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30 lines
1.1 KiB
30 lines
1.1 KiB
# visualization.py
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# ----------------
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# Licensing Information: You are free to use or extend these projects for
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# educational purposes provided that (1) you do not distribute or publish
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# solutions, (2) you retain this notice, and (3) you provide clear
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# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
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#
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# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
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# The core projects and autograders were primarily created by John DeNero
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# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
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# Student side autograding was added by Brad Miller, Nick Hay, and
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# Pieter Abbeel (pabbeel@cs.berkeley.edu).
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import tensorflow as tf
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def variable_summaries(var, name):
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with tf.name_scope("summaries"):
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mean = tf.reduce_mean(var)
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tf.scalar_summary('mean/'+name,mean)
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with tf.name_scope('stddev'):
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stddev = tf.sqrt(tf.reduce_sum(tf.square(var-mean)))
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tf.scalar_summary('stddev/'+name,stddev)
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tf.scalar_summary('max/'+name,tf.reduce_max(var))
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tf.scalar_summary('min/'+name,tf.reduce_min(var))
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tf.histogram_summary(name,var)
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