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

fix typos

This commit is contained in:
bohanjason 2019-09-26 22:47:20 -04:00 committed by Dana Van Aken
parent e1b97bca9b
commit be955cc812
1 changed files with 5 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
server/analysis/nn_tf.py
View File

@ -45,12 +45,12 @@ class NeuralNet(object):
self.n_input = n_input self.n_input = n_input
self.debug = debug self.debug = debug
self.debug_interval = debug_interval self.debug_interval = debug_interval
self.learning_rate = 0.01 self.learning_rate = learning_rate
self.batch_size = batch_size self.batch_size = batch_size
self.explore_iters = explore_iters self.explore_iters = explore_iters
self.noise_scale_begin = noise_scale_begin self.noise_scale_begin = noise_scale_begin
self.noise_scale_end = noise_scale_end self.noise_scale_end = noise_scale_end
self.optimizer = tf.train.AdamOptimizer(learning_rate=0.01) self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
# input X is placeholder, weights are variables. # input X is placeholder, weights are variables.
self.model = keras.Sequential([ self.model = keras.Sequential([
layers.Dense(64, activation=tf.nn.relu, input_shape=[n_input]), layers.Dense(64, activation=tf.nn.relu, input_shape=[n_input]),
@ -93,7 +93,7 @@ class NeuralNet(object):
l1_ = tf.nn.relu(tf.add(tf.matmul(x_, w1_), b1_)) l1_ = tf.nn.relu(tf.add(tf.matmul(x_, w1_), b1_))
l2_ = tf.nn.relu(tf.add(tf.matmul(l1_, w2_), b2_)) l2_ = tf.nn.relu(tf.add(tf.matmul(l1_, w2_), b2_))
y_ = tf.add(tf.matmul(l2_, w3_), b3_) y_ = tf.add(tf.matmul(l2_, w3_), b3_)
optimizer_ = tf.train.AdamOptimizer(learning_rate=0.01) optimizer_ = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
train_ = optimizer_.minimize(y_) train_ = optimizer_.minimize(y_)
self.vars['x_'] = x_ self.vars['x_'] = x_
@ -155,8 +155,8 @@ class NeuralNet(object):
w3 = self.add_noise(w3) w3 = self.add_noise(w3)
b3 = self.add_noise(b3) b3 = self.add_noise(b3)
if self.debug:
y_predict = self.predict(X_start) y_predict = self.predict(X_start)
if self.debug:
LOG.info("Recommend phase, y prediction: min %f, max %f, mean %f", LOG.info("Recommend phase, y prediction: min %f, max %f, mean %f",
np.min(y_predict), np.max(y_predict), np.mean(y_predict)) np.min(y_predict), np.max(y_predict), np.mean(y_predict))
@ -204,7 +204,7 @@ class NeuralNet(object):
if self.debug: if self.debug:
LOG.info("Recommend phase, epoch %d, y after gradient descent: \ LOG.info("Recommend phase, epoch %d, y after gradient descent: \
min %f, max %f, mean %f", recommend_epochs, np.mean(y_recommend), min %f, max %f, mean %f", recommend_epochs, np.min(y_recommend),
np.max(y_recommend), np.mean(y_recommend)) np.max(y_recommend), np.mean(y_recommend))
self.recommend_iters += 1 self.recommend_iters += 1