test_rnn_cell_api.py 23.0 KB
Newer Older
G
Guo Sheng 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function

import unittest
import numpy

import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.core as core
X
Xing Wu 已提交
23
from paddle.fluid.framework import program_guard, Program
G
Guo Sheng 已提交
24 25 26 27 28 29 30 31 32 33 34 35 36

from paddle.fluid.executor import Executor
from paddle.fluid import framework

from paddle.fluid.layers.rnn import LSTMCell, GRUCell, RNNCell
from paddle.fluid.layers import rnn as dynamic_rnn
from paddle.fluid import contrib
from paddle.fluid.contrib.layers import basic_lstm
import paddle.fluid.layers.utils as utils

import numpy as np


X
Xing Wu 已提交
37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103
class TestLSTMCellError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            batch_size, input_size, hidden_size = 4, 16, 16
            inputs = fluid.data(
                name='inputs', shape=[None, input_size], dtype='float32')
            pre_hidden = fluid.data(
                name='pre_hidden', shape=[None, hidden_size], dtype='float32')
            pre_cell = fluid.data(
                name='pre_cell', shape=[None, hidden_size], dtype='float32')
            cell = LSTMCell(hidden_size)

            def test_input_Variable():
                np_input = np.random.random(
                    (batch_size, input_size)).astype("float32")
                cell(np_input, [pre_hidden, pre_cell])

            self.assertRaises(TypeError, test_input_Variable)

            def test_pre_hidden_Variable():
                np_pre_hidden = np.random.random(
                    (batch_size, hidden_size)).astype("float32")
                cell(inputs, [np_pre_hidden, pre_cell])

            self.assertRaises(TypeError, test_pre_hidden_Variable)

            def test_pre_cell_Variable():
                np_pre_cell = np.random.random(
                    (batch_size, input_size)).astype("float32")
                cell(inputs, [pre_hidden, np_pre_cell])

            self.assertRaises(TypeError, test_pre_cell_Variable)

            def test_input_type():
                error_inputs = fluid.data(
                    name='error_inputs',
                    shape=[None, input_size],
                    dtype='int32')
                cell(error_inputs, [pre_hidden, pre_cell])

            self.assertRaises(TypeError, test_input_type)

            def test_pre_hidden_type():
                error_pre_hidden = fluid.data(
                    name='error_pre_hidden',
                    shape=[None, hidden_size],
                    dtype='int32')
                cell(inputs, [error_pre_hidden, pre_cell])

            self.assertRaises(TypeError, test_pre_hidden_type)

            def test_pre_cell_type():
                error_pre_cell = fluid.data(
                    name='error_pre_cell',
                    shape=[None, hidden_size],
                    dtype='int32')
                cell(inputs, [pre_hidden, error_pre_cell])

            self.assertRaises(TypeError, test_pre_cell_type)

            def test_dtype():
                # the input type must be Variable
                LSTMCell(hidden_size, dtype="int32")

            self.assertRaises(TypeError, test_dtype)


G
Guo Sheng 已提交
104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163
class TestLSTMCell(unittest.TestCase):
    def setUp(self):
        self.batch_size = 4
        self.input_size = 16
        self.hidden_size = 16

    def test_run(self):
        inputs = fluid.data(
            name='inputs', shape=[None, self.input_size], dtype='float32')
        pre_hidden = fluid.data(
            name='pre_hidden', shape=[None, self.hidden_size], dtype='float32')
        pre_cell = fluid.data(
            name='pre_cell', shape=[None, self.hidden_size], dtype='float32')

        cell = LSTMCell(self.hidden_size)
        lstm_hidden_new, lstm_states_new = cell(inputs, [pre_hidden, pre_cell])

        lstm_unit = contrib.layers.rnn_impl.BasicLSTMUnit(
            "basicLSTM", self.hidden_size, None, None, None, None, 1.0,
            "float32")
        lstm_hidden, lstm_cell = lstm_unit(inputs, pre_hidden, pre_cell)

