621 lines
23 KiB
621 lines
23 KiB
# Copyright (c) 2019 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
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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import paddle.fluid.core as core
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from paddle.fluid.framework import program_guard, Program
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from paddle.fluid.executor import Executor
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from paddle.fluid import framework
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from paddle.fluid.layers.rnn import LSTMCell, GRUCell, RNNCell
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from paddle.fluid.layers import rnn as dynamic_rnn
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from paddle.fluid import contrib
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from paddle.fluid.contrib.layers import basic_lstm
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import paddle.fluid.layers.utils as utils
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import numpy as np
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class TestLSTMCellError(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, input_size, hidden_size = 4, 16, 16
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inputs = fluid.data(
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name='inputs', shape=[None, input_size], dtype='float32')
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pre_hidden = fluid.data(
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name='pre_hidden', shape=[None, hidden_size], dtype='float32')
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pre_cell = fluid.data(
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name='pre_cell', shape=[None, hidden_size], dtype='float32')
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cell = LSTMCell(hidden_size)
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def test_input_Variable():
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np_input = np.random.random(
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(batch_size, input_size)).astype("float32")
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cell(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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np_pre_hidden = np.random.random(
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(batch_size, hidden_size)).astype("float32")
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cell(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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np_pre_cell = np.random.random(
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(batch_size, input_size)).astype("float32")
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cell(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_inputs = fluid.data(
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name='error_inputs',
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shape=[None, input_size],
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dtype='int32')
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cell(error_inputs, [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=[None, hidden_size],
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dtype='int32')
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cell(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=[None, hidden_size],
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dtype='int32')
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cell(inputs, [pre_hidden, error_pre_cell])
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self.assertRaises(TypeError, test_pre_cell_type)
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def test_dtype():
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# the input type must be Variable
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LSTMCell(hidden_size, dtype="int32")
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self.assertRaises(TypeError, test_dtype)
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class TestLSTMCell(unittest.TestCase):
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def setUp(self):
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self.batch_size = 4
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self.input_size = 16
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self.hidden_size = 16
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def test_run(self):
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inputs = fluid.data(
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name='inputs', shape=[None, self.input_size], dtype='float32')
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pre_hidden = fluid.data(
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name='pre_hidden', shape=[None, self.hidden_size], dtype='float32')
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pre_cell = fluid.data(
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name='pre_cell', shape=[None, self.hidden_size], dtype='float32')
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cell = LSTMCell(self.hidden_size)
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lstm_hidden_new, lstm_states_new = cell(inputs, [pre_hidden, pre_cell])
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lstm_unit = contrib.layers.rnn_impl.BasicLSTMUnit(
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"basicLSTM", self.hidden_size, None, None, None, None, 1.0,
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"float32")
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lstm_hidden, lstm_cell = lstm_unit(inputs, pre_hidden, pre_cell)
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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else:
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place = core.CPUPlace()
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exe = Executor(place)
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exe.run(framework.default_startup_program())
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inputs_np = np.random.uniform(
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-0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
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pre_hidden_np = np.random.uniform(
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-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
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pre_cell_np = np.random.uniform(
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-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
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param_names = [[
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"LSTMCell/BasicLSTMUnit_0.w_0", "basicLSTM/BasicLSTMUnit_0.w_0"
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], ["LSTMCell/BasicLSTMUnit_0.b_0", "basicLSTM/BasicLSTMUnit_0.b_0"]]
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for names in param_names:
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param = np.array(fluid.global_scope().find_var(names[0]).get_tensor(
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))
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param = np.random.uniform(
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-0.1, 0.1, size=param.shape).astype('float32')
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fluid.global_scope().find_var(names[0]).get_tensor().set(param,
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place)
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fluid.global_scope().find_var(names[1]).get_tensor().set(param,
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place)
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out = exe.run(feed={
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'inputs': inputs_np,
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'pre_hidden': pre_hidden_np,
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'pre_cell': pre_cell_np
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},
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fetch_list=[lstm_hidden_new, lstm_hidden])
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self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4, atol=0))
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class TestGRUCellError(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, input_size, hidden_size = 4, 16, 16
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inputs = fluid.data(
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name='inputs', shape=[None, input_size], dtype='float32')
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pre_hidden = layers.data(
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name='pre_hidden',
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shape=[None, hidden_size],
