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hyperparameter debug info for new gpr

This commit is contained in:
bohanjason 2019-12-04 23:59:18 -05:00 committed by Dana Van Aken
parent f66543d965
commit 70f9f952d5
3 changed files with 9 additions and 4 deletions
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2
server/analysis/gpr/gprc.py
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@ -45,4 +45,4 @@ class GPRC(GPR):
else: else:
fvar = self.kern.Kdiag(Xnew) - tf.reduce_sum(tf.square(A), 0) fvar = self.kern.Kdiag(Xnew) - tf.reduce_sum(tf.square(A), 0)
fvar = tf.tile(tf.reshape(fvar, (-1, 1)), [1, tf.shape(self.Y)[1]]) fvar = tf.tile(tf.reshape(fvar, (-1, 1)), [1, tf.shape(self.Y)[1]])
return fmean, fvar return fmean, fvar, self.kern.variance, self.kern.lengthscales, self.likelihood.variance

9
server/analysis/gpr/optimize.py
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@ -26,7 +26,7 @@ class GPRGDResult():
def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3., def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3.,
active_dims=None, bounds=None): active_dims=None, bounds=None, debug=True):
Xnew_arr = check_array(Xnew_arr, copy=False, warn_on_dtype=True, dtype=FLOAT_DTYPES) Xnew_arr = check_array(Xnew_arr, copy=False, warn_on_dtype=True, dtype=FLOAT_DTYPES)
Xnew = tf.Variable(Xnew_arr, name='Xnew', dtype=settings.float_type) Xnew = tf.Variable(Xnew_arr, name='Xnew', dtype=settings.float_type)
@ -52,7 +52,8 @@ def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3.,
Xin = Xnew_bounded Xin = Xnew_bounded
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)) fmean, fvar, kvar, kls, lvar = model._build_predict(Xin) # pylint: disable=protected-access
y_mean_var = model.likelihood.predict_mean_and_var(fmean, fvar)
y_mean = y_mean_var[0] y_mean = y_mean_var[0]
y_var = y_mean_var[1] y_var = y_mean_var[1]
y_std = tf.sqrt(y_var) y_std = tf.sqrt(y_var)
@ -74,4 +75,8 @@ def tf_optimize(model, Xnew_arr, learning_rate=0.01, maxiter=100, ucb_beta=3.,
assert_all_finite(y_mean_value) assert_all_finite(y_mean_value)
assert_all_finite(y_std_value) assert_all_finite(y_std_value)
assert_all_finite(loss_value) assert_all_finite(loss_value)
if debug:
LOG.info("kernel variance: %f", session.run(kvar))
LOG.info("kernel lengthscale: %f", session.run(kls))
LOG.info("likelihood variance: %f", session.run(lvar))
return GPRGDResult(y_mean_value, y_std_value, loss_value, Xnew_value) return GPRGDResult(y_mean_value, y_std_value, loss_value, Xnew_value)

2
server/website/website/settings/constants.py
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@ -66,7 +66,7 @@ HP_MAX_ITER = 5000
HP_LEARNING_RATE = 0.001 HP_LEARNING_RATE = 0.001
# ---GRADIENT DESCENT FOR DNN--- # ---GRADIENT DESCENT FOR DNN---
DNN_TRAIN_ITER = 500 DNN_TRAIN_ITER = 100
DNN_EXPLORE = False DNN_EXPLORE = False