        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()
        exe = Executor(place)
        exe.run(framework.default_startup_program())

        inputs_np = np.random.uniform(
            -0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
        pre_hidden_np = np.random.uniform(
            -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
        pre_cell_np = np.random.uniform(
            -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')

        param_names = [[
            "LSTMCell/BasicLSTMUnit_0.w_0", "basicLSTM/BasicLSTMUnit_0.w_0"
        ], ["LSTMCell/BasicLSTMUnit_0.b_0", "basicLSTM/BasicLSTMUnit_0.b_0"]]

        for names in param_names:
            param = np.array(fluid.global_scope().find_var(names[0]).get_tensor(
            ))
            param = np.random.uniform(
                -0.1, 0.1, size=param.shape).astype('float32')
            fluid.global_scope().find_var(names[0]).get_tensor().set(param,
                                                                     place)
            fluid.global_scope().find_var(names[1]).get_tensor().set(param,
                                                                     place)

        out = exe.run(feed={
            'inputs': inputs_np,
            'pre_hidden': pre_hidden_np,
            'pre_cell': pre_cell_np
        },
                      fetch_list=[lstm_hidden_new, lstm_hidden])

        self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4, atol=0))


X
Xing Wu 已提交
164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
class TestGRUCellError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            batch_size, input_size, hidden_size = 4, 16, 16
            inputs = fluid.data(
                name='inputs', shape=[None, input_size], dtype='float32')
            pre_hidden = layers.data(
                name='pre_hidden',
                shape=[None, hidden_size],
                append_batch_size=False,
                dtype='float32')
            cell = GRUCell(hidden_size)

            def test_input_Variable():
                np_input = np.random.random(
                    (batch_size, input_size)).astype("float32")
                cell(np_input, pre_hidden)

            self.assertRaises(TypeError, test_input_Variable)

            def test_pre_hidden_Variable():
                np_pre_hidden = np.random.random(
                    (batch_size, hidden_size)).astype("float32")
                cell(inputs, np_pre_hidden)

            self.assertRaises(TypeError, test_pre_hidden_Variable)

            def test_input_type():
                error_inputs = fluid.data(
                    name='error_inputs',
                    shape=[None, input_size],
                    dtype='int32')
                cell(error_inputs, pre_hidden)

            self.assertRaises(TypeError, test_input_type)

            def test_pre_hidden_type():
                error_pre_hidden = fluid.data(
                    name='error_pre_hidden',
                    shape=[None, hidden_size],
                    dtype='int32')
                cell(inputs, error_pre_hidden)

            self.assertRaises(TypeError, test_pre_hidden_type)

            def test_dtype():
                # the input type must be Variable
                GRUCell(hidden_size, dtype="int32")

            self.assertRaises(TypeError, test_dtype)


G
Guo Sheng 已提交
216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273
class TestGRUCell(unittest.TestCase):
    def setUp(self):
        self.batch_size = 4
        self.input_size = 16
        self.hidden_size = 16

    def test_run(self):
        inputs = fluid.data(
            name='inputs', shape=[None, self.input_size], dtype='float32')
        pre_hidden = layers.data(
            name='pre_hidden',
            shape=[None, self.hidden_size],
            append_batch_size=False,
            dtype='float32')

        cell = GRUCell(self.hidden_size)
        gru_hidden_new, _ = cell(inputs, pre_hidden)

        gru_unit = contrib.layers.rnn_impl.BasicGRUUnit(
            "basicGRU", self.hidden_size, None, None, None, None, "float32")
        gru_hidden = gru_unit(inputs, pre_hidden)

        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()
        exe = Executor(place)
        exe.run(framework.default_startup_program())

        inputs_np = np.random.uniform(
            -0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
        pre_hidden_np = np.random.uniform(
            -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')

        param_names = [
            ["GRUCell/BasicGRUUnit_0.w_0", "basicGRU/BasicGRUUnit_0.w_0"],
            ["GRUCell/BasicGRUUnit_0.w_1", "basicGRU/BasicGRUUnit_0.w_1"],
            ["GRUCell/BasicGRUUnit_0.b_0", "basicGRU/BasicGRUUnit_0.b_0"],
            ["GRUCell/BasicGRUUnit_0.b_1", "basicGRU/BasicGRUUnit_0.b_1"]
        ]

        for names in param_names:
            param = np.array(fluid.global_scope().find_var(names[0]).get_tensor(
            ))
            param = np.random.uniform(
                -0.1, 0.1, size=param.shape).astype('float32')
            fluid.global_scope().find_var(names[0]).get_tensor().set(param,
                                                                     place)
            fluid.global_scope().find_var(names[1]).get_tensor().set(param,
                                                                     place)

        out = exe.run(feed={'inputs': inputs_np,
                            'pre_hidden': pre_hidden_np},
                      fetch_list=[gru_hidden_new, gru_hidden])

        self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4, atol=0))


