在linux，尤其是无root权限的服务器上安装brew是个挑战。
python_tutorial
发表于
tuple
- a = (5) type(a) =
的问题
关于这个问题有相关的讨论。
一个例子是如果(3+4)*3, 结果把括号中当作tuple来看，岂不是(7,7,7)，这显然不是我们想要的。
其更直接的解读是
it is the commas, not the parentheses, that define the tuple.
我们做一个测试1
2
3>>> a = 1,2,
>>> type(a)
<class 'tuple'>
Conference-Workshop-Following-List
发表于
|
更新于
HPCaML
International Workshop on the Intersection of High Performance Computing and Machine Learning
HPCaML 2019
TensorTuner Nelder-Mead (kind of gradient-free optimizer).Find better parameter values, based on configuration space(hyper parameters, input data, hardware, software,etc).
NUMA-CaffeC4ML(Compilers for Machine Learning)
OMASE Workshop (Optimization, Modeling, Analysis and Space Exploration)
arg_parse_tutorial
发表于
Python
argpase module
Convert a PyTorch model to Tensorflow using ONNX1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32import argparse
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
# 这相当于在程序中人工指定一些args的取值
# Train this model with 60 epochs and after process every 300 batches log the train status
args = parser.parse_args(['--epochs', '60', '--log-interval', '300'])
...
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, **kwargs)
gflags
basic_usage1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26#!/usr/bin/env python
"""Example for basic usage of the flags library."""
from google.apputils import app
import gflags
FLAGS = gflags.FLAGS
# Flag names are globally defined! So in general, we need to be
# careful to pick names that are unlikely to be used by other libraries.
# If there is a conflict, we'll get an error at import time.
gflags.DEFINE_string('name', 'Mr. President', 'your name')
gflags.DEFINE_integer('age', None, 'your age in years', lower_bound=0)
gflags.DEFINE_boolean('debug', False, 'produces debugging output')
gflags.DEFINE_enum('job', 'running', ['running', 'stopped'], 'job status')
def main(argv):
if FLAGS.debug:
print 'non-flag arguments:', argv
print 'Happy Birthday', FLAGS.name
if FLAGS.age is not None:
print 'You are %d years old, and your job is %s' % (FLAGS.age, FLAGS.job)
if __name__ == '__main__':
app.run()