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Robert Nishihara
3c5375345f
* Initial commit of evolution strategies example. * Some small simplifications. * Update example to use new API. * Add example to documentation.
87 lines
2.3 KiB
Python
87 lines
2.3 KiB
Python
# Code in this file is copied and adapted from
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# https://github.com/openai/evolution-strategies-starter.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import tensorflow as tf
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def compute_ranks(x):
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"""Returns ranks in [0, len(x))
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Note: This is different from scipy.stats.rankdata, which returns ranks in
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[1, len(x)].
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"""
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assert x.ndim == 1
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ranks = np.empty(len(x), dtype=int)
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ranks[x.argsort()] = np.arange(len(x))
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return ranks
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def compute_centered_ranks(x):
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y = compute_ranks(x.ravel()).reshape(x.shape).astype(np.float32)
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y /= (x.size - 1)
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y -= 0.5
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return y
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def make_session(single_threaded):
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if not single_threaded:
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return tf.InteractiveSession()
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return tf.InteractiveSession(
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config=tf.ConfigProto(inter_op_parallelism_threads=1,
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intra_op_parallelism_threads=1))
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def itergroups(items, group_size):
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assert group_size >= 1
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group = []
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for x in items:
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group.append(x)
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if len(group) == group_size:
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yield tuple(group)
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del group[:]
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if group:
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yield tuple(group)
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def batched_weighted_sum(weights, vecs, batch_size):
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total = 0
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num_items_summed = 0
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for batch_weights, batch_vecs in zip(itergroups(weights, batch_size),
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itergroups(vecs, batch_size)):
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assert len(batch_weights) == len(batch_vecs) <= batch_size
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total += np.dot(np.asarray(batch_weights, dtype=np.float32),
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np.asarray(batch_vecs, dtype=np.float32))
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num_items_summed += len(batch_weights)
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return total, num_items_summed
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class RunningStat(object):
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def __init__(self, shape, eps):
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self.sum = np.zeros(shape, dtype=np.float32)
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self.sumsq = np.full(shape, eps, dtype=np.float32)
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self.count = eps
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def increment(self, s, ssq, c):
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self.sum += s
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self.sumsq += ssq
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self.count += c
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@property
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def mean(self):
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return self.sum / self.count
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@property
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def std(self):
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return np.sqrt(np.maximum(self.sumsq / self.count - np.square(self.mean),
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1e-2))
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def set_from_init(self, init_mean, init_std, init_count):
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self.sum[:] = init_mean * init_count
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self.sumsq[:] = (np.square(init_mean) + np.square(init_std)) * init_count
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self.count = init_count
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