mirror of
https://github.com/vale981/ray
synced 2025-03-10 05:16:49 -04:00
59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
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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 tensorflow as tf
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import numpy as np
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class Categorical(object):
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def __init__(self, logits):
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self.logits = logits
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def logp(self, x):
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return -tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits, labels=x)
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def entropy(self):
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a0 = self.logits - tf.reduce_max(self.logits, reduction_indices=[1], keep_dims=True)
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ea0 = tf.exp(a0)
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z0 = tf.reduce_sum(ea0, reduction_indices=[1], keep_dims=True)
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p0 = ea0 / z0
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return tf.reduce_sum(p0 * (tf.log(z0) - a0), reduction_indices=[1])
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def kl(self, other):
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a0 = self.logits - tf.reduce_max(self.logits, reduction_indices=[1], keep_dims=True)
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a1 = other.logits - tf.reduce_max(other.logits, reduction_indices=[1], keep_dims=True)
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ea0 = tf.exp(a0)
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ea1 = tf.exp(a1)
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z0 = tf.reduce_sum(ea0, reduction_indices=[1], keep_dims=True)
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z1 = tf.reduce_sum(ea1, reduction_indices=[1], keep_dims=True)
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p0 = ea0 / z0
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return tf.reduce_sum(p0 * (a0 - tf.log(z0) - a1 + tf.log(z1)), reduction_indices=[1])
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def sample(self):
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return tf.multinomial(self.logits, 1)
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class DiagGaussian(object):
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def __init__(self, flat):
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self.flat = flat
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mean, logstd = tf.split(1, 2, flat)
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self.mean = mean
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self.logstd = logstd
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self.std = tf.exp(logstd)
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def logp(self, x):
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return - 0.5 * tf.reduce_sum(tf.square((x - self.mean) / self.std), reduction_indices=[1]) \
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- 0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(x)[1]) \
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- tf.reduce_sum(self.logstd, reduction_indices=[1])
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def kl(self, other):
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assert isinstance(other, DiagGaussian)
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return tf.reduce_sum(other.logstd - self.logstd + (tf.square(self.std) + tf.square(self.mean - other.mean)) / (2.0 * tf.square(other.std)) - 0.5, reduction_indices=[1])
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def entropy(self):
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return tf.reduce_sum(self.logstd + .5 * np.log(2.0 * np.pi * np.e), reduction_indices=[1])
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def sample(self):
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return self.mean + self.std * tf.random_normal(tf.shape(self.mean))
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