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""" | |
Usage: | |
python convert.py xxx.rst xxx.py | |
To convert markdown to sphinx_gallery `.py`, use pandoc to generate a `.rst` text in advance: | |
pandoc xxx.md --output xxx.rst | |
python convert.py xxx.rst xxx.py |
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Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
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from __future__ import print_function | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from torch.autograd import Variable | |
def sample_gumbel(shape, eps=1e-20): | |
U = torch.rand(shape).cuda() | |
return -Variable(torch.log(-torch.log(U + eps) + eps)) |
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def sample_gumbel(shape, eps=1e-20): | |
"""Sample from Gumbel(0, 1)""" | |
U = tf.random_uniform(shape,minval=0,maxval=1) | |
return -tf.log(-tf.log(U + eps) + eps) | |
def gumbel_softmax_sample(logits, temperature): | |
""" Draw a sample from the Gumbel-Softmax distribution""" | |
y = logits + sample_gumbel(tf.shape(logits)) | |
return tf.nn.softmax( y / temperature) |
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