X
Xing Wu 已提交
274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359
class TestRnnError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            batch_size = 4
            input_size = 16
            hidden_size = 16
            seq_len = 4
            inputs = fluid.data(
                name='inputs', shape=[None, input_size], dtype='float32')
            pre_hidden = layers.data(
                name='pre_hidden',
                shape=[None, hidden_size],
                append_batch_size=False,
                dtype='float32')
            inputs_basic_lstm = fluid.data(
                name='inputs_basic_lstm',
                shape=[None, None, input_size],
                dtype='float32')
            sequence_length = fluid.data(
                name="sequence_length", shape=[None], dtype='int64')

            inputs_dynamic_rnn = layers.transpose(
                inputs_basic_lstm, perm=[1, 0, 2])
            cell = LSTMCell(hidden_size, name="LSTMCell_for_rnn")
            np_inputs_dynamic_rnn = np.random.random(
                (seq_len, batch_size, input_size)).astype("float32")

            def test_input_Variable():
                dynamic_rnn(
                    cell=cell,
                    inputs=np_inputs_dynamic_rnn,
                    sequence_length=sequence_length,
                    is_reverse=False)

            self.assertRaises(TypeError, test_input_Variable)

            def test_input_list():
                dynamic_rnn(
                    cell=cell,
                    inputs=[np_inputs_dynamic_rnn],
                    sequence_length=sequence_length,
                    is_reverse=False)

            self.assertRaises(TypeError, test_input_list)

            def test_initial_states_type():
                cell = GRUCell(hidden_size, name="GRUCell_for_rnn")
                error_initial_states = np.random.random(
                    (batch_size, hidden_size)).astype("float32")
                dynamic_rnn(
                    cell=cell,
                    inputs=inputs_dynamic_rnn,
                    initial_states=error_initial_states,
                    sequence_length=sequence_length,
                    is_reverse=False)

            self.assertRaises(TypeError, test_initial_states_type)

            def test_initial_states_list():
                error_initial_states = [
                    np.random.random(
                        (batch_size, hidden_size)).astype("float32"),
                    np.random.random(
                        (batch_size, hidden_size)).astype("float32")
                ]
                dynamic_rnn(
                    cell=cell,
                    inputs=inputs_dynamic_rnn,
                    initial_states=error_initial_states,
                    sequence_length=sequence_length,
                    is_reverse=False)

            self.assertRaises(TypeError, test_initial_states_type)

            def test_sequence_length_type():
                np_sequence_length = np.random.random(
                    (batch_size)).astype("float32")
                dynamic_rnn(
                    cell=cell,
                    inputs=inputs_dynamic_rnn,
                    sequence_length=np_sequence_length,
                    is_reverse=False)

            self.assertRaises(TypeError, test_sequence_length_type)


G
Guo Sheng 已提交
360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453
class TestRnn(unittest.TestCase):
    def setUp(self):
        self.batch_size = 4
        self.input_size = 16
        self.hidden_size = 16
        self.seq_len = 4

    def test_run(self):
        inputs_basic_lstm = fluid.data(
            name='inputs_basic_lstm',
            shape=[None, None, self.input_size],
            dtype='float32')
        sequence_length = fluid.data(
            name="sequence_length", shape=[None], dtype='int64')

        inputs_dynamic_rnn = layers.transpose(inputs_basic_lstm, perm=[1, 0, 2])
        cell = LSTMCell(self.hidden_size, name="LSTMCell_for_rnn")
        output, final_state = dynamic_rnn(
            cell=cell,
            inputs=inputs_dynamic_rnn,
            sequence_length=sequence_length,
            is_reverse=False)
        output_new = layers.transpose(output, perm=[1, 0, 2])

        rnn_out, last_hidden, last_cell = basic_lstm(inputs_basic_lstm, None, None, self.hidden_size, num_layers=1, \
                batch_first = False, bidirectional=False, sequence_length=sequence_length, forget_bias = 1.0)

