fix sigma in new gpr model
This commit is contained in:
parent
7ee615a3f3
commit
5654d23637
|
@ -53,7 +53,10 @@ def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3.,
|
||||||
|
|
||||||
beta_t = tf.constant(ucb_beta, name='ucb_beta', dtype=settings.float_type)
|
beta_t = tf.constant(ucb_beta, name='ucb_beta', dtype=settings.float_type)
|
||||||
y_mean_var = model.likelihood.predict_mean_and_var(*model._build_predict(Xin))
|
y_mean_var = model.likelihood.predict_mean_and_var(*model._build_predict(Xin))
|
||||||
loss = tf.subtract(y_mean_var[0], tf.multiply(beta_t, y_mean_var[1]), name='loss_fn')
|
y_mean = y_mean_var[0]
|
||||||
|
y_var = y_mean_var[1]
|
||||||
|
y_std = tf.sqrt(y_var)
|
||||||
|
loss = tf.subtract(y_mean, tf.multiply(beta_t, y_std), name='loss_fn')
|
||||||
opt = tf.train.AdamOptimizer(learning_rate, epsilon=1e-6)
|
opt = tf.train.AdamOptimizer(learning_rate, epsilon=1e-6)
|
||||||
train_op = opt.minimize(loss)
|
train_op = opt.minimize(loss)
|
||||||
variables = opt.variables()
|
variables = opt.variables()
|
||||||
|
@ -64,10 +67,11 @@ def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3.,
|
||||||
for i in range(maxiter):
|
for i in range(maxiter):
|
||||||
session.run(train_op)
|
session.run(train_op)
|
||||||
Xnew_value = session.run(Xnew_bounded)
|
Xnew_value = session.run(Xnew_bounded)
|
||||||
y_mean_value, y_var_value = session.run(y_mean_var)
|
y_mean_value = session.run(y_mean)
|
||||||
|
y_std_value = session.run(y_std)
|
||||||
loss_value = session.run(loss)
|
loss_value = session.run(loss)
|
||||||
assert_all_finite(Xnew_value)
|
assert_all_finite(Xnew_value)
|
||||||
assert_all_finite(y_mean_value)
|
assert_all_finite(y_mean_value)
|
||||||
assert_all_finite(y_var_value)
|
assert_all_finite(y_std_value)
|
||||||
assert_all_finite(loss_value)
|
assert_all_finite(loss_value)
|
||||||
return GPRGDResult(y_mean_value, y_var_value, loss_value, Xnew_value)
|
return GPRGDResult(y_mean_value, y_std_value, loss_value, Xnew_value)
|
||||||
|
|
Loading…
Reference in New Issue