layers.py 176.5 KB
Newer Older
1
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
Z
zhangjinchao01 已提交
2 3 4 5 6 7 8 9 10 11 12 13 14 15
#
# 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.

import functools
16
import collections
Y
Yu Yang 已提交
17
import inspect
Z
zhangjinchao01 已提交
18 19 20

from paddle.trainer.config_parser import *
from .activations import LinearActivation, SigmoidActivation, TanhActivation, \
Y
Yu Yang 已提交
21
    ReluActivation, IdentityActivation, SoftmaxActivation, BaseActivation
Z
zhangjinchao01 已提交
22 23 24 25
from .evaluators import *
from .poolings import MaxPooling, AvgPooling, BasePoolingType
from .attrs import *
from .default_decorators import *
26

Z
zhangjinchao01 已提交
27 28 29 30 31 32
try:
    import cPickle as pickle
except ImportError:
    import pickle
import copy

Q
qijun 已提交
33 34 35 36 37 38 39 40
__all__ = [
    "full_matrix_projection",
    "AggregateLevel",
    "ExpandLevel",
    "identity_projection",
    "dotmul_projection",
    "dotmul_operator",
    "repeat_layer",
41
    "seq_reshape_layer",
Q
qijun 已提交
42 43 44 45 46 47 48 49 50 51 52 53 54
    "table_projection",
    "mixed_layer",
    "data_layer",
    "embedding_layer",
    "fc_layer",
    "grumemory",
    "pooling_layer",
    "lstmemory",
    "last_seq",
    "first_seq",
    "cos_sim",
    "hsigmoid",
    "conv_projection",
L
Luo Tao 已提交
55
    "mse_cost",
Q
qijun 已提交
56 57 58 59 60 61 62 63 64
    "regression_cost",
    'classification_cost',
    "LayerOutput",
    'img_conv_layer',
    'img_pool_layer',
    'batch_norm_layer',
    'img_cmrnorm_layer',
    'addto_layer',
    'concat_layer',
65
    'seq_concat_layer',
Q
qijun 已提交
66 67 68 69 70 71
    'lstm_step_layer',
    'recurrent_group',
    'memory',
    'StaticInput',
    'expand_layer',
    'scaling_layer',
X
xuwei06 已提交
72
    'scaling_projection',
Q
qijun 已提交
73 74 75 76
    'power_layer',
    'interpolation_layer',
    'bilinear_interp_layer',
    'trans_layer',
77
    'rotate_layer',
Q
qijun 已提交
78 79 80 81 82 83 84 85 86
    'sum_to_one_norm_layer',
    'get_output_layer',
    'LayerType',
    'context_projection',
    'beam_search',
    'maxid_layer',
    'GeneratedInput',
    'SubsequenceInput',
    'gru_step_layer',
Y
Yu Yang 已提交
87
    'gru_step_naive_layer',
Q
qijun 已提交
88 89 90 91 92 93 94 95 96 97 98 99
    'recurrent_layer',
    'BaseGeneratedInput',
    'conv_operator',
    'conv_shift_layer',
    'tensor_layer',
    'selective_fc_layer',
    'sampling_id_layer',
    'slope_intercept_layer',
    'trans_full_matrix_projection',
    'linear_comb_layer',
    'convex_comb_layer',
    'ctc_layer',
100
    'warp_ctc_layer',
Q
qijun 已提交
101 102 103 104 105 106 107 108 109 110 111 112 113 114
    'crf_layer',
    'crf_decoding_layer',
    'nce_layer',
    'cross_entropy_with_selfnorm',
    'cross_entropy',
    'multi_binary_label_cross_entropy',
    'sum_cost',
    'rank_cost',
    'lambda_cost',
    'huber_cost',
    'block_expand_layer',
    'maxout_layer',
    'out_prod_layer',
    'print_layer',
Y
yuan 已提交
115
    'priorbox_layer',
116
    'cross_channel_norm_layer',
Q
qijun 已提交
117
    'spp_layer',
D
dangqingqing 已提交
118
    'pad_layer',
L
Luo Tao 已提交
119
    'eos_layer',
120
    'smooth_l1_cost',
121
    'layer_support',
W
wwhu 已提交
122
    'multiplex_layer',
Q
qijun 已提交
123
]
Z
zhangjinchao01 已提交
124 125 126 127 128 129 130 131 132 133 134 135 136


class LayerType(object):
    """
    Layer type enumerations.
    """

    DATA = "data"
    MIXED_LAYER = "mixed"
    LSTMEMORY = "lstmemory"
    GRUMEMORY = "gated_recurrent"
    SEQUENCE_LAST_INSTANCE = "seqlastins"
    SEQUENCE_FIRST_INSTANCE = "seqfirstins"
137
    SEQUENCE_RESHAPE = "seqreshape"
Z
zhangjinchao01 已提交
138 139 140 141
    POOLING_MAX = "max"
    POOLING_AVG = 'average'
    FC_LAYER = "fc"
    COST = 'cost'
142 143
    COSINE_SIM_VEC = 'cos_vm'
    COSINE_SIM = 'cos'
Z
zhangjinchao01 已提交
144 145
    HSIGMOID = 'hsigmoid'
    CONV_LAYER = "conv"
146
    CONVTRANS_LAYER = "convt"
147 148 149
    EXCONV_LAYER = "exconv"
    EXCONVTRANS_LAYER = "exconvt"
    CUDNNCONV_LAYER = "cudnn_conv"
Z
zhangjinchao01 已提交
150 151 152 153 154 155 156 157
    POOL_LAYER = "pool"
    BATCH_NORM_LAYER = 'batch_norm'
    NORM_LAYER = 'norm'
    SUM_TO_ONE_NORM_LAYER = 'sum_to_one_norm'
    ADDTO_LAYER = 'addto'

    CONCAT_LAYER = 'concat'
    CONCAT_PROJ_LAYER = 'concat2'
158
    SEQUENCE_CONCAT_LAYER = 'seqconcat'
Z
zhangjinchao01 已提交
159 160 161 162 163 164 165

    LSTM_STEP_LAYER = 'lstm_step'
    GRU_STEP_LAYER = 'gru_step'
    GET_OUTPUT_LAYER = 'get_output'

