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https://github.com/zebrajr/pytorch.git
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da4f3629c5
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/21305 Differential Revision: D15613078 Pulled By: ezyang fbshipit-source-id: 1fedf4368d65db406b617a51402ee8a20968aff7
46 lines
1.5 KiB
Python
46 lines
1.5 KiB
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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from __future__ import unicode_literals
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from caffe2.python import core
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from hypothesis import given
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import caffe2.python.hypothesis_test_util as hu
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import hypothesis.strategies as st
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import numpy as np
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class TestLars(hu.HypothesisTestCase):
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@given(offset=st.floats(min_value=0, max_value=100),
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lr_min=st.floats(min_value=1e-8, max_value=1e-6),
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**hu.gcs)
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def test_lars(self, offset, lr_min, dc, gc):
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X = np.random.rand(6, 7, 8, 9).astype(np.float32)
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dX = np.random.rand(6, 7, 8, 9).astype(np.float32)
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wd = np.array([1e-4]).astype(np.float32)
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trust = np.random.rand(1).astype(np.float32)
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lr_max = np.random.rand(1).astype(np.float32)
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def ref_lars(X, dX, wd, trust, lr_max):
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rescale_factor = \
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trust / (np.linalg.norm(dX) / np.linalg.norm(X) + wd + offset)
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rescale_factor = np.minimum(rescale_factor, lr_max)
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rescale_factor = np.maximum(rescale_factor, lr_min)
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return [rescale_factor]
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op = core.CreateOperator(
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"Lars",
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["X", "dX", "wd", "trust", "lr_max"],
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["rescale_factor"],
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offset=offset,
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lr_min=lr_min,
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X, dX, wd, trust, lr_max],
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reference=ref_lars
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)
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