143 lines
4.9 KiB
Python
143 lines
4.9 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for PowerSign."""
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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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import math
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.contrib.opt.python.training import powersign
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from tensorflow.contrib.opt.python.training import sign_decay
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from tensorflow.python.framework import constant_op
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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def py_linear_decay_fn(decay_steps):
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def linear_decay(step):
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step = min(step, decay_steps)
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return float(decay_steps - step) / decay_steps
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return linear_decay
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def powersign_update_numpy(params,
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g_t,
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m,
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lr,
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base=math.e,
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beta=0.9,
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py_sign_decay_fn=None,
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t=None):
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m_t = beta * m + (1 - beta) * g_t
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if py_sign_decay_fn is None:
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sign_decayed = 1.0
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else:
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sign_decayed = py_sign_decay_fn(t-1)
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multiplier = base ** (sign_decayed * np.sign(g_t) * np.sign(m_t))
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params_t = params - lr * multiplier * g_t
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return params_t, m_t
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class PowerSignTest(xla_test.XLATestCase):
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def _testDense(self,
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learning_rate=0.1,
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sign_decay_fn=None,
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py_sign_decay_fn=None,
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base=math.e,
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beta=0.9):
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for dtype in self.float_types:
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with self.test_session(), self.test_scope():
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# Initialize variables for numpy implementation.
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m0, m1 = 0.0, 0.0
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var0_np = np.array([1.0, 2.0], dtype=dtype)
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grads0_np = np.array([0.1, 0.1], dtype=dtype)
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var1_np = np.array([3.0, 4.0], dtype=dtype)
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grads1_np = np.array([0.01, 0.01], dtype=dtype)
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var0 = resource_variable_ops.ResourceVariable(var0_np)
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var1 = resource_variable_ops.ResourceVariable(var1_np)
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global_step = resource_variable_ops.ResourceVariable(0, trainable=False)
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grads0 = constant_op.constant(grads0_np)
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grads1 = constant_op.constant(grads1_np)
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opt = powersign.PowerSignOptimizer(
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learning_rate=learning_rate,
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base=base,
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beta=beta,
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sign_decay_fn=sign_decay_fn,
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)
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update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]),
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global_step=global_step)
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neg_update = opt.apply_gradients(zip([-grads0, -grads1], [var0, var1]),
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global_step=global_step)
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variables.global_variables_initializer().run()
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], var0.eval())
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self.assertAllClose([3.0, 4.0], var1.eval())
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# Run 7 steps of powersign
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# first 4 steps with positive gradient
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# last 3 steps with negative gradient (sign(gm) should be -1)
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for t in range(1, 8):
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if t < 5:
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update.run()
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else:
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neg_update.run()
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var0_np, m0 = powersign_update_numpy(
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var0_np,
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grads0_np if t < 5 else -grads0_np,
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m0,
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learning_rate,
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base=base,
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beta=beta,
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py_sign_decay_fn=py_sign_decay_fn,
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t=t,
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)
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var1_np, m1 = powersign_update_numpy(
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var1_np,
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grads1_np if t < 5 else -grads1_np,
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m1,
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learning_rate,
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base=base,
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beta=beta,
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py_sign_decay_fn=py_sign_decay_fn,
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t=t,
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)
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# Validate updated params
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self.assertAllCloseAccordingToType(var0_np, var0.eval())
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self.assertAllCloseAccordingToType(var1_np, var1.eval())
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def testDense(self):
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decay_steps = 10
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sign_decay_fn = sign_decay.get_linear_decay_fn(decay_steps)
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py_sign_decay_fn = py_linear_decay_fn(decay_steps)
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self._testDense()
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self._testDense(learning_rate=0.1, base=10.0, beta=0.8)
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self._testDense(
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sign_decay_fn=sign_decay_fn, py_sign_decay_fn=py_sign_decay_fn)
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if __name__ == '__main__':
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test.main()
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