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This code is highly based on https://github.com/carpedm20/deep-rl-tensorflow/blob/master/agents/statistic.py
'''
import tensorflow as tf
import numpy as np
import baselines.common.tf_util as U
class stats():
def __init__(self, scalar_keys=[], histogram_keys=[]):
self.scalar_keys = scalar_keys
self.histogram_keys = histogram_keys
self.scalar_summaries = []
self.scalar_summaries_ph = []
self.histogram_summaries_ph = []
self.histogram_summaries = []
with tf.compat.v1.variable_scope('summary'):
for k in scalar_keys:
ph = tf.compat.v1.placeholder('float32', None, name=k+'.scalar.summary')
sm = tf.compat.v1.summary.scalar(k+'.scalar.summary', ph)
self.scalar_summaries_ph.append(ph)
self.scalar_summaries.append(sm)
for k in histogram_keys:
ph = tf.compat.v1.placeholder('float32', None, name=k+'.histogram.summary')
sm = tf.compat.v1.summary.scalar(k+'.histogram.summary', ph)
self.histogram_summaries_ph.append(ph)
self.histogram_summaries.append(sm)
self.summaries = tf.compat.v1.summary.merge(self.scalar_summaries+self.histogram_summaries)
def add_all_summary(self, writer, values, iter):
# Note that the order of the incoming ```values``` should be the same as the that of the
# ```scalar_keys``` given in ```__init__```
if np.sum(np.isnan(values)+0) != 0:
return
sess = U.get_session()
keys = self.scalar_summaries_ph + self.histogram_summaries_ph
feed_dict = {}
for k, v in zip(keys, values):
feed_dict.update({k: v})
summaries_str = sess.run(self.summaries, feed_dict)
writer.add_summary(summaries_str, iter)
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