February 2018
Intermediate to advanced
262 pages
6h 59m
English
Content preview from Deep Learning with PyTorch
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DenseLayer
One good way to learn how a particular network works is to look at the source code. PyTorch has a very clean implementation and most of the time is easily readable. Let's look at the DenseLayer implementation:
class _DenseLayer(nn.Sequential): def __init__(self, num_input_features, growth_rate, bn_size, drop_rate): super(_DenseLayer, self).__init__() self.add_module('norm.1', nn.BatchNorm2d(num_input_features)), self.add_module('relu.1', nn.ReLU(inplace=True)), self.add_module('conv.1', nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)), self.add_module('norm.2', nn.BatchNorm2d(bn_size * growth_rate)), self.add_module('relu.2', nn.ReLU(inplace=True)), self.add_module('conv.2', nn.Conv2d(bn_size ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.