter(ax, inputs, plot_inputs, targets, f_bnn)
print("ITER {} | LOSS {}".format(t, -loss(params, t)))
if t > 50:
D = inputs, targets
x_plot = np.reshape(np.linspace(-8, 8, 400), (400, 1))
pred = sample_bnn(params, x_plot, 5, arch, act)
p.plot_deciles(x_plot.ravel(), pred.T, D, str(t) + "bnnpostfullprior", plot="gpp")
var_params = adam(grad(loss), init_var_params(arch),
step_size=lr, num_iters=iters, callback=callback)
D = inputs, targets
x_plot = np.reshape(np.linspace(-8, 8, 400), (400, 1))
pred = sample_bnn(var_params, x_plot, 5, arch, act)
p.plot_deciles(x_plot.ravel(), pred.T, D,"bnnpostfullprior", plot="gpp")
ax.set_ylim([-2, 3])
ax.set_xticks([])
ax.set_yticks([])
plt.draw()
plt.pause(1.0/60.0)
if t == 1:
D = X, y[:, None]
p = sample_f(params, X, y, x_plot, samples)
plotting.plot_deciles(x_plot.ravel(), p, D, save_dir, plot="gp")
# Initialize covariance parameters
rs = npr.RandomState(0)
init_params = 0.1 * rs.randn(num_params)
cov_params = adam(grad(objective), init_params,
step_size=0.1, num_iters=iters, callback=callback)
if save_plots:
D = X, y[:, None]
x_plot = np.reshape(np.linspace(-8, 8, 400), (400, 1))
p = sample_f(cov_params, X, y, x_plot, samples)
print(p.shape)
plotting.plot_deciles(x_plot.ravel(), p, D, save_dir, plot="gp")
pred_mean - 1.96 * std,
pred_mean + 1.96 * std,
color='b')
# Show sampled functions from posterior.
sf = mvnorm(pred_mean, pred_cov, size=5) # [ns, nd]
ax.plot(x_plot, sf.T)
ax.plot(X, y, 'k.')
ax.set_ylim([-2, 3])
ax.set_xticks([])
ax.set_yticks([])
plt.draw()
plt.pause(1.0 / 60.0)
# Initialize covariance parameters
rs = npr.RandomState(0)
init_params = 0.1 * rs.randn(num_params)
cov_params = adam(grad(objective),
init_params,
step_size=0.01,
num_iters=iters,
callback=callback)
if save_plots:
D = X, y[:, None]
x_plot = np.reshape(np.linspace(-8, 8, 400), (400, 1))
p = sample_functions(cov_params, X, y, x_plot, samples)
print(p.shape)
plotting.plot_deciles(x_plot.ravel(), p, D, "gppost", plot="gp")
print("Iteration {} | vlb {}".format(t, -objective(params, t)))
callback_vlb = lambda params, t, g: callback(params, t, g, vlb)
init_var_params = init_bnn_params(num_weights)
var_params = adam(grad(vlb), init_var_params,
step_size=0.1, num_iters=iters_2, callback=callback_vlb)
# PLOT STUFF BELOW HERE
if save_plot:
N_data = 400
N_samples = 5
D = (inputs.ravel(), targets.ravel())
x_plot = np.linspace(-8, 8, num=N_data)
save_title = "exp-" + str(exp_num)
# predictions from posterior of bnn
p = sample_bnn(x_plot, N_samples, var_params)
save_dir = os.path.join(os.getcwd(), 'plots', 'gpp-bnn', save_title+data)
plotting.plot_deciles(x_plot, p, D, save_dir, plot="gpp")