    EXPAND_LAYER = 'expand'
    INTERPOLATION_LAYER = 'interpolation'
L
liaogang 已提交
166
    BILINEAR_INTERP_LAYER = 'bilinear_interp'
Z
zhangjinchao01 已提交
167 168 169
    POWER_LAYER = 'power'
    SCALING_LAYER = 'scaling'
    TRANS_LAYER = 'trans'
170
    ROTATE_LAYER = 'rotate'
H
Haonan 已提交
171
    OUT_PROD_LAYER = 'out_prod'
X
xuwei06 已提交
172
    FEATURE_MAP_EXPAND_LAYER = 'featmap_expand'
Z
zhangjinchao01 已提交
173 174 175 176 177 178 179 180 181 182 183

    MEMORY = 'memory'
    MAXID_LAYER = 'maxid'
    EOSID_LAYER = 'eos_id'
    RECURRENT_LAYER = 'recurrent'

    CONV_SHIFT_LAYER = "conv_shift"
    TENSOR_LAYER = "tensor"
    SEL_FC_LAYER = "selective_fc"
    SAMPLING_ID_LAYER = "sampling_id"
    SLOPE_INTERCEPT_LAYER = "slope_intercept"
184
    LINEAR_COMBINATION_LAYER = "convex_comb"
Z
zhangjinchao01 已提交
185
    BLOCK_EXPAND = "blockexpand"
186
    MAXOUT = "maxout"
Q
qijun 已提交
187
    SPP_LAYER = "spp"
D
dangqingqing 已提交
188
    PAD_LAYER = "pad"
W
wwhu 已提交
189
    MULTIPLEX_LAYER = "multiplex"
Z
zhangjinchao01 已提交
190

191
    PRINT_LAYER = "print"
Y
yuan 已提交
192
    PRIORBOX_LAYER = "priorbox"
193

Z
zhangjinchao01 已提交
194
    CTC_LAYER = "ctc"
195
    WARP_CTC_LAYER = "warp_ctc"
Z
zhangjinchao01 已提交
196 197
    CRF_LAYER = "crf"
    CRF_DECODING_LAYER = "crf_decoding"
198
    NCE_LAYER = 'nce'
Z
zhangjinchao01 已提交
199 200 201 202 203 204 205 206

    RANK_COST = "rank-cost"
    LAMBDA_COST = "lambda_cost"
    HUBER = "huber"
    CROSS_ENTROPY = "multi-class-cross-entropy"
    CROSS_ENTROPY_WITH_SELFNORM = "multi_class_cross_entropy_with_selfnorm"
    SOFT_BIN_CLASS_CROSS_ENTROPY = "soft_binary_class_cross_entropy"
    MULTI_BIN_LABEL_CROSS_ENTROPY = "multi_binary_label_cross_entropy"
X
xuwei06 已提交
207
    SUM_COST = "sum_cost"
D
dangqingqing 已提交
208
    SMOOTH_L1 = "smooth_l1"
Z
zhangjinchao01 已提交
209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229

    @staticmethod
    def is_layer_type(type_name):
        """
        If type_name is a layer type.

        :param type_name: layer type name. Because layer type enumerations are
                          strings.
        :type type_name: basestring
        :return: True if is a layer_type
        :rtype: bool
        """
        for key in dir(LayerType):
            if key.isupper():
                att = getattr(LayerType, key)
                if isinstance(att, basestring) and type_name == att:
                    return True
        return False


class AggregateLevel(object):
230
    """
L
Luo Tao 已提交
231
    PaddlePaddle supports three sequence types:
232 233 234

    - :code:`SequenceType.NO_SEQUENCE` means the sample is not a sequence.
    - :code:`SequenceType.SEQUENCE` means the sample is a sequence.
L
Luo Tao 已提交
235 236
    - :code:`SequenceType.SUB_SEQUENCE` means the sample is a nested sequence,
      each timestep of which is also a sequence.
237

L
Luo Tao 已提交
238
    Accordingly, AggregateLevel supports two modes:
239

L
Luo Tao 已提交
240
    - :code:`AggregateLevel.TO_NO_SEQUENCE` means the aggregation acts on each
L
Luo Tao 已提交
241
      timestep of a sequence, both :code:`SUB_SEQUENCE` and :code:`SEQUENCE` will
242 243
      be aggregated to :code:`NO_SEQUENCE`.

L
Luo Tao 已提交
244
    - :code:`AggregateLevel.TO_SEQUENCE` means the aggregation acts on each
245 246 247
      sequence of a nested sequence, :code:`SUB_SEQUENCE` will be aggregated to
      :code:`SEQUENCE`.
    """
L
Luo Tao 已提交
248 249
    TO_NO_SEQUENCE = 'non-seq'
    TO_SEQUENCE = 'seq'
250 251 252
    # compatible with previous configuration
    EACH_TIMESTEP = TO_NO_SEQUENCE
    EACH_SEQUENCE = TO_SEQUENCE
Z
zhangjinchao01 已提交
253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274


class LayerOutput(object):
    """
    LayerOutput is output for layer function. It is used internally by several
    reasons.

    - Check layer connection make sense.

        - FC(Softmax) => Cost(MSE Error) is not good for example.

    - Tracking layer connection.

    - Pass to layer methods as input.

    :param name: Layer output name.
    :type name: basestring
    :param layer_type: Current Layer Type. One of LayerType enumeration.
    :type layer_type: basestring
    :param activation: Layer Activation.
    :type activation: BaseActivation.
    :param parents: Layer's parents.
275
    :type parents: list|tuple|collections.Sequence
Z
zhangjinchao01 已提交
276 277
    """

Q
qijun 已提交
278 279 280 281 282 283 284 285 286
    def __init__(self,
                 name,
                 layer_type,
                 parents=None,
                 activation=None,
                 num_filters=None,
                 img_norm_type=None,
                 size=None,
                 outputs=None,
287
                 reverse=None):
Z
zhangjinchao01 已提交
288 289
        assert isinstance(name, basestring)
        assert isinstance(layer_type, basestring)
X
xuwei06 已提交
290
        assert size is not None
Z
zhangjinchao01 已提交
291 292
        assert LayerType.is_layer_type(layer_type)
        self.name = name
X
xuwei06 已提交
293
        self.full_name = MakeLayerNameInSubmodel(name)
Z
zhangjinchao01 已提交
294
        self.layer_type = layer_type
295 296
        if parents is not None and type(parents) != list:
            parents = [parents]
Z
zhangjinchao01 已提交
297 298 299 300 301 302 303 304
        self.parents = [] if parents is None else parents
        self.activation = activation
        self.num_filters = num_filters
        self.img_norm_type = img_norm_type
        self.size = size
        if outputs is None:
            outputs = ['default']
        self.outputs = outputs
305
        self.reverse = reverse
Z
zhangjinchao01 已提交
306 307 308 309 310 311 312 313 314 315 316 317 318

    def __repr__(self):
        """
        Disable __repr__ for debug reason. Will be implemented when release
        """
        assert False, "this method should not be invoked"

    def __str__(self):
        """
        Disable __str__ for debug reason. Will be implemented when release
        """
        assert False, "this method should not be invoked"