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append_batch_size=False,
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dtype='float32')
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cell = GRUCell(hidden_size)
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def test_input_Variable():
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np_input = np.random.random(
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(batch_size, input_size)).astype("float32")
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cell(np_input, pre_hidden)
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self.assertRaises(TypeError, test_input_Variable)
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def test_pre_hidden_Variable():
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np_pre_hidden = np.random.random(
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(batch_size, hidden_size)).astype("float32")
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cell(inputs, np_pre_hidden)
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self.assertRaises(TypeError, test_pre_hidden_Variable)
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def test_input_type():
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error_inputs = fluid.data(
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name='error_inputs',
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shape=[None, input_size],
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dtype='int32')
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cell(error_inputs, pre_hidden)
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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=[None, hidden_size],
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dtype='int32')
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cell(inputs, error_pre_hidden)
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self.assertRaises(TypeError, test_pre_hidden_type)
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def test_dtype():
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# the input type must be Variable
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GRUCell(hidden_size, dtype="int32")
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self.assertRaises(TypeError, test_dtype)
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class TestGRUCell(unittest.TestCase):
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def setUp(self):
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self.batch_size = 4
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self.input_size = 16
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self.hidden_size = 16
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def test_run(self):
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inputs = fluid.data(
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name='inputs', shape=[None, self.input_size], dtype='float32')
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pre_hidden = layers.data(
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name='pre_hidden',
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shape=[None, self.hidden_size],
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append_batch_size=False,
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dtype='float32')
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cell = GRUCell(self.hidden_size)
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gru_hidden_new, _ = cell(inputs, pre_hidden)
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gru_unit = contrib.layers.rnn_impl.BasicGRUUnit(
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"basicGRU", self.hidden_size, None, None, None, None, "float32")
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gru_hidden = gru_unit(inputs, pre_hidden)
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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else:
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place = core.CPUPlace()
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exe = Executor(place)
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exe.run(framework.default_startup_program())
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inputs_np = np.random.uniform(
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-0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
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pre_hidden_np = np.random.uniform(
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-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
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param_names = [
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["GRUCell/BasicGRUUnit_0.w_0", "basicGRU/BasicGRUUnit_0.w_0"],
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["GRUCell/BasicGRUUnit_0.w_1", "basicGRU/BasicGRUUnit_0.w_1"],
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["GRUCell/BasicGRUUnit_0.b_0", "basicGRU/BasicGRUUnit_0.b_0"],
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["GRUCell/BasicGRUUnit_0.b_1", "basicGRU/BasicGRUUnit_0.b_1"]
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]
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for names in param_names:
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param = np.array(fluid.global_scope().find_var(names[0]).get_tensor(
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))
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param = np.random.uniform(
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-0.1, 0.1, size=param.shape).astype('float32')
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fluid.global_scope().find_var(names[0]).get_tensor().set(param,
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place)
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fluid.global_scope().find_var(names[1]).get_tensor().set(param,
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place)
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out = exe.run(feed={'inputs': inputs_np,
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'pre_hidden': pre_hidden_np},
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fetch_list=[gru_hidden_new, gru_hidden])
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self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4, atol=0))
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class TestRnnError(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 = 4
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input_size = 16
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hidden_size = 16
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seq_len = 4
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inputs = fluid.data(
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name='inputs', shape=[None, input_size], dtype='float32')
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pre_hidden = layers.data(
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name='pre_hidden',
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shape=[None, hidden_size],
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append_batch_size=False,
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dtype='float32')
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inputs_basic_lstm = fluid.data(
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name='inputs_basic_lstm',
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shape=[None, None, input_size],
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dtype='float32')
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sequence_length = fluid.data(
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name="sequence_length", shape=[None], dtype='int64')
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inputs_dynamic_rnn = layers.transpose(
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inputs_basic_lstm, perm=[1, 0, 2])
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cell = LSTMCell(hidden_size, name="LSTMCell_for_rnn")
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np_inputs_dynamic_rnn = np.random.random(
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(seq_len, batch_size, input_size)).astype("float32")
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def test_input_Variable():
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dynamic_rnn(
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cell=cell,
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inputs=np_inputs_dynamic_rnn,
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sequence_length=sequence_length,
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is_reverse=False)
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self.assertRaises(TypeError, test_input_Variable)
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def test_input_list():
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dynamic_rnn(
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cell=cell,
