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111 lines
3.5 KiB
111 lines
3.5 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 TestAdadeltaOp1(OpTest):
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def setUp(self):
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self.op_type = "adadelta"
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param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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# The squared gradient is positive
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avg_squared_grad = np.random.random((102, 105)).astype("float32")
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# The squared update is positive
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avg_squared_update = np.random.random((102, 105)).astype("float32")
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rho = 0.95
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epsilon = 1e-6
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self.inputs = {
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'Param': param,
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'Grad': grad,
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'AvgSquaredGrad': avg_squared_grad,
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'AvgSquaredUpdate': avg_squared_update
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}
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self.attrs = {'rho': rho, 'epsilon': epsilon}
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avg_squared_grad_out = rho * avg_squared_grad + \
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(1 - rho) * np.square(grad)
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update = -np.multiply(
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np.sqrt(
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np.divide(avg_squared_update + epsilon, avg_squared_grad_out +
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epsilon)), grad)
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avg_squared_update_out = rho * avg_squared_update + \
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(1 - rho) * np.square(update)
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param_out = param + update
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self.outputs = {
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'ParamOut': param_out,
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'AvgSquaredGradOut': avg_squared_grad_out,
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'AvgSquaredUpdateOut': avg_squared_update_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 TestAdadeltaOp2(OpTest):
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'''Test Adadelta op with default attribute values
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'''
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def setUp(self):
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self.op_type = "adadelta"
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param = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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grad = np.random.uniform(-1, 1, (102, 105)).astype("float32")
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# The squared gradient is positive
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avg_squared_grad = np.random.random((102, 105)).astype("float32")
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# The squared update is positive
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avg_squared_update = np.random.random((102, 105)).astype("float32")
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rho = 0.95
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epsilon = 1e-6
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self.inputs = {
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'Param': param,
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'Grad': grad,
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'AvgSquaredGrad': avg_squared_grad,
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'AvgSquaredUpdate': avg_squared_update
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}
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avg_squared_grad_out = rho * avg_squared_grad + \
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(1 - rho) * np.square(grad)
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update = -np.multiply(
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np.sqrt(
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np.divide(avg_squared_update + epsilon, avg_squared_grad_out +
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epsilon)), grad)
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avg_squared_update_out = rho * avg_squared_update + \
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(1 - rho) * np.square(update)
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param_out = param + update
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self.outputs = {
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'ParamOut': param_out,
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'AvgSquaredGradOut': avg_squared_grad_out,
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'AvgSquaredUpdateOut': avg_squared_update_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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