319 320 321 322 323 324 325 326
    def set_input(self, input):
        """
        Set the input for a memory layer. Can only be used for memory layer
        """
        assert isinstance(input, LayerOutput)
        assert self.layer_type == LayerType.MEMORY
        SetMemoryInput(self.name, input.name)

Z
zhangjinchao01 已提交
327 328 329

ERROR_CLIPPING = 'error_clipping_threshold'
DROPOUT = 'drop_rate'
330
DEVICE = 'device'
Z
zhangjinchao01 已提交
331 332 333


def layer_support(*attrs):
334
    attrs_list = list(attrs)
335
    attrs_list.append(DEVICE)
Q
qijun 已提交
336

Z
zhangjinchao01 已提交
337 338 339
    def decorator(method):
        @functools.wraps(method)
        def wrapper(*args, **kwargs):
340
            for attr in attrs_list:
Z
zhangjinchao01 已提交
341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356
                for each in args:
                    if isinstance(each, ExtraLayerAttribute):
                        setattr(each, '_'.join(['can', attr]), True)
                for key in kwargs:
                    val = kwargs[key]
                    if isinstance(val, ExtraLayerAttribute):
                        setattr(val, '_'.join(['can', attr]), True)
            for each in args:
                if isinstance(each, ExtraLayerAttribute):
                    each.check(method.__name__)
            for key in kwargs:
                val = kwargs[key]
                if isinstance(val, ExtraLayerAttribute):
                    val.check(method.__name__)
            return method(*args, **kwargs)

Y
Yu Yang 已提交
357 358 359 360 361
        if hasattr(method, 'argspec'):
            wrapper.argspec = method.argspec
        else:
            wrapper.argspec = inspect.getargspec(method)

Z
zhangjinchao01 已提交
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
        return wrapper

    return decorator


@wrap_param_attr_default()
def full_matrix_projection(input, size=0, param_attr=None):
    """
    Full Matrix Projection. It performs full matrix multiplication.

    ..  math::
        out.row[i] += in.row[i] * weight

    There are two styles of usage.

    1. When used in mixed_layer like this, you can only set the input:

    .. code-block:: python

       with mixed_layer(size=100) as m:
           m += full_matrix_projection(input=layer)

    2. When used as an independant object like this, you must set the size:

    .. code-block:: python

       proj = full_matrix_projection(input=layer,
                                     size=100,
                                     param_attr=ParamAttr(name='_proj'))

    :param input: input layer
    :type input: LayerOutput
    :param size: The parameter size. Means the width of parameter.
    :type size: int
    :param param_attr: Parameter config, None if use default.
    :type param_attr: ParameterAttribute
    :return: A FullMatrixProjection Object.
    :rtype: FullMatrixProjection
    """
Q
qijun 已提交
401 402
    proj = FullMatrixProjection(
        input_layer_name=input.name, size=size, **param_attr.attr)
Z
zhangjinchao01 已提交
403 404 405 406
    proj.origin = input
    return proj


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
@wrap_param_attr_default()
def trans_full_matrix_projection(input, size=0, param_attr=None):
    """
    Different from full_matrix_projection, this projection performs matrix
    multiplication, using transpose of weight.

    ..  math::
        out.row[i] += in.row[i] * w^\mathrm{T}

    :math:`w^\mathrm{T}` means transpose of weight.
    The simply usage is:

    .. code-block:: python

       proj = trans_full_matrix_projection(input=layer,
                                           size=100,
                                           param_attr=ParamAttr(
                                                name='_proj',
                                                initial_mean=0.0,
                                                initial_std=0.01))

    :param input: input layer
    :type input: LayerOutput
    :param size: The parameter size. Means the width of parameter.
    :type size: int
    :param param_attr: Parameter config, None if use default.
    :type param_attr: ParameterAttribute
    :return: A TransposedFullMatrixProjection Object.
    :rtype: TransposedFullMatrixProjection
    """
Q
qijun 已提交
437 438
    proj = TransposedFullMatrixProjection(
        input_layer_name=input.name, size=size, **param_attr.attr)
439 440 441 442
    proj.origin = input
    return proj


Z
zhangjinchao01 已提交
443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481
@wrap_param_attr_default()
def table_projection(input, size=0, param_attr=None):
    """
    Table Projection. It selects rows from parameter where row\_id
    is in input\_ids.

    .. math::
       out.row[i] += table.row[ids[i]]

    where :math:`out` is output, :math:`table` is parameter, :math:`ids` is input\_ids,
    and :math:`i` is row\_id.

    There are two styles of usage.

    1. When used in mixed_layer like this, you can only set the input:

    .. code-block:: python

       with mixed_layer(size=100) as m:
           m += table_projection(input=layer)

    2. When used as an independant object like this, you must set the size:

    .. code-block:: python

       proj = table_projection(input=layer,
                               size=100,
                               param_attr=ParamAttr(name='_proj'))


    :param input: Input layer, which must contains id fields.
    :type input: LayerOutput
    :param size: The parameter size. Means the width of parameter.
    :type size: int
    :param param_attr: Parameter config, None if use default.
    :type param_attr: ParameterAttribute
    :return: A TableProjection Object.
    :rtype: TableProjection
    """
Q
qijun 已提交
482 483
    proj = TableProjection(
        input_layer_name=input.name, size=size, **param_attr.attr)
Z
zhangjinchao01 已提交
484 485 486 487
    proj.origin = input
    return proj


488
def identity_projection(input, offset=None, size=None):
Z
zhangjinchao01 已提交
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
    """
    1. IdentityProjection if offset=None. It performs:

    .. math::
       out.row[i] += in.row[i]

    The example usage is:

    .. code-block:: python

       proj = identity_projection(input=layer)


    2. IdentityOffsetProjection if offset!=None. It likes IdentityProjection,
    but layer size may be smaller than input size.
    It select dimesions [offset, offset+layer_size) from input:

    .. math::
       out.row[i] += in.row[i + \\textrm{offset}]

    The example usage is:

    .. code-block:: python

       proj = identity_projection(input=layer,
                                  offset=10)

    Note that both of two projections should not have any parameter.

    :param input: Input Layer.
519
    :type input: LayerOutput
Z
zhangjinchao01 已提交
520 521
    :param offset: Offset, None if use default.
    :type offset: int
X
xuwei06 已提交
522
    :return: A IdentityProjection or IdentityOffsetProjection object
Z
zhangjinchao01 已提交
523 524 525 526 527 528
    :rtype: IdentityProjection or IdentityOffsetProjection
    """
    if offset is None:
        proj = IdentityProjection(input_layer_name=input.name)
        proj.origin = input
    else:
529 530
        if size is None:
            size = input.size - offset
Q
qijun 已提交
531
        proj = IdentityOffsetProjection(
532
            input_layer_name=input.name, offset=offset, size=size)
Z
zhangjinchao01 已提交
533 534 535 536
        proj.origin = input
    return proj


X
xuwei06 已提交
537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558
@wrap_param_attr_default()
def scaling_projection(input, param_attr=None):
    """
    scaling_projection multiplies the input with a scalar parameter and add to
    the output.

    .. math::
       out += w * in

    The example usage is:

    .. code-block:: python

       proj = scaling_projection(input=layer)

    :param input: Input Layer.
    :type input: LayerOutput
    :param param_attr: Parameter config, None if use default.
    :type param_attr: ParameterAttribute
    :return: A ScalingProjection object
    :rtype: ScalingProjection
    """
L
Luo Tao 已提交
559
    proj = ScalingProjection(input_layer_name=input.name, **param_attr.attr)
X
xuwei06 已提交
560 561 562 563
    proj.origin = input
    return proj


Z
zhangjinchao01 已提交
564
@wrap_param_attr_default()
565
def dotmul_projection(input, param_attr=None):
Z
zhangjinchao01 已提交
566
    """
567
    DotMulProjection with a layer as input.
Z
zhangjinchao01 已提交
568 569 570 571 572 573 574 575 576 577 578 579 580
    It performs element-wise multiplication with weight.

    ..  math::
        out.row[i] += in.row[i] .* weight

    where :math:`.*` means element-wise multiplication.

    The example usage is:

    .. code-block:: python

       proj = dotmul_projection(input=layer)

581 582 583 584 585 586 587
    :param input: Input layer.
    :type input: LayerOutput
    :param param_attr: Parameter config, None if use default.
    :type param_attr: ParameterAttribute
    :return: A DotMulProjection Object.
    :rtype: DotMulProjection
    """
Q
qijun 已提交
588 589
    proj = DotMulProjection(
        input_layer_name=input.name, size=input.size, **param_attr.attr)
590
    proj.origin = input
591
    return proj
Z
zhangjinchao01 已提交
592

593 594

def dotmul_operator(a=None, b=None, scale=1, **kwargs):
595 596
    """
    DotMulOperator takes two inputs and performs element-wise multiplication:
597

Z
zhangjinchao01 已提交
598
    .. math::
L
Luo Tao 已提交
599
       out.row[i] += scale * (a.row[i] .* b.row[i])
600

Z
zhangjinchao01 已提交
601 602
    where :math:`.*` means element-wise multiplication, and
    scale is a config scalar, its default value is one.
603

Z
zhangjinchao01 已提交
604
    The example usage is:
605

Z
zhangjinchao01 已提交
606
    .. code-block:: python
607

L
Luo Tao 已提交
608
       op = dotmul_operator(a=layer1, b=layer2, scale=0.5)
609

610 611 612 613
    :param a: Input layer1
    :type a: LayerOutput
    :param b: Input layer2
    :type b: LayerOutput
Z
zhangjinchao01 已提交
614 615
    :param scale: config scalar, default value is one.
    :type scale: float
616 617
    :return: A DotMulOperator Object.
    :rtype: DotMulOperator
Z
zhangjinchao01 已提交
618
    """
619 620 621
    if 'x' in kwargs or 'y' in kwargs:
        logger.warning('x and y arguments for dotmul_operator is deprecated. '
                       'Please use a and b as parameter.')
Q
qijun 已提交
622
    a = kwargs.get('x', a)  # For Backward capacity.
623 624 625 626 627 628
    b = kwargs.get('y', b)
    assert isinstance(a, LayerOutput)
    assert isinstance(b, LayerOutput)
    if a.size is not None and b.size is not None:
        assert a.size == b.size

Q
qijun 已提交
629
    op = DotMulOperator(input_layer_names=[a.name, b.name], scale=scale)
630
    op.origin = [a, b]
631
    return op
Z
zhangjinchao01 已提交
632

633

Z
zhangjinchao01 已提交
634
@wrap_bias_attr_default(['padding_attr'])
Q
qijun 已提交
635 636 637
def context_projection(input,
                       context_len,
                       context_start=None,
Z
zhangjinchao01 已提交
638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673
                       padding_attr=False):
    """
    Context Projection.

    It just simply reorganizes input sequence, combines "context_len" sequence
    to one context from context_start. "context_start" will be set to
    -(context_len - 1) / 2 by default. If context position out of sequence
    length, padding will be filled as zero if padding_attr = False, otherwise
    it is trainable.

    For example, origin sequence is [A B C D E F G], context len is 3, then
    after context projection and not set padding_attr, sequence will
    be [ 0AB ABC BCD CDE DEF EFG FG0 ].

    :param input: Input Sequence.
    :type input: LayerOutput
    :param context_len: context length.
    :type context_len: int
    :param context_start: context start position. Default is
                          -(context_len - 1)/2
    :type context_start: int
    :param padding_attr: Padding Parameter Attribute. If false, it means padding
                         always be zero. Otherwise Padding is learnable, and
                         parameter attribute is set by this parameter.
    :type padding_attr: bool|ParameterAttribute
    :return: Projection
    :rtype: Projection
    """
    context_start = -(
        context_len - 1) / 2 if context_start is None else context_start

    extra_dict = dict()
    trainable = isinstance(padding_attr, ParameterAttribute)
    if trainable:
        extra_dict = padding_attr.attr

Q
qijun 已提交
674 675 676 677 678 679
    proj = ContextProjection(
        input_layer_name=input.name,
        context_length=context_len,
        context_start=context_start,
        trainable_padding=trainable,
        **extra_dict)
Z
zhangjinchao01 已提交
680 681 682 683 684 685 686 687 688 689 690 691 692
    proj.origin = input
    return proj


class MixedLayerType(LayerOutput):
    """
    The internal object for trainer_helpers.
    """

    class AddToSealedMixedLayerException(Exception):
        def __init__(self):
            Exception.__init__(self)

