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improve dnn

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This commit is contained in:
Bohan Zhang
2019-12-18 21:37:40 -08:00
committed by Dana Van Aken
parent 3a722df5e2
commit 9633b0e99c
3 changed files with 27 additions and 15 deletions
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35
server/analysis/nn_tf.py
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@@ -108,6 +108,12 @@ class NeuralNet(object):
with self.graph.as_default():
with self.session.as_default(): # pylint: disable=not-context-manager
x_ = tf.Variable(tf.ones([batch_size, self.n_input]))
X_min_ = tf.placeholder(tf.float32, [self.n_input])
X_max_ = tf.placeholder(tf.float32, [self.n_input])
x_bounded_ = tf.minimum(x_, X_max_)
x_bounded_ = tf.maximum(x_bounded_, X_min_)
x_bounded_ = tf.cast(x_bounded_, tf.float32)
w1_ = tf.placeholder(tf.float32, [self.n_input, 64])
b1_ = tf.placeholder(tf.float32, [64])
w2_ = tf.placeholder(tf.float32, [64, 64])
@@ -128,6 +134,9 @@ class NeuralNet(object):
self.vars['b1_'] = b1_
self.vars['b2_'] = b2_
self.vars['b3_'] = b3_
self.vars['X_min_'] = X_min_
self.vars['X_max_'] = X_max_
self.vars['x_bounded_'] = x_bounded_
self.ops['train_'] = train_
def fit(self, X_train, y_train, fit_epochs=500):
@@ -169,6 +178,10 @@ class NeuralNet(object):
def recommend(self, X_start, X_min=None, X_max=None, recommend_epochs=500, explore=False):
batch_size = len(X_start)
assert(batch_size == self.batch_size)
if X_min is None:
X_min = np.tile([-np.infty], self.n_input)
if X_max is None:
X_max = np.tile([np.infty], self.n_input)
with self.graph.as_default():
with self.session.as_default() as sess: # pylint: disable=not-context-manager
@@ -196,7 +209,8 @@ class NeuralNet(object):
y_before = sess.run(self.vars['y_'],
feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
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})
if self.debug:
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))
@@ -205,29 +219,24 @@ class NeuralNet(object):
sess.run(self.ops['train_'],
feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
self.vars['w3_']: w3, self.vars['b1_']: b1,
self.vars['b2_']: b2, self.vars['b3_']: b3})
# constrain by X_min and X_max
if X_min is not None and X_max is not None:
X_train = sess.run(self.vars['x_'])
X_train = np.minimum(X_train, X_max)
X_train = np.maximum(X_train, X_min)
constraint_x_op = self.vars['x_'].assign(X_train)
sess.run(constraint_x_op)
self.vars['b2_']: b2, self.vars['b3_']: b3,
self.vars['X_max_']: X_max, self.vars['X_min_']: X_min})
if self.debug and i % self.debug_interval == 0:
y_train = sess.run(self.vars['y_'],
feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
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})
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))
y_recommend = sess.run(self.vars['y_'],
feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
self.vars['w3_']: w3, self.vars['b1_']: b1,
self.vars['b2_']: b2, self.vars['b3_']: b3})
X_recommend = sess.run(self.vars['x_'])
self.vars['b2_']: b2, self.vars['b3_']: b3,
self.vars['X_max_']: X_max, 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)
if self.debug: