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104 lines
3.0 KiB
104 lines
3.0 KiB
# Copyright (c) 2018 PaddlePaddle 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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import unittest
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import numpy as np
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from op_test import OpTest
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class TestRmspropOp1(OpTest):
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''' Test RMSProp with explicit inputs
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'''
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def setUp(self):
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self.op_type = "rmsprop"
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param = np.random.random((123, 321)).astype("float32")
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mean_square = np.random.random((123, 321)).astype("float32")
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learning_rate = np.array([0.01]).astype("float32")
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grad = np.random.random((123, 321)).astype("float32")
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moment = np.zeros((123, 321)).astype("float32")
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epsilon = 1e-6
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decay = 0.9
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momentum = 0.0
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self.inputs = {
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'Param': param,
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'MeanSquare': mean_square,
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'LearningRate': learning_rate,
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'Grad': grad,
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'Moment': moment,
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}
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self.attrs = {'epsilon': epsilon, 'decay': decay, 'momentum': momentum}
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ms_out = decay * mean_square + (1 - decay) * grad * grad
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moment_out = momentum * moment + \
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learning_rate * grad / np.sqrt(ms_out + epsilon)
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param_out = param - moment_out
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self.outputs = {
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'ParamOut': param_out,
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'MomentOut': moment_out,
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'MeanSquareOut': ms_out
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}
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def test_check_output(self):
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self.check_output()
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class TestRmspropOp2(OpTest):
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'''Test RMSProp with default values for attributes
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'''
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def setUp(self):
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self.op_type = "rmsprop"
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param = np.random.random((123, 321)).astype("float32")
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mean_square = np.random.random((123, 321)).astype("float32")
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learning_rate = np.array([0.01]).astype("float32")
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grad = np.random.random((123, 321)).astype("float32")
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moment = np.zeros((123, 321)).astype("float32")
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epsilon = 1.0e-10
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decay = 0.9
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momentum = 0.0
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self.inputs = {
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'Param': param,
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'MeanSquare': mean_square,
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'LearningRate': learning_rate,
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'Grad': grad,
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'Moment': moment,
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}
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ms_out = decay * mean_square + (1 - decay) * grad * grad
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moment_out = momentum * moment + \
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learning_rate * grad / np.sqrt(ms_out + epsilon)
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param_out = param - moment_out
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self.outputs = {
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'ParamOut': param_out,
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'MomentOut': moment_out,
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'MeanSquareOut': ms_out
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}
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def test_check_output(self):
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self.check_output()
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if __name__ == "__main__":
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unittest.main()
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