        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()
        exe = Executor(place)
        exe.run(framework.default_startup_program())

        inputs_basic_lstm_np = np.random.uniform(
            -0.1, 0.1,
            (self.seq_len, self.batch_size, self.input_size)).astype('float32')
        sequence_length_np = np.ones(
            self.batch_size, dtype='int64') * self.seq_len

        inputs_np = np.random.uniform(
            -0.1, 0.1, (self.batch_size, self.input_size)).astype('float32')
        pre_hidden_np = np.random.uniform(
            -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')
        pre_cell_np = np.random.uniform(
            -0.1, 0.1, (self.batch_size, self.hidden_size)).astype('float32')

        param_names = [[
            "LSTMCell_for_rnn/BasicLSTMUnit_0.w_0",
            "basic_lstm_layers_0/BasicLSTMUnit_0.w_0"
        ], [
            "LSTMCell_for_rnn/BasicLSTMUnit_0.b_0",
            "basic_lstm_layers_0/BasicLSTMUnit_0.b_0"
        ]]

        for names in param_names:
            param = np.array(fluid.global_scope().find_var(names[0]).get_tensor(
            ))
            param = np.random.uniform(
                -0.1, 0.1, size=param.shape).astype('float32')
            fluid.global_scope().find_var(names[0]).get_tensor().set(param,
                                                                     place)
            fluid.global_scope().find_var(names[1]).get_tensor().set(param,
                                                                     place)

        out = exe.run(feed={
            'inputs_basic_lstm': inputs_basic_lstm_np,
            'sequence_length': sequence_length_np,
            'inputs': inputs_np,
            'pre_hidden': pre_hidden_np,
            'pre_cell': pre_cell_np
        },
                      fetch_list=[output_new, rnn_out])

        self.assertTrue(np.allclose(out[0], out[1], rtol=1e-4))


class TestRnnUtil(unittest.TestCase):
    """
    Test cases for rnn apis' utility methods for coverage.
    """

    def test_case(self):
        inputs = {"key1": 1, "key2": 2}
        func = lambda x: x + 1
        outputs = utils.map_structure(func, inputs)
        utils.assert_same_structure(inputs, outputs)
        try:
            inputs["key3"] = 3
            utils.assert_same_structure(inputs, outputs)
        except ValueError as identifier:
            pass


454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618
class EncoderCell(RNNCell):
    """Encoder Cell"""

    def __init__(
            self,
            num_layers,
            hidden_size,
            dropout_prob=0.,
            init_scale=0.1, ):
        self.num_layers = num_layers
        self.hidden_size = hidden_size
        self.dropout_prob = dropout_prob
        self.lstm_cells = []

        for i in range(num_layers):
            self.lstm_cells.append(LSTMCell(hidden_size))

    def call(self, step_input, states):
        new_states = []
        for i in range(self.num_layers):
            out, new_state = self.lstm_cells[i](step_input, states[i])
            step_input = layers.dropout(
                out,
                self.dropout_prob, ) if self.dropout_prob else out
            new_states.append(new_state)
        return step_input, new_states

    @property
    def state_shape(self):
        return [cell.state_shape for cell in self.lstm_cells]


class DecoderCell(RNNCell):
    """Decoder Cell"""

    def __init__(self, num_layers, hidden_size, dropout_prob=0.):
        self.num_layers = num_layers
        self.hidden_size = hidden_size
        self.dropout_prob = dropout_prob
        self.lstm_cells = []
        for i in range(num_layers):
            self.lstm_cells.append(LSTMCell(hidden_size))

    def call(self, step_input, states):
        new_lstm_states = []
        for i in range(self.num_layers):
            out, new_lstm_state = self.lstm_cells[i](step_input, states[i])
            step_input = layers.dropout(
                out,
                self.dropout_prob, ) if self.dropout_prob else out
            new_lstm_states.append(new_lstm_state)
        return step_input, new_lstm_states


def def_seq2seq_model(num_layers, hidden_size, dropout_prob, src_vocab_size,
                      trg_vocab_size):
    "vanilla seq2seq model"
    # data
    source = fluid.data(name="src", shape=[None, None], dtype="int64")
    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="float")
    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))


G
Guo Sheng 已提交
619 620
if __name__ == '__main__':
    unittest.main()