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250 lines
9.2 KiB
250 lines
9.2 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.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 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 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 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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if __name__ == '__main__':
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unittest.main()
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