mirror of
https://github.com/wassname/ray.git
synced 2026-07-15 11:25:40 +08:00
Eric LiangandPhilipp Moritz
4374ad1453
* add tf metrics * comments * fix network scopes * add doc * initial work * try with 3 virtual cpus * clean up metrics * use format string * fix trace level * back to pong * always run summary on cpu * plot intermediate and final sgd stats * add back a global step * update * add timeline * use staging area and reuse weights properly * stage at cpu * whoops, stage only the batch * clean up a bit * fix py flake * wip * create an optimizer graph per device * print timeline on 5th batch instead * print examples per second * log placement for training ops * force placement on cpu:0 * try separating weights onto different gpus * try using nccl * add cpu fallback * remove space from date * check has gpu device * fix flag config * checkpoint * wip * update * add some timing * trace loading * try cpu * revert that * remove expensive test * lint * cleanups * clean up timers * clean it up a bit * fix code for non-scalar action spaces * address some nits * fix quotes * efficient shuffling between sgd epochs
17 lines
606 B
Python
17 lines
606 B
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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def vision_net(inputs, num_classes=10):
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with tf.name_scope("vision_net"):
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conv1 = slim.conv2d(inputs, 16, [8, 8], 4, scope="conv1")
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conv2 = slim.conv2d(conv1, 32, [4, 4], 2, scope="conv2")
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fc1 = slim.conv2d(conv2, 512, [10, 10], padding="VALID", scope="fc1")
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fc2 = slim.conv2d(fc1, num_classes, [1, 1], activation_fn=None,
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normalizer_fn=None, scope="fc2")
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return tf.squeeze(fc2, [1, 2])
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