124 lines
4.2 KiB
124 lines
4.2 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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from __future__ import print_function
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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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from paddle import fluid
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from paddle.fluid.layers import lstm_unit
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from paddle.fluid.framework import program_guard, Program
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def sigmoid_np(x):
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return 1. / (1. + np.exp(-x))
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def tanh_np(x):
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return 2 * sigmoid_np(2. * x) - 1.
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class LstmUnitTestError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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batch_size, dict_dim, emb_dim, hidden_dim = 32, 128, 64, 512
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data = fluid.data(
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name='step_data', shape=[batch_size], dtype='int64')
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inputs = fluid.embedding(input=data, size=[dict_dim, emb_dim])
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pre_hidden = fluid.data(
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name='pre_hidden',
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shape=[batch_size, hidden_dim],
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dtype='float32')
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pre_cell = fluid.data(
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name='pre_cell',
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shape=[batch_size, hidden_dim],
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dtype='float32')
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np_input = np.random.uniform(
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-0.1, 0.1, (batch_size, emb_dim)).astype('float64')
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np_pre_hidden = np.random.uniform(
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-0.1, 0.1, (batch_size, hidden_dim)).astype('float64')
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np_pre_cell = np.random.uniform(
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-0.1, 0.1, (batch_size, hidden_dim)).astype('float64')
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def test_input_Variable():
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lstm_unit(np_input, pre_hidden, pre_cell)
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self.assertRaises(TypeError, test_input_Variable)
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def test_pre_hidden_Variable():
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lstm_unit(inputs, np_pre_hidden, pre_cell)
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self.assertRaises(TypeError, test_pre_hidden_Variable)
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def test_pre_cell_Variable():
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lstm_unit(inputs, pre_hidden, np_pre_cell)
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self.assertRaises(TypeError, test_pre_cell_Variable)
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def test_input_type():
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error_input = fluid.data(
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name='error_input',
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shape=[batch_size, emb_dim],
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dtype='int32')
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lstm_unit(error_input, pre_hidden, pre_cell)
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self.assertRaises(TypeError, test_input_type)
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def test_pre_hidden_type():
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error_pre_hidden = fluid.data(
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name='error_pre_hidden',
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shape=[batch_size, hidden_dim],
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dtype='int32')
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lstm_unit(inputs, error_pre_hidden, pre_cell)
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self.assertRaises(TypeError, test_pre_hidden_type)
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def test_pre_cell_type():
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error_pre_cell = fluid.data(
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name='error_pre_cell',
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shape=[batch_size, hidden_dim],
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dtype='int32')
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lstm_unit(inputs, pre_hidden, error_pre_cell)
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self.assertRaises(TypeError, test_pre_cell_type)
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class LstmUnitTest(OpTest):
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def setUp(self):
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self.op_type = "lstm_unit"
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x_np = np.random.normal(size=(15, 160)).astype("float64")
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c_np = np.random.normal(size=(15, 40)).astype("float64")
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i_np, f_np, o_np, j_np = np.split(x_np, 4, axis=1)
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forget_bias_np = 0.
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self.attrs = {'forget_bias': 0.}
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new_c = c_np * sigmoid_np(f_np + forget_bias_np) + sigmoid_np(
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i_np) * tanh_np(j_np)
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new_h = tanh_np(new_c) * sigmoid_np(o_np)
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self.inputs = {'X': x_np, 'C_prev': c_np}
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self.outputs = {'C': new_c, 'H': new_h}
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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', 'C_prev'], ['C', 'H'])
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if __name__ == "__main__":
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unittest.main()
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