Q
qijun 已提交
693
    def __init__(self, name, size, act, bias_attr, layer_attr, parents=None):
Z
zhangjinchao01 已提交
694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709
        """
        Ctor.
        :param name: layer name.
        :type name: basestring
        :param size: layer size.
        :type size: int
        :param act: activation type.
        :type act: BaseActivation
        :param bias_attr: The Bias Attribute. If no bias, then pass False or
                          something not type of ParameterAttribute. None will
                          get a default Bias.
        :type bias_attr: ParameterAttribute or None means has bias. Any other
                         type means no bias.
        :param layer_attr: Extra Layer Attribute.
        :type layer_attr: ExtraLayerAttribute or None
        """
Q
qijun 已提交
710 711 712 713 714 715 716
        LayerOutput.__init__(
            self,
            name,
            LayerType.MIXED_LAYER,
            parents,
            size=size,
            activation=act)
Z
zhangjinchao01 已提交
717 718 719 720 721
        self.bias_attr = bias_attr
        self.layer_attr = layer_attr
        self.inputs = []
        self.finalized = False

722
    def __iadd__(self, other):
Z
zhangjinchao01 已提交
723 724 725 726 727 728 729 730
        """
        + += operator
        :param other: Other projection.
        :type other: Projection
        :return: self.
        :rtype: MixedLayerType
        """
        if not self.finalized:
731
            assert isinstance(other, Projection) or isinstance(other, Operator)
Z
zhangjinchao01 已提交
732
            self.inputs.append(other)
733 734 735 736
            if isinstance(other, Projection):
                self.parents.append(other.origin)
            else:
                self.parents.extend(other.origin)
Z
zhangjinchao01 已提交
737 738 739 740 741 742 743 744
            return self
        else:
            raise MixedLayerType.AddToSealedMixedLayerException()

    def __enter__(self):
        assert len(self.inputs) == 0
        return self

745
    def __exit__(self, exc_type, exc_value, tb):
W
wangyang59 已提交
746 747
        if exc_value is not None:
            raise exc_value
Z
zhangjinchao01 已提交
748
        assert len(self.inputs) != 0
749
        ml = MixedLayer(
Z
zhangjinchao01 已提交
750 751 752 753 754
            name=self.name,
            size=self.size,
            active_type=self.activation.name,
            bias=ParamAttr.to_bias(self.bias_attr),
            inputs=self.inputs,
Q
qijun 已提交
755
            **ExtraLayerAttribute.to_kwargs(self.layer_attr))
756 757 758
        # update the size which might be computed inside MixedLayer
        # according to the operator's output size
        self.size = ml.config.size
759
        self.finalized = True
Z
zhangjinchao01 已提交
760 761 762 763 764 765


@wrap_name_default("mixed")
@wrap_act_default(act=LinearActivation())
@wrap_bias_attr_default(has_bias=False)
@layer_support(ERROR_CLIPPING, DROPOUT)
Q
qijun 已提交
766 767 768 769 770
def mixed_layer(size=0,
                input=None,
                name=None,
                act=None,
                bias_attr=False,
Z
zhangjinchao01 已提交
771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814
                layer_attr=None):
    """
    Mixed Layer. A mixed layer will add all inputs together, then activate.
    Each inputs is a projection or operator.

    There are two styles of usages.

    1. When not set inputs parameter, use mixed_layer like this:

    .. code-block:: python

       with mixed_layer(size=256) as m:
           m += full_matrix_projection(input=layer1)
           m += identity_projection(input=layer2)

    2. You can also set all inputs when invoke mixed_layer as follows:

    .. code-block:: python

       m = mixed_layer(size=256,
                       input=[full_matrix_projection(input=layer1),
                              full_matrix_projection(input=layer2)])

    :param name: mixed layer name. Can be referenced by other layer.
    :type name: basestring
    :param size: layer size.
    :type size: int
    :param input: inputs layer. It is an optional parameter. If set,
                  then this function will just return layer's name.
    :param act: Activation Type.
    :type act: BaseActivation
    :param bias_attr: The Bias Attribute. If no bias, then pass False or
                      something not type of ParameterAttribute. None will get a
                      default Bias.
    :type bias_attr: ParameterAttribute or None or bool
    :param layer_attr: The extra layer config. Default is None.
    :type layer_attr: ExtraLayerAttribute
    :return: MixedLayerType object can add inputs or layer name.
    :rtype: MixedLayerType
    """

    if input is None:
        return MixedLayerType(name, size, act, bias_attr, layer_attr)
    else:
Q
qijun 已提交
815 816 817 818 819 820
        with mixed_layer(
                name=name,
                size=size,
                act=act,
                bias_attr=bias_attr,
                layer_attr=layer_attr) as m:
821
            if isinstance(input, collections.Sequence):
Z
zhangjinchao01 已提交
822 823 824 825 826 827 828 829
                for each in input:
                    m += each
            else:
                m += input
        return m


@layer_support()
L
Luo Tao 已提交
830
def data_layer(name, size, height=None, width=None, layer_attr=None):
Z
zhangjinchao01 已提交
831 832 833 834 835 836 837
    """
    Define DataLayer For NeuralNetwork.

    The example usage is:

    ..  code-block:: python

Y
Yu Yang 已提交
838
        data = data_layer(name="input", size=1000)
Z
zhangjinchao01 已提交
839 840 841 842 843

    :param name: Name of this data layer.
    :type name: basestring
    :param size: Size of this data layer.
    :type size: int
L
Luo Tao 已提交
844
    :param height: Height of this data layer, used for image
Y
Yu Yang 已提交
845
    :type height: int|None
L
Luo Tao 已提交
846
    :param width: Width of this data layer, used for image
Y
Yu Yang 已提交
847
    :type width: int|None
Z
zhangjinchao01 已提交
848 849
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
850
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
851 852
    :rtype: LayerOutput
    """
Q
qijun 已提交
853 854 855 856
    Layer(
        type=LayerType.DATA,
        name=name,
        size=size,
L
Luo Tao 已提交
857 858
        height=height,
        width=width,
Q
qijun 已提交
859
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881

    return LayerOutput(name, LayerType.DATA, size=size)


@wrap_name_default("embedding")
@wrap_param_attr_default()
@layer_support(ERROR_CLIPPING)
def embedding_layer(input, size, name=None, param_attr=None, layer_attr=None):
    """
    Define a embedding Layer.

