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Diffstat (limited to 'tensorflow/compiler/tests/addsign_test.py')
-rw-r--r-- | tensorflow/compiler/tests/addsign_test.py | 142 |
1 files changed, 142 insertions, 0 deletions
diff --git a/tensorflow/compiler/tests/addsign_test.py b/tensorflow/compiler/tests/addsign_test.py new file mode 100644 index 0000000000..9ec5a964cb --- /dev/null +++ b/tensorflow/compiler/tests/addsign_test.py @@ -0,0 +1,142 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Tests for AddSign.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.compiler.tests import xla_test +from tensorflow.contrib.opt.python.training import addsign +from tensorflow.contrib.opt.python.training import sign_decay +from tensorflow.python.framework import constant_op +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + + +def py_linear_decay_fn(decay_steps): + def linear_decay(step): + step = min(step, decay_steps) + return float(decay_steps - step) / decay_steps + return linear_decay + + +def addsign_update_numpy(params, + g_t, + m, + lr, + alpha=1.0, + beta=0.9, + py_sign_decay_fn=None, + t=None): + m_t = beta * m + (1 - beta) * g_t + if py_sign_decay_fn is None: + sign_decayed = 1.0 + else: + sign_decayed = py_sign_decay_fn(t-1) + multiplier = alpha + sign_decayed * np.sign(g_t) * np.sign(m_t) + params_t = params - lr * multiplier * g_t + return params_t, m_t + + +class AddSignTest(xla_test.XLATestCase): + + def _testDense(self, + learning_rate=0.1, + sign_decay_fn=None, + py_sign_decay_fn=None, + alpha=1.0, + beta=0.9): + for dtype in self.float_types: + with self.test_session(), self.test_scope(): + # Initialize variables for numpy implementation. + m0, m1 = 0.0, 0.0 + var0_np = np.array([1.0, 2.0], dtype=dtype) + grads0_np = np.array([0.1, 0.1], dtype=dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype) + grads1_np = np.array([0.01, 0.01], dtype=dtype) + + var0 = resource_variable_ops.ResourceVariable(var0_np) + var1 = resource_variable_ops.ResourceVariable(var1_np) + global_step = resource_variable_ops.ResourceVariable(0, trainable=False) + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + + opt = addsign.AddSignOptimizer( + learning_rate=learning_rate, + alpha=alpha, + beta=beta, + sign_decay_fn=sign_decay_fn, + ) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]), + global_step=global_step) + neg_update = opt.apply_gradients(zip([-grads0, -grads1], [var0, var1]), + global_step=global_step) + variables.global_variables_initializer().run() + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + # Run 7 steps of AddSign + # first 4 steps with positive gradient + # last 3 steps with negative gradient (sign(gm) should be -1) + for t in range(1, 8): + if t < 5: + update.run() + else: + neg_update.run() + + var0_np, m0 = addsign_update_numpy( + var0_np, + grads0_np if t < 5 else -grads0_np, + m0, + learning_rate, + alpha=alpha, + beta=beta, + py_sign_decay_fn=py_sign_decay_fn, + t=t, + ) + var1_np, m1 = addsign_update_numpy( + var1_np, + grads1_np if t < 5 else -grads1_np, + m1, + learning_rate, + alpha=alpha, + beta=beta, + py_sign_decay_fn=py_sign_decay_fn, + t=t, + ) + + # Validate updated params + self.assertAllCloseAccordingToType( + var0_np, var0.eval(), half_rtol=1e-2) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + + def testDense(self): + decay_steps = 10 + sign_decay_fn = sign_decay.get_linear_decay_fn(decay_steps) + py_sign_decay_fn = py_linear_decay_fn(decay_steps) + self._testDense() + self._testDense(learning_rate=0.01, alpha=0.1, beta=0.8) + self._testDense( + sign_decay_fn=sign_decay_fn, py_sign_decay_fn=py_sign_decay_fn) + + +if __name__ == '__main__': + test.main() |