public
/
ottertune
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

fix style

This commit is contained in:
bohanjason 2019-12-18 22:27:46 -08:00 committed by Dana Van Aken
parent 9633b0e99c
commit 13e92bed6a
1 changed files with 8 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
server/analysis/nn_tf.py
View File

@ -210,7 +210,8 @@ class NeuralNet(object):
feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2, feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
self.vars['w3_']: w3, self.vars['b1_']: b1, self.vars['w3_']: w3, self.vars['b1_']: b1,
self.vars['b2_']: b2, self.vars['b3_']: b3, self.vars['b2_']: b2, self.vars['b3_']: b3,
self.vars['X_max_']: X_max, self.vars['X_min_']: X_min}) self.vars['X_max_']: X_max,
self.vars['X_min_']: X_min})
if self.debug: if self.debug:
LOG.info("Recommend phase, y before gradient descent: min %f, max %f, mean %f", LOG.info("Recommend phase, y before gradient descent: min %f, max %f, mean %f",
np.min(y_before), np.max(y_before), np.mean(y_before)) np.min(y_before), np.max(y_before), np.mean(y_before))
@ -227,7 +228,8 @@ class NeuralNet(object):
feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2, feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
self.vars['w3_']: w3, self.vars['b1_']: b1, self.vars['w3_']: w3, self.vars['b1_']: b1,
self.vars['b2_']: b2, self.vars['b3_']: b3, self.vars['b2_']: b2, self.vars['b3_']: b3,
self.vars['X_max_']: X_max, self.vars['X_min_']: X_min}) self.vars['X_max_']: X_max,
self.vars['X_min_']: X_min})
LOG.info("Recommend phase, epoch %d, y: min %f, max %f, mean %f", LOG.info("Recommend phase, epoch %d, y: min %f, max %f, mean %f",
i, np.min(y_train), np.max(y_train), np.mean(y_train)) i, np.min(y_train), np.max(y_train), np.mean(y_train))
@ -235,8 +237,10 @@ class NeuralNet(object):
feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2, feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
self.vars['w3_']: w3, self.vars['b1_']: b1, self.vars['w3_']: w3, self.vars['b1_']: b1,
self.vars['b2_']: b2, self.vars['b3_']: b3, self.vars['b2_']: b2, self.vars['b3_']: b3,
self.vars['X_max_']: X_max, self.vars['X_min_']: X_min}) self.vars['X_max_']: X_max,
X_recommend = sess.run(self.vars['x_bounded_'], feed_dict={self.vars['X_max_']: X_max, self.vars['X_min_']: X_min}) self.vars['X_min_']: X_min})
X_recommend = sess.run(self.vars['x_bounded_'], feed_dict={self.vars['X_max_']: X_max,
self.vars['X_min_']: X_min})
res = NeuralNetResult(minl=y_recommend, minl_conf=X_recommend) res = NeuralNetResult(minl=y_recommend, minl_conf=X_recommend)
if self.debug: if self.debug: