mirror of
https://github.com/vale981/ray
synced 2025-03-10 13:26:39 -04:00
555dcf35a2
* add policy gradient example * fix typos * Minor changes plus some documentation. * Minor fixes.
26 lines
1.1 KiB
Python
26 lines
1.1 KiB
Python
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 tensorflow.contrib.slim as slim
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import numpy as np
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def normc_initializer(std=1.0):
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def _initializer(shape, dtype=None, partition_info=None): #pylint: disable=W0613
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out = np.random.randn(*shape).astype(np.float32)
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out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True))
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return tf.constant(out)
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return _initializer
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def fc_net(inputs, num_classes=10, logstd=False):
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fc1 = slim.fully_connected(inputs, 128, weights_initializer=normc_initializer(1.0), scope="fc1")
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fc2 = slim.fully_connected(fc1, 128, weights_initializer=normc_initializer(1.0), scope="fc2")
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fc3 = slim.fully_connected(fc2, 128, weights_initializer=normc_initializer(1.0), scope="fc3")
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fc4 = slim.fully_connected(fc3, num_classes, weights_initializer=normc_initializer(0.01), activation_fn=None, scope="fc4")
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if logstd:
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logstd = tf.get_variable(name="logstd", shape=[num_classes], initializer=tf.zeros_initializer)
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return tf.concat(1, [fc4, logstd])
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else:
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return fc4
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