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inputs=[np_inputs_dynamic_rnn],
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sequence_length=sequence_length,
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is_reverse=False)
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self.assertRaises(TypeError, test_input_list)
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def test_initial_states_type():
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cell = GRUCell(hidden_size, name="GRUCell_for_rnn")
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error_initial_states = np.random.random(
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(batch_size, hidden_size)).astype("float32")
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dynamic_rnn(
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cell=cell,
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inputs=inputs_dynamic_rnn,
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initial_states=error_initial_states,
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sequence_length=sequence_length,
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is_reverse=False)
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self.assertRaises(TypeError, test_initial_states_type)
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def test_initial_states_list():
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error_initial_states = [
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np.random.random(
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(batch_size, hidden_size)).astype("float32"),
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np.random.random(
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(batch_size, hidden_size)).astype("float32")
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]
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dynamic_rnn(
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cell=cell,
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inputs=inputs_dynamic_rnn,
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initial_states=error_initial_states,
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sequence_length=sequence_length,
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is_reverse=False)
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self.assertRaises(TypeError, test_initial_states_type)
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def test_sequence_length_type():
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np_sequence_length = np.random.random(
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(batch_size)).astype("float32")
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dynamic_rnn(
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cell=cell,
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inputs=inputs_dynamic_rnn,
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sequence_length=np_sequence_length,
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is_reverse=False)
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self.assertRaises(TypeError, test_sequence_length_type)
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class TestRnn(unittest.TestCase):
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def setUp(self):
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self.batch_size = 4
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self.input_size = 16
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self.hidden_size = 16
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self.seq_len = 4
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def test_run(self):
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inputs_basic_lstm = fluid.data(
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name='inputs_basic_lstm',
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shape=[None, None, self.input_size],
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dtype='float32')
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sequence_length = fluid.data(
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name="sequence_length", shape=[None], dtype='int64')
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inputs_dynamic_rnn = layers.transpose(inputs_basic_lstm, perm=[1, 0, 2])
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cell = LSTMCell(self.hidden_size, name="LSTMCell_for_rnn")
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output, final_state = dynamic_rnn(
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cell=cell,
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inputs=inputs_dynamic_rnn,
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sequence_length=sequence_length,
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is_reverse=False)
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output_new = layers.transpose(output, perm=[1, 0, 2])
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rnn_out, last_hidden, last_cell = basic_lstm(inputs_basic_lstm, None, None, self.hidden_size, num_layers=1, \
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batch_first = False, bidirectional=False, sequence_length=sequence_length, forget_bias = 1.0)
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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else:
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place = core.CPUPlace()
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exe = Executor(place)
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exe.run(framework.default_startup_program())
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inputs_basic_lstm_np = np.random.uniform(
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-0.1, 0.1,
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(self.seq_len, self.batch_size, self.input_size)).astype('float32')
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sequence_length_np = np.ones(
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self.batch_size, dtype='int64') * self.seq_len
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inputs_np = np.random.uniform(
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-0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
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pre_hidden_np = np.random.uniform(
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-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
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pre_cell_np = np.random.uniform(
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-0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
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param_names = [[
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"LSTMCell_for_rnn/BasicLSTMUnit_0.w_0",
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"basic_lstm_layers_0/BasicLSTMUnit_0.w_0"
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], [
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"LSTMCell_for_rnn/BasicLSTMUnit_0.b_0",
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"basic_lstm_layers_0/BasicLSTMUnit_0.b_0"
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]]
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for names in param_names:
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param = np.array(fluid.global_scope().find_var(names[0]).get_tensor(
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))
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param = np.random.uniform(
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-0.1, 0.1, size=param.shape).astype('float32')
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fluid.global_scope().find_var(names[0]).get_tensor().set(param,
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place)
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fluid.global_scope().find_var(names[1]).get_tensor().set(param,
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place)
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out = exe.run(feed={
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'inputs_basic_lstm': inputs_basic_lstm_np,
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'sequence_length': sequence_length_np,
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'inputs': inputs_np,
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'pre_hidden': pre_hidden_np,
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'pre_cell': pre_cell_np
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},
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fetch_list=[output_new, rnn_out])
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self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4))
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class TestRnnUtil(unittest.TestCase):
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"""
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Test cases for rnn apis' utility methods for coverage.
