212 lines
8.5 KiB
Python
212 lines
8.5 KiB
Python
#
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# OtterTune - nn_tf.py
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#
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# Copyright (c) 2017-18, Carnegie Mellon University Database Group
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#
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'''
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Created on Sep 16, 2019
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@author: Bohan Zhang
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'''
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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from .util import get_analysis_logger
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LOG = get_analysis_logger(__name__)
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class NeuralNetResult(object):
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def __init__(self, minl=None, minl_conf=None):
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self.minl = minl
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self.minl_conf = minl_conf
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class NeuralNet(object):
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def __init__(self,
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n_input,
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weights_file,
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learning_rate=0.01,
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debug=False,
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debug_interval=100,
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batch_size=1,
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explore_iters=500,
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noise_scale_begin=0.1,
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noise_scale_end=0):
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# absolute path for the model weitghs file
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# one model for each (project, session)
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self.weights_file = weights_file
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self.history = None
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self.recommend_iters = 0
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self.n_input = n_input
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self.debug = debug
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self.debug_interval = debug_interval
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self.learning_rate = 0.01
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self.batch_size = batch_size
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self.explore_iters = explore_iters
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self.noise_scale_begin = noise_scale_begin
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self.noise_scale_end = noise_scale_end
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self.optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
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# input X is placeholder, weights are variables.
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self.model = keras.Sequential([
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layers.Dense(64, activation=tf.nn.relu, input_shape=[n_input]),
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layers.Dropout(0.5),
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layers.Dense(64, activation=tf.nn.relu),
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layers.Dense(1)
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])
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self.load_weights()
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self.model.compile(loss='mean_squared_error',
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optimizer=self.optimizer,
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metrics=['mean_squared_error', 'mean_absolute_error'])
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self.vars = {}
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self.ops = {}
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self.build_graph()
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def save_weights(self):
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self.model.save_weights(self.weights_file)
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def load_weights(self):
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try:
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self.model.load_weights(self.weights_file)
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if self.debug:
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LOG.info('Neural Network Model weights file exists, load weights from the file')
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except Exception: # pylint: disable=broad-except
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LOG.info('Weights file does not match neural network model, train model from scratch')
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# Build same neural network as self.model, But input X is variables,
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# weights are placedholders. Find optimial X using gradient descent.
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def build_graph(self):
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batch_size = self.batch_size
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self.graph = tf.Graph()
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with self.graph.as_default():
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x_ = tf.Variable(tf.ones([batch_size, self.n_input]))
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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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b2_ = tf.placeholder(tf.float32, [64])
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w3_ = tf.placeholder(tf.float32, [64, 1])
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b3_ = tf.placeholder(tf.float32, [1])
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l1_ = tf.nn.relu(tf.add(tf.matmul(x_, w1_), b1_))
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l2_ = tf.nn.relu(tf.add(tf.matmul(l1_, w2_), b2_))
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y_ = tf.add(tf.matmul(l2_, w3_), b3_)
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optimizer_ = tf.train.AdamOptimizer(learning_rate=0.01)
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train_ = optimizer_.minimize(y_)
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self.vars['x_'] = x_
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self.vars['y_'] = y_
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self.vars['w1_'] = w1_
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self.vars['w2_'] = w2_
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self.vars['w3_'] = w3_
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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.ops['train_'] = train_
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def fit(self, X_train, y_train, fit_epochs=500):
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self.history = self.model.fit(
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X_train, y_train, epochs=fit_epochs, verbose=0)
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# save model weights
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self.save_weights()
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if self.debug:
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mse = self.history.history['mean_squared_error']
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i = 0
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size = len(mse)
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while(i < size):
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LOG.info("Neural network training phase, epoch %d: mean_squared_error %f",
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i, mse[i])
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i += self.debug_interval
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LOG.info("Neural network training phase, epoch %d: mean_squared_error %f",
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size - 1, mse[size - 1])
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def predict(self, X_pred):
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return self.model.predict(X_pred)
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# Reference: Parameter Space Noise for Exploration.ICLR 2018, https://arxiv.org/abs/1706.01905
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def add_noise(self, weights):
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scale = self.adaptive_noise_scale()
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size = weights.shape[-1]
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noise = scale * np.random.normal(size=size)
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return weights + noise
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def adaptive_noise_scale(self):
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if self.recommend_iters > self.explore_iters:
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scale = self.noise_scale_end
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else:
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scale = self.noise_scale_begin - (self.noise_scale_begin - self.noise_scale_end) \
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* 1.0 * self.recommend_iters / self.explore_iters
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return scale
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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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w1, b1 = self.model.get_layer(index=0).get_weights()
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w2, b2 = self.model.get_layer(index=2).get_weights()
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w3, b3 = self.model.get_layer(index=3).get_weights()
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if explore is True:
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w1 = self.add_noise(w1)
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b1 = self.add_noise(b1)
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w2 = self.add_noise(w2)
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b2 = self.add_noise(b2)
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w3 = self.add_noise(w3)
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b3 = self.add_noise(b3)
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if self.debug:
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y_predict = self.predict(X_start)
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LOG.info("Recommend phase, y prediction: min %f, max %f, mean %f",
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np.min(y_predict), np.max(y_predict), np.mean(y_predict))
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with tf.Session(graph=self.graph) as sess:
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init = tf.global_variables_initializer()
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sess.run(init)
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assign_x_op = self.vars['x_'].assign(X_start)
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sess.run(assign_x_op)
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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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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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for i in range(recommend_epochs):
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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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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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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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res = NeuralNetResult(minl=y_recommend, minl_conf=X_recommend)
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if self.debug:
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LOG.info("Recommend phase, epoch %d, y after gradient descent: \
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min %f, max %f, mean %f", recommend_epochs, np.mean(y_recommend),
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np.max(y_recommend), np.mean(y_recommend))
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self.recommend_iters += 1
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return res
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