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# Copyright 2017 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Basic data management and plotting utilities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import cPickle as pickle
import getpass
import numpy as np
import gc
import tensorflow as tf
#
# Python utlities
#
def exp_moving_average(x, alpha=0.9):
res = []
mu = 0
alpha_factor = 1
for x_i in x:
mu += (1 - alpha)*(x_i - mu)
alpha_factor *= alpha
res.append(mu/(1 - alpha_factor))
return np.array(res)
def sanitize(s):
return s.replace('.', '_')
#
# Tensorflow utilities
#
def softplus(x):
'''
Let m = max(0, x), then,
sofplus(x) = log(1 + e(x)) = log(e(0) + e(x)) = log(e(m)(e(-m) + e(x-m)))
= m + log(e(-m) + e(x - m))
The term inside of the log is guaranteed to be between 1 and 2.
'''
m = tf.maximum(tf.zeros_like(x), x)
return m + tf.log(tf.exp(-m) + tf.exp(x - m))
def safe_log_prob(x, eps=1e-8):
return tf.log(tf.clip_by_value(x, eps, 1.0))
def rms(x):
return tf.sqrt(tf.reduce_mean(tf.square(x)))
def center(x):
mu = (tf.reduce_sum(x) - x)/tf.to_float(tf.shape(x)[0] - 1)
return x - mu
def vectorize(grads_and_vars, set_none_to_zero=False, skip_none=False):
if set_none_to_zero:
return tf.concat([tf.reshape(g, [-1]) if g is not None else
tf.reshape(tf.zeros_like(v), [-1]) for g, v in grads_and_vars], 0)
elif skip_none:
return tf.concat([tf.reshape(g, [-1]) for g, v in grads_and_vars if g is not None], 0)
else:
return tf.concat([tf.reshape(g, [-1]) for g, v in grads_and_vars], 0)
def add_grads_and_vars(a, b):
'''Add grads_and_vars from two calls to tf.compute_gradients.'''
res = []
for (g_a, v_a), (g_b, v_b) in zip(a, b):
assert v_a == v_b
if g_a is None:
res.append((g_b, v_b))
elif g_b is None:
res.append((g_a, v_a))
else:
res.append((g_a + g_b, v_a))
return res
def binary_log_likelihood(y, log_y_hat):
"""Computes binary log likelihood.
Args:
y: observed data
log_y_hat: parameters of the binary variables
Returns:
log_likelihood
"""
return tf.reduce_sum(y*(-softplus(-log_y_hat)) +
(1 - y)*(-log_y_hat-softplus(-log_y_hat)),
1)
def cov(a, b):
"""Compute the sample covariance between two vectors."""
mu_a = tf.reduce_mean(a)
mu_b = tf.reduce_mean(b)
n = tf.to_float(tf.shape(a)[0])
return tf.reduce_sum((a - mu_a)*(b - mu_b))/(n - 1.0)
def corr(a, b):
return cov(a, b)*tf.rsqrt(cov(a, a))*tf.rsqrt(cov(b, b))
def logSumExp(t, axis=0, keep_dims = False):
'''Computes the log(sum(exp(t))) numerically stabily.
Args:
t: input tensor
axis: which axis to sum over
keep_dims: whether to keep the dim or not
Returns:
tensor with result
'''
m = tf.reduce_max(t, [axis])
res = m + tf.log(tf.reduce_sum(tf.exp(t - tf.expand_dims(m, axis)), [axis]))
if keep_dims:
return tf.expand_dims(res, axis)
else:
return res
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