    :param name: Name of this embedding layer.
    :type name: basestring
    :param input: The input layer for this embedding. NOTE: must be Index Data.
    :type input: LayerOutput
    :param size: The embedding dimension.
    :type size: int
    :param param_attr: The embedding parameter attribute. See ParameterAttribute
                      for details.
    :type param_attr: ParameterAttribute|None
    :param layer_attr: Extra layer Config. Default is None.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
882
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
883 884
    :rtype: LayerOutput
    """
Q
qijun 已提交
885 886 887 888 889 890
    with mixed_layer(
            name=name,
            size=size,
            act=LinearActivation(),
            bias_attr=False,
            layer_attr=layer_attr) as mix:
Z
zhangjinchao01 已提交
891 892 893 894 895 896 897 898 899
        mix += table_projection(input=input, size=size, param_attr=param_attr)
    return mix


@wrap_name_default()
@wrap_param_attr_default()
@wrap_bias_attr_default()
@wrap_act_default()
@layer_support(ERROR_CLIPPING, DROPOUT)
Q
qijun 已提交
900 901 902 903 904 905 906
def fc_layer(input,
             size,
             act=None,
             name=None,
             param_attr=None,
             bias_attr=None,
             layer_attr=None):
Z
zhangjinchao01 已提交
907 908 909 910 911 912 913 914 915 916 917 918
    """
    Helper for declare fully connected layer.

    The example usage is:

    .. code-block:: python

       fc = fc_layer(input=layer,
                     size=1024,
                     act=LinearActivation(),
                     bias_attr=False)

L
luotao02 已提交
919
    which is equal to:
Z
zhangjinchao01 已提交
920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941

    .. code-block:: python

       with mixed_layer(size=1024) as fc:
           fc += full_matrix_projection(input=layer)

    :param name: The Layer Name.
    :type name: basestring
    :param input: The input layer. Could be a list/tuple of input layer.
    :type input: LayerOutput|list|tuple
    :param size: The layer dimension.
    :type size: int
    :param act: Activation Type. Default is tanh.
    :type act: BaseActivation
    :param param_attr: The Parameter Attribute|list.
    :type param_attr: ParameterAttribute
    :param bias_attr: The Bias Attribute. If no bias, then pass False or
                      something not type of ParameterAttribute. None will get a
                      default Bias.
    :type bias_attr: ParameterAttribute|None|Any
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
942
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
943 944 945 946
    :rtype: LayerOutput
    """
    if isinstance(input, LayerOutput):
        input = [input]
947
        assert not isinstance(param_attr, collections.Sequence)
Z
zhangjinchao01 已提交
948 949
        param_attr = [param_attr]
    else:
950
        if isinstance(param_attr, collections.Sequence):
Z
zhangjinchao01 已提交
951 952 953 954
            assert len(input) == len(param_attr)
        else:
            param_attr = [copy.deepcopy(param_attr) for _ in range(len(input))]

955
    assert isinstance(input, collections.Sequence)
Z
zhangjinchao01 已提交
956 957

    Layer(
Q
qijun 已提交
958 959 960
        inputs=[
            Input(ipt.name, **attr.attr) for ipt, attr in zip(input, param_attr)
        ],
Z
zhangjinchao01 已提交
961 962 963 964 965
        name=name,
        type=LayerType.FC_LAYER,
        size=size,
        bias=ParamAttr.to_bias(bias_attr),
        active_type=act.name,
Q
qijun 已提交
966 967 968
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.FC_LAYER, input, activation=act, size=size)
Z
zhangjinchao01 已提交
969

970

971 972 973 974
@wrap_name_default("print")
def print_layer(input, name=None):
    """
    Print the output value of input layers. This layer is useful for debugging.
975 976 977 978 979

    :param name: The Layer Name.
    :type name: basestring
    :param input: The input layer. Could be a list/tuple of input layer.
    :type input: LayerOutput|list|tuple
980
    :return: LayerOutput
981
    """
982 983 984 985 986
    if isinstance(input, LayerOutput):
        input = [input]
    assert isinstance(input, collections.Sequence)  # list or tuple
    for each in input:
        assert isinstance(each, LayerOutput)
987 988 989 990

    Layer(
        name=name,
        type=LayerType.PRINT_LAYER,
Q
qijun 已提交
991
        inputs=[l.name for l in input], )
992
    # this layer don't return anything, can not be input of other layer.
993

Z
zhangjinchao01 已提交
994

Y
yuan 已提交
995
@wrap_name_default("priorbox")
G
gaoyuan 已提交
996
def priorbox_layer(input,
G
gaoyuan 已提交
997
                   image,
G
gaoyuan 已提交
998 999 1000 1001 1002
                   aspect_ratio,
                   variance,
                   min_size,
                   max_size=[],
                   name=None):
Y
yuan 已提交
1003 1004 1005 1006 1007 1008 1009
    """
    Compute the priorbox and set the variance. This layer is necessary for ssd.

    :param name: The Layer Name.
    :type name: basestring
    :param input: The input layer.
    :type input: LayerOutput
G
gaoyuan 已提交
1010 1011
    :param image: The network input image.
    :type image: LayerOutput
Y
yuan 已提交
1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022
    :param aspect_ratio: The aspect ratio.
    :type aspect_ratio: list
    :param variance: The bounding box variance.
    :type min_size: The min size of the priorbox width/height.
    :param min_size: list
    :type max_size: The max size of the priorbox width/height. Could be NULL.
    :param max_size: list
    :return: LayerOutput
    """
    # plus one for ratio 1.
    num_filters = (len(aspect_ratio) * 2 + 1 + len(max_size)) * 4
G
gaoyuan 已提交
1023
    size = (input.size / input.num_filters) * num_filters * 2
Y
yuan 已提交
1024 1025 1026
    Layer(
        name=name,
        type=LayerType.PRIORBOX_LAYER,
G
gaoyuan 已提交
1027
        inputs=[input.name, image.name],
Y
yuan 已提交
1028 1029 1030 1031 1032 1033
        size=size,
        min_size=min_size,
        max_size=max_size,
        aspect_ratio=aspect_ratio,
        variance=variance)
    return LayerOutput(
G
gaoyuan 已提交
1034 1035
        name,
        LayerType.PRIORBOX_LAYER,
G
gaoyuan 已提交
1036
        parents=[input, image],
G
gaoyuan 已提交
1037 1038 1039
        num_filters=num_filters,
        size=size)

