def callback(params, t, g):
x, y, xy = sample_data(1, n_data, ker=ker)
preds = hyper_predict(params, x, xy, nn_arch, act) # [1, nd]
if plot: p.plot_iter(ax, x[0], x[0], y, preds)
# cov_compare = np.cov(y.ravel())-np.cov(preds.ravel())
print("ITER {} | OBJ {} COV DIFF {}".format(t, objective(params, t), 1))
def callback(params, t, g):
y = sample_gpp(x, 1, ker)
#y=sample_function(x,1)
preds = hyper_predict(params, x, y, nn_arch, act) #[1,nd]
if plot: p.plot_iter(ax, x, x, y, preds)
cd = np.cov(y.ravel()) - np.cov(preds.ravel())
print("ITER {} | OBJ {} COV DIFF {}".format(t, objective(params, t),
cd))
def callback(params, t, g):
plot_inputs = np.linspace(-10, 10, num=500)[:, None]
f_bnn = sample_bnn(params, plot_inputs, 5, arch, act)
#print(params[1])
# Plot data and functions.
p.plot_iter(ax, inputs, plot_inputs, targets, f_bnn)
print("ITER {} | LOSS {}".format(t, -loss(params, t)))
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 callback(params, t, g):
preds = bnn_predict(params, x, nn_arch, act)[:, :, 0] #[1,nd]
#print(preds.shape)
if plot: plot_iter(ax, x.ravel(), x.ravel(), y, preds[0])
print("ITER {} | OBJ {}".format(t, objective(params, t)))