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"""
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def test_case(self):
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inputs = {"key1": 1, "key2": 2}
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func = lambda x: x + 1
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outputs = utils.map_structure(func, inputs)
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utils.assert_same_structure(inputs, outputs)
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try:
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inputs["key3"] = 3
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utils.assert_same_structure(inputs, outputs)
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except ValueError as identifier:
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pass
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class EncoderCell(RNNCell):
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"""Encoder Cell"""
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def __init__(
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self,
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num_layers,
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hidden_size,
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dropout_prob=0.,
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init_scale=0.1, ):
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self.num_layers = num_layers
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self.hidden_size = hidden_size
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self.dropout_prob = dropout_prob
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self.lstm_cells = []
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for i in range(num_layers):
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self.lstm_cells.append(LSTMCell(hidden_size))
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def call(self, step_input, states):
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new_states = []
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for i in range(self.num_layers):
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out, new_state = self.lstm_cells[i](step_input, states[i])
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step_input = layers.dropout(
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out,
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self.dropout_prob, ) if self.dropout_prob else out
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new_states.append(new_state)
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return step_input, new_states
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@property
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def state_shape(self):
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return [cell.state_shape for cell in self.lstm_cells]
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class DecoderCell(RNNCell):
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"""Decoder Cell"""
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def __init__(self, num_layers, hidden_size, dropout_prob=0.):
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self.num_layers = num_layers
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|
self.hidden_size = hidden_size
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|
self.dropout_prob = dropout_prob
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|
self.lstm_cells = []
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for i in range(num_layers):
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self.lstm_cells.append(LSTMCell(hidden_size))
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|
|
|
def call(self, step_input, states):
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|
new_lstm_states = []
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|
for i in range(self.num_layers):
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out, new_lstm_state = self.lstm_cells[i](step_input, states[i])
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|
step_input = layers.dropout(
|
|
out,
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|
self.dropout_prob, ) if self.dropout_prob else out
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|
new_lstm_states.append(new_lstm_state)
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|
return step_input, new_lstm_states
|
|
|
|
|
|
def def_seq2seq_model(num_layers, hidden_size, dropout_prob, src_vocab_size,
|
|
trg_vocab_size):
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|
"vanilla seq2seq model"
|
|
# data
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|
source = fluid.data(name="src", shape=[None, None], dtype="int64")
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|
source_length = fluid.data(
|
|
name="src_sequence_length", shape=[None], dtype="int64")
|
|
target = fluid.data(name="trg", shape=[None, None], dtype="int64")
|
|
target_length = fluid.data(
|
|
name="trg_sequence_length", shape=[None], dtype="int64")
|
|
label = fluid.data(name="label", shape=[None, None, 1], dtype="int64")
|
|
|
|
# embedding
|
|
src_emb = fluid.embedding(source, (src_vocab_size, hidden_size))
|
|
tar_emb = fluid.embedding(target, (src_vocab_size, hidden_size))
|
|
|
|
# encoder
|
|
enc_cell = EncoderCell(num_layers, hidden_size, dropout_prob)
|
|
enc_output, enc_final_state = dynamic_rnn(
|
|
cell=enc_cell, inputs=src_emb, sequence_length=source_length)
|
|
|
|
# decoder
|
|
dec_cell = DecoderCell(num_layers, hidden_size, dropout_prob)
|
|
dec_output, dec_final_state = dynamic_rnn(
|
|
cell=dec_cell, inputs=tar_emb, initial_states=enc_final_state)
|
|
logits = layers.fc(dec_output,
|
|
size=trg_vocab_size,
|
|
num_flatten_dims=len(dec_output.shape) - 1,
|
|
bias_attr=False)
|
|
|
|
# loss
|
|
loss = layers.softmax_with_cross_entropy(
|
|
logits=logits, label=label, soft_label=False)
|
|
loss = layers.unsqueeze(loss, axes=[2])
|
|
max_tar_seq_len = layers.shape(target)[1]
|
|
tar_mask = layers.sequence_mask(
|
|
target_length, maxlen=max_tar_seq_len, dtype="float32")
|
|
loss = loss * tar_mask
|
|
loss = layers.reduce_mean(loss, dim=[0])
|
|
loss = layers.reduce_sum(loss)
|
|
|
|
# optimizer
|
|
optimizer = fluid.optimizer.Adam(0.001)
|
|
optimizer.minimize(loss)
|
|
return loss
|
|
|
|
|
|
class TestSeq2SeqModel(unittest.TestCase):
|
|
"""
|
|
Test cases to confirm seq2seq api training correctly.