Z
zhangjinchao01 已提交
1040

1041 1042
@wrap_name_default("cross_channel_norm")
def cross_channel_norm_layer(input, name=None, param_attr=None):
G
gaoyuan 已提交
1043 1044 1045 1046 1047
    """
    Normalize a layer's output. This layer is necessary for ssd.
    This layer applys normalize across the channels of each sample to
    a conv layer's output and scale the output by a group of trainable
    factors which dimensions equal to the channel's number.
G
gaoyuan 已提交
1048

G
gaoyuan 已提交
1049 1050 1051 1052 1053 1054 1055 1056
    :param name: The Layer Name.
    :type name: basestring
    :param input: The input layer.
    :type input: LayerOutput
    :param param_attr: The Parameter Attribute|list.
    :type param_attr: ParameterAttribute
    :return: LayerOutput
    """
1057
    assert input.num_filters is not None
G
gaoyuan 已提交
1058 1059
    Layer(
        name=name,
1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
        type=LayerType.NORM_LAYER,
        inputs=[
            Input(
                input.name,
                norm=Norm(
                    norm_type="cross-channel-norm",
                    channels=input.num_filters,
                    size=input.size,
                    scale=0,
                    pow=0,
                    blocked=0),
                **param_attr.attr)
        ])
G
gaoyuan 已提交
1073 1074
    return LayerOutput(
        name,
1075
        LayerType.NORM_LAYER,
G
gaoyuan 已提交
1076 1077 1078 1079 1080
        parents=input,
        num_filters=input.num_filters,
        size=input.size)


Z
zhangjinchao01 已提交
1081 1082 1083 1084
@wrap_name_default("seq_pooling")
@wrap_bias_attr_default(has_bias=False)
@wrap_param_default(['pooling_type'], default_factory=lambda _: MaxPooling())
@layer_support()
Q
qijun 已提交
1085 1086 1087 1088
def pooling_layer(input,
                  pooling_type=None,
                  name=None,
                  bias_attr=None,
L
Luo Tao 已提交
1089
                  agg_level=AggregateLevel.TO_NO_SEQUENCE,
Z
zhangjinchao01 已提交
1090 1091 1092 1093 1094 1095 1096 1097 1098 1099
                  layer_attr=None):
    """
    Pooling layer for sequence inputs, not used for Image.

    The example usage is:

    .. code-block:: python

       seq_pool = pooling_layer(input=layer,
                                pooling_type=AvgPooling(),
L
Luo Tao 已提交
1100
                                agg_level=AggregateLevel.TO_NO_SEQUENCE)
Z
zhangjinchao01 已提交
1101

L
Luo Tao 已提交
1102 1103
    :param agg_level: AggregateLevel.TO_NO_SEQUENCE or
                      AggregateLevel.TO_SEQUENCE
Z
zhangjinchao01 已提交
1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115
    :type agg_level: AggregateLevel
    :param name: layer name.
    :type name: basestring
    :param input: input layer name.
    :type input: LayerOutput
    :param pooling_type: Type of pooling, MaxPooling(default), AvgPooling,
                         SumPooling, SquareRootNPooling.
    :type pooling_type: BasePoolingType|None
    :param bias_attr: Bias parameter attribute. False if no bias.
    :type bias_attr: ParameterAttribute|None|False
    :param layer_attr: The Extra Attributes for layer, such as dropout.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
1116
    :return: LayerOutput object.
Y
Yu Yang 已提交
1117
    :rtype: LayerOutput
Z
zhangjinchao01 已提交
1118 1119
    """
    extra_dict = dict()
1120
    # noinspection PyUnresolvedReferences
Z
zhangjinchao01 已提交
1121 1122
    if isinstance(pooling_type, AvgPooling):
        extra_dict['average_strategy'] = pooling_type.strategy
1123 1124 1125 1126
    elif isinstance(pooling_type, MaxPooling) and \
                    pooling_type.output_max_index is not None:
        assert isinstance(pooling_type.output_max_index, bool)
        extra_dict['output_max_index'] = pooling_type.output_max_index
Z
zhangjinchao01 已提交
1127 1128 1129 1130 1131 1132 1133 1134
    extra_dict.update(ExtraLayerAttribute.to_kwargs(layer_attr))

    Layer(
        name=name,
        type=pooling_type.name,
        inputs=[Input(input.name)],
        bias=ParamAttr.to_bias(bias_attr),
        trans_type=agg_level,
Q
qijun 已提交
1135
        **extra_dict)
Z
zhangjinchao01 已提交
1136

Q
qijun 已提交
1137 1138
    return LayerOutput(
        name, pooling_type.name, parents=[input], size=input.size)
Z
zhangjinchao01 已提交
1139

Q
qijun 已提交
1140

Z
zhangjinchao01 已提交
1141 1142
@wrap_bias_attr_default()
@wrap_param_attr_default()
Q
qijun 已提交
1143
@wrap_act_default(param_names=['gate_act'], act=SigmoidActivation())
Z
zhangjinchao01 已提交
1144 1145 1146
@wrap_act_default(param_names=["act", 'state_act'], act=TanhActivation())
@wrap_name_default("lstmemory")
@layer_support(DROPOUT)
Q
qijun 已提交
1147 1148 1149 1150 1151 1152 1153 1154 1155
def lstmemory(input,
              name=None,
              reverse=False,
              act=None,
              gate_act=None,
              size=None,
              state_act=None,
              bias_attr=None,
              param_attr=None,
Z
zhangjinchao01 已提交
1156 1157 1158 1159 1160 1161 1162 1163
              layer_attr=None):
    """
    Long Short-term Memory Cell.

    The memory cell was implemented as follow equations.

    ..  math::

L
luotao02 已提交
1164
        i_t & = \\sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)
Z
zhangjinchao01 已提交
1165

L
luotao02 已提交
1166
        f_t & = \\sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)
Z
zhangjinchao01 已提交
1167

L
luotao02 已提交
1168
        c_t & = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)
Z
zhangjinchao01 已提交
1169

L
luotao02 已提交
1170
        o_t & = \\sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)
Z
zhangjinchao01 已提交
1171

L
luotao02 已提交
1172
        h_t & = o_t tanh(c_t)
Z
zhangjinchao01 已提交
1173 1174


C
caoying03 已提交
1175
    NOTE: In PaddlePaddle's implementation, the multiplications
Z
zhangjinchao01 已提交
1176
    :math:`W_{xi}x_{t}` , :math:`W_{xf}x_{t}`,
C
caoying03 已提交
1177 1178 1179 1180
    :math:`W_{xc}x_t`, :math:`W_{xo}x_{t}` are not done in the lstmemory layer,
    so an additional mixed_layer with full_matrix_projection or a fc_layer must
    be included in the configuration file to complete the input-to-hidden
    mappings before lstmemory is called.
Z
zhangjinchao01 已提交
1181

C
caoying03 已提交
1182
    NOTE: This is a low level user interface. You can use network.simple_lstm
Z
zhangjinchao01 已提交
1183 1184
    to config a simple plain lstm layer.

