improve dnn
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3a722df5e2
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@ -108,6 +108,12 @@ class NeuralNet(object):
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with self.graph.as_default():
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with self.session.as_default(): # pylint: disable=not-context-manager
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x_ = tf.Variable(tf.ones([batch_size, self.n_input]))
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X_min_ = tf.placeholder(tf.float32, [self.n_input])
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X_max_ = tf.placeholder(tf.float32, [self.n_input])
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x_bounded_ = tf.minimum(x_, X_max_)
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x_bounded_ = tf.maximum(x_bounded_, X_min_)
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x_bounded_ = tf.cast(x_bounded_, tf.float32)
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w1_ = tf.placeholder(tf.float32, [self.n_input, 64])
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b1_ = tf.placeholder(tf.float32, [64])
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w2_ = tf.placeholder(tf.float32, [64, 64])
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@ -128,6 +134,9 @@ class NeuralNet(object):
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self.vars['b1_'] = b1_
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self.vars['b2_'] = b2_
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self.vars['b3_'] = b3_
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self.vars['X_min_'] = X_min_
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self.vars['X_max_'] = X_max_
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self.vars['x_bounded_'] = x_bounded_
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self.ops['train_'] = train_
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def fit(self, X_train, y_train, fit_epochs=500):
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@ -169,6 +178,10 @@ class NeuralNet(object):
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def recommend(self, X_start, X_min=None, X_max=None, recommend_epochs=500, explore=False):
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batch_size = len(X_start)
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assert(batch_size == self.batch_size)
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if X_min is None:
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X_min = np.tile([-np.infty], self.n_input)
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if X_max is None:
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X_max = np.tile([np.infty], self.n_input)
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with self.graph.as_default():
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with self.session.as_default() as sess: # pylint: disable=not-context-manager
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@ -196,7 +209,8 @@ class NeuralNet(object):
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y_before = sess.run(self.vars['y_'],
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feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
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self.vars['w3_']: w3, self.vars['b1_']: b1,
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self.vars['b2_']: b2, self.vars['b3_']: b3})
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self.vars['b2_']: b2, self.vars['b3_']: b3,
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self.vars['X_max_']: X_max, self.vars['X_min_']: X_min})
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if self.debug:
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LOG.info("Recommend phase, y before gradient descent: min %f, max %f, mean %f",
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np.min(y_before), np.max(y_before), np.mean(y_before))
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@ -205,29 +219,24 @@ class NeuralNet(object):
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sess.run(self.ops['train_'],
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feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
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self.vars['w3_']: w3, self.vars['b1_']: b1,
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self.vars['b2_']: b2, self.vars['b3_']: b3})
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# constrain by X_min and X_max
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if X_min is not None and X_max is not None:
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X_train = sess.run(self.vars['x_'])
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X_train = np.minimum(X_train, X_max)
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X_train = np.maximum(X_train, X_min)
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constraint_x_op = self.vars['x_'].assign(X_train)
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sess.run(constraint_x_op)
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self.vars['b2_']: b2, self.vars['b3_']: b3,
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self.vars['X_max_']: X_max, self.vars['X_min_']: X_min})
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if self.debug and i % self.debug_interval == 0:
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y_train = sess.run(self.vars['y_'],
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feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
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self.vars['w3_']: w3, self.vars['b1_']: b1,
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self.vars['b2_']: b2, self.vars['b3_']: b3})
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self.vars['b2_']: b2, self.vars['b3_']: b3,
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self.vars['X_max_']: X_max, self.vars['X_min_']: X_min})
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LOG.info("Recommend phase, epoch %d, y: min %f, max %f, mean %f",
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i, np.min(y_train), np.max(y_train), np.mean(y_train))
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y_recommend = sess.run(self.vars['y_'],
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feed_dict={self.vars['w1_']: w1, self.vars['w2_']: w2,
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self.vars['w3_']: w3, self.vars['b1_']: b1,
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self.vars['b2_']: b2, self.vars['b3_']: b3})
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X_recommend = sess.run(self.vars['x_'])
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self.vars['b2_']: b2, self.vars['b3_']: b3,
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self.vars['X_max_']: X_max, self.vars['X_min_']: X_min})
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X_recommend = sess.run(self.vars['x_bounded_'], feed_dict={self.vars['X_max_']: X_max, self.vars['X_min_']: X_min})
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res = NeuralNetResult(minl=y_recommend, minl_conf=X_recommend)
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if self.debug:
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@ -84,6 +84,9 @@ HP_LEARNING_RATE = 0.001
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# ---GRADIENT DESCENT FOR DNN---
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DNN_TRAIN_ITER = 100
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# Gradient Descent iteration for recommendation
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DNN_GD_ITER = 100
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DNN_EXPLORE = False
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DNN_EXPLORE_ITER = 500
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@ -44,7 +44,7 @@ from website.settings import (USE_GPFLOW, DEFAULT_LENGTH_SCALE, DEFAULT_MAGNITUD
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DNN_TRAIN_ITER, DNN_EXPLORE, DNN_EXPLORE_ITER,
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DNN_NOISE_SCALE_BEGIN, DNN_NOISE_SCALE_END,
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DNN_DEBUG, DNN_DEBUG_INTERVAL, GPR_DEBUG, UCB_BETA,
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GPR_MODEL_NAME, ENABLE_DUMMY_ENCODER)
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GPR_MODEL_NAME, ENABLE_DUMMY_ENCODER, DNN_GD_ITER)
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from website.settings import INIT_FLIP_PROB, FLIP_PROB_DECAY
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from website.types import VarType
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@ -700,7 +700,7 @@ def configuration_recommendation(recommendation_input):
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model_nn.set_weights_bin(session.dnn_model)
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model_nn.fit(X_scaled, y_scaled, fit_epochs=DNN_TRAIN_ITER)
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res = model_nn.recommend(X_samples, X_min, X_max,
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explore=DNN_EXPLORE, recommend_epochs=MAX_ITER)
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explore=DNN_EXPLORE, recommend_epochs=DNN_GD_ITER)
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session.dnn_model = model_nn.get_weights_bin()
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session.save()
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