|
|
"""
|
|
|
|
def setUp(self):
|
|
np.random.seed(123)
|
|
self.model_hparams = {
|
|
"num_layers": 2,
|
|
"hidden_size": 128,
|
|
"dropout_prob": 0.1,
|
|
"src_vocab_size": 100,
|
|
"trg_vocab_size": 100
|
|
}
|
|
|
|
self.iter_num = iter_num = 2
|
|
self.batch_size = batch_size = 4
|
|
src_seq_len = 10
|
|
trg_seq_len = 12
|
|
self.data = {
|
|
"src": np.random.randint(
|
|
2, self.model_hparams["src_vocab_size"],
|
|
(iter_num * batch_size, src_seq_len)).astype("int64"),
|
|
"src_sequence_length": np.random.randint(
|
|
1, src_seq_len, (iter_num * batch_size, )).astype("int64"),
|
|
"trg": np.random.randint(
|
|
2, self.model_hparams["src_vocab_size"],
|
|
(iter_num * batch_size, trg_seq_len)).astype("int64"),
|
|
"trg_sequence_length": np.random.randint(
|
|
1, trg_seq_len, (iter_num * batch_size, )).astype("int64"),
|
|
"label": np.random.randint(
|
|
2, self.model_hparams["src_vocab_size"],
|
|
(iter_num * batch_size, trg_seq_len, 1)).astype("int64"),
|
|
}
|
|
|
|
place = core.CUDAPlace(0) if core.is_compiled_with_cuda(
|
|
) else core.CPUPlace()
|
|
self.exe = Executor(place)
|
|
|
|
def test_seq2seq_model(self):
|
|
main_program = fluid.Program()
|
|
startup_program = fluid.Program()
|
|
with fluid.program_guard(main_program, startup_program):
|
|
cost = def_seq2seq_model(**self.model_hparams)
|
|
self.exe.run(startup_program)
|
|
for iter_idx in range(self.iter_num):
|
|
cost_val = self.exe.run(feed={
|
|
"src": self.data["src"][iter_idx * self.batch_size:(
|
|
iter_idx + 1) * self.batch_size, :],
|
|
"src_sequence_length": self.data["src_sequence_length"]
|
|
[iter_idx * self.batch_size:(iter_idx + 1) *
|
|
self.batch_size],
|
|
"trg": self.data["trg"][iter_idx * self.batch_size:(
|
|
iter_idx + 1) * self.batch_size, :],
|
|
"trg_sequence_length": self.data["trg_sequence_length"][
|
|
iter_idx * self.batch_size:(iter_idx + 1
|
|
) * self.batch_size],
|
|
"label": self.data["label"][iter_idx * self.batch_size:(
|
|
iter_idx + 1) * self.batch_size]
|
|
},
|
|
fetch_list=[cost])[0]
|
|
print("iter_idx: %d, cost: %f" % (iter_idx, cost_val))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|