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110 lines
3.4 KiB
110 lines
3.4 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, randomize_probability
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class TestCrossEntropyOp1(OpTest):
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"""Test cross-entropy with discrete one-hot labels.
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"""
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def setUp(self):
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self.op_type = "cross_entropy"
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batch_size = 30
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class_num = 10
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X = randomize_probability(batch_size, class_num, dtype='float64')
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label = np.random.randint(0, class_num, (batch_size, 1), dtype="int64")
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cross_entropy = np.asmatrix(
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[[-np.log(X[i][label[i][0]])] for i in range(X.shape[0])],
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dtype="float64")
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self.inputs = {"X": X, "Label": label}
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self.outputs = {"Y": cross_entropy}
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self.attrs = {"soft_label": False}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(["X"], "Y", numeric_grad_delta=0.001)
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class TestCrossEntropyOp2(OpTest):
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"""Test cross-entropy with vectorized soft labels.
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"""
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def setUp(self):
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self.op_type = "cross_entropy"
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batch_size = 5
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class_num = 37
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X = randomize_probability(batch_size, class_num)
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label = np.random.uniform(0.1, 1.0,
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[batch_size, class_num]).astype("float32")
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label /= label.sum(axis=1, keepdims=True)
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cross_entropy = (-label * np.log(X)).sum(
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axis=1, keepdims=True).astype("float32")
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self.inputs = {"X": X, "Label": label}
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self.outputs = {"Y": cross_entropy}
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self.attrs = {"soft_label": True}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(
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["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
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class TestCrossEntropyOp3(OpTest):
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"""Test cross-entropy with vectorized one-hot representation of labels.
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"""
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def setUp(self):
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self.op_type = "cross_entropy"
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batch_size = 5
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class_num = 17
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X = randomize_probability(batch_size, class_num)
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label_index = np.random.randint(
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0, class_num, (batch_size), dtype="int32")
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label = np.zeros(X.shape)
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label[np.arange(batch_size), label_index] = 1
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cross_entropy = np.asmatrix(
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[[-np.log(X[i][label_index[i]])] for i in range(X.shape[0])],
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dtype="float32")
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cross_entropy2 = (-label * np.log(X)).sum(
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axis=1, keepdims=True).astype("float32")
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self.inputs = {"X": X, "Label": label.astype(np.float32)}
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self.outputs = {"Y": cross_entropy}
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self.attrs = {"soft_label": True}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(
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["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
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if __name__ == "__main__":
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unittest.main()
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