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e7c1e6a8e3
Here's the command I used to invoke autopep8 (in parallel!):
git ls-files | grep '\.py$' | xargs -n1 -P`nproc` autopep8 -i
Several rules are ignored in setup.cfg. The goal is to let autopep8
handle everything which it can handle safely, and to disable any rules
which are tricky or controversial to address. We may want to come back
and re-enable some of these rules later, but I'm trying to make this
patch as safe as possible.
Also configures flake8 to match pep8's behavior.
Also configures TravisCI to check the whole project for lint.
42 lines
1.1 KiB
Python
42 lines
1.1 KiB
Python
import json
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import torch
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import torch.legacy.optim as optim
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from pprint import pprint
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def rosenbrock(tensor):
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x, y = tensor
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return (1 - x)**2 + 100 * (y - x**2)**2
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def drosenbrock(tensor):
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x, y = tensor
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return torch.DoubleTensor((-400 * x * (y - x**2) - 2 * (1 - x), 200 * x * (y - x**2)))
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algorithms = {
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'adadelta': optim.adadelta,
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'adagrad': optim.adagrad,
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'adam': optim.adam,
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'adamax': optim.adamax,
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'asgd': optim.asgd,
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'cg': optim.cg,
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'nag': optim.nag,
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'rmsprop': optim.rmsprop,
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'rprop': optim.rprop,
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'sgd': optim.sgd,
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'lbfgs': optim.lbfgs,
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}
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with open('tests.json', 'r') as f:
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tests = json.loads(f.read())
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for test in tests:
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print(test['algorithm'] + '\t')
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algorithm = algorithms[test['algorithm']]
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for config in test['config']:
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print('================================================================================\t')
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params = torch.DoubleTensor((1.5, 1.5))
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for i in range(100):
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algorithm(lambda x: (rosenbrock(x), drosenbrock(x)), params, config)
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print('{:.8f}\t{:.8f}\t'.format(params[0], params[1]))
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