# create validation data - here we'll just a 1-d grid
X_val = np.atleast_2d(np.linspace(-3, 3, 100)).T
y_val = np.expand_dims(X_val[:, 0], 1) # just dummy data
params = {
"init_stddev_1_w": np.sqrt(10),
"init_stddev_1_b": np.sqrt(10), # set these equal
"init_stddev_2_w": 1.0 / np.sqrt(100) # normal scaling
}
trainer = Trainer(X_train=X_train,
X_val=X_val,
y_train=y_train,
base=NN,
n_ensembles=5,
data_noise=0.001,
params=params)
plot_priors(X_val, trainer.y_prior, n_ensembles=5)
trainer.train()
trainer.predict()
plot_pred(X_train, X_val, y_train, trainer.y_pred, n_ensembles=5)
y_pred_mu, y_pred_std = trainer.ensemble()
plot_result(X_train, X_val, y_train, trainer.y_pred, y_pred_mu, y_pred_std)