C
caoying03 已提交
1185 1186 1187 1188
    Please refer to **Generating Sequences With Recurrent Neural Networks** for
    more details about LSTM.

    Link_ goes as below.
Z
zhangjinchao01 已提交
1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211

    .. _Link: http://arxiv.org/abs/1308.0850

    :param name: The lstmemory layer name.
    :type name: basestring
    :param input: input layer name.
    :type input: LayerOutput
    :param reverse: is sequence process reversed or not.
    :type reverse: bool
    :param act: activation type, TanhActivation by default. :math:`h_t`
    :type act: BaseActivation
    :param gate_act: gate activation type, SigmoidActivation by default.
    :type gate_act: BaseActivation
    :param state_act: state activation type, TanhActivation by default.
    :type state_act: BaseActivation

    :param bias_attr: Bias attribute. None means default bias. False means no
                      bias.
    :type bias_attr: ParameterAttribute|None|False
    :param param_attr: Parameter Attribute.
    :type param_attr: ParameterAttribute|None|False
    :param layer_attr: Extra Layer attribute
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
1212
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1213 1214 1215 1216 1217 1218
    :rtype: LayerOutput
    """

    assert gate_act.support_hppl
    assert state_act.support_hppl
    assert act.support_hppl
1219 1220 1221 1222 1223 1224 1225 1226 1227 1228
    assert input.size is not None and input.size % 4 == 0
    if size is not None:
        if input.size / 4 == size:
            plog = logger.warning
        else:
            plog = logger.fatal

        plog("NOTE: The lstmemory layer[%s]'s size is set by previous input "
             "layer. The lstm size should be equal with input layer size/4. The"
             " size which is set explicitly will be ignored." % name)
Z
zhangjinchao01 已提交
1229

Q
qijun 已提交
1230 1231 1232 1233 1234 1235 1236 1237 1238 1239
    Layer(
        name=name,
        type=LayerType.LSTMEMORY,
        active_type=act.name,
        active_state_type=state_act.name,
        active_gate_type=gate_act.name,
        reversed=reverse,
        bias=ParamAttr.to_bias(bias_attr),
        inputs=[Input(input.name, **param_attr.attr)],
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
1240

Q
qijun 已提交
1241 1242 1243 1244 1245
    return LayerOutput(
        name,
        LayerType.LSTMEMORY, [input],
        size=input.size / 4,
        reverse=reverse)
1246

Z
zhangjinchao01 已提交
1247 1248 1249

@wrap_bias_attr_default()
@wrap_param_attr_default()
Q
qijun 已提交
1250
@wrap_act_default(param_names=['gate_act'], act=SigmoidActivation())
Z
zhangjinchao01 已提交
1251 1252 1253
@wrap_act_default(param_names=["act"], act=TanhActivation())
@wrap_name_default("gru")
@layer_support(DROPOUT)
Q
qijun 已提交
1254 1255 1256 1257 1258 1259 1260 1261
def grumemory(input,
              name=None,
              reverse=False,
              act=None,
              gate_act=None,
              size=None,
              bias_attr=None,
              param_attr=None,
Z
zhangjinchao01 已提交
1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282
              layer_attr=None):
    """
    Gate Recurrent Unit Layer.

    The memory cell was implemented as follow equations.

    1. update gate :math:`z`: defines how much of the previous memory to
    keep around or the unit updates its activations. The update gate
    is computed by:

    ..  math::

        z_t = \\sigma(W_{z}x_{t} + U_{z}h_{t-1} + b_z)

    2. reset gate :math:`r`: determines how to combine the new input with the
    previous memory. The reset gate is computed similarly to the update gate:

    ..  math::

        r_t = \\sigma(W_{r}x_{t} + U_{r}h_{t-1} + b_r)

C
caoying03 已提交
1283 1284
    3. The candidate activation :math:`\\tilde{h_t}` is computed similarly to
    that of the traditional recurrent unit:
Z
zhangjinchao01 已提交
1285 1286 1287 1288 1289

    ..  math::

        {\\tilde{h_t}} = tanh(W x_{t} + U (r_{t} \odot h_{t-1}) + b)

C
caoying03 已提交
1290 1291 1292
    4. The hidden activation :math:`h_t` of the GRU at time t is a linear
    interpolation between the previous activation :math:`h_{t-1}` and the
    candidate activation :math:`\\tilde{h_t}`:
Z
zhangjinchao01 已提交
1293 1294 1295 1296 1297

    ..  math::

        h_t = (1 - z_t) h_{t-1} + z_t {\\tilde{h_t}}

C
caoying03 已提交
1298
    NOTE: In PaddlePaddle's implementation, the multiplication operations
Z
zhangjinchao01 已提交
1299
    :math:`W_{r}x_{t}`, :math:`W_{z}x_{t}` and :math:`W x_t` are not computed in
C
caoying03 已提交
1300 1301 1302
    gate_recurrent layer. Consequently, an additional mixed_layer with
    full_matrix_projection or a fc_layer must be included before grumemory
    is called.
Z
zhangjinchao01 已提交
1303

C
caoying03 已提交
1304 1305 1306
    More details can be found by referring to `Empirical Evaluation of Gated
    Recurrent Neural Networks on Sequence Modeling.
    <https://arxiv.org/abs/1412.3555>`_
Z
zhangjinchao01 已提交
1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317

    The simple usage is:

    .. code-block:: python

       gru = grumemory(input)

    :param name: The gru layer name.
    :type name: None|basestring
    :param input: input layer.
    :type input: LayerOutput.
1318
    :param reverse: Whether sequence process is reversed or not.
Z
zhangjinchao01 已提交
1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333
    :type reverse: bool
    :param act: activation type, TanhActivation by default. This activation
                affects the :math:`{\\tilde{h_t}}`.
    :type act: BaseActivation
    :param gate_act: gate activation type, SigmoidActivation by default.
                     This activation affects the :math:`z_t` and :math:`r_t`. It is the
                     :math:`\\sigma` in the above formula.
    :type gate_act: BaseActivation
    :param bias_attr: Bias attribute. None means default bias. False means no
                      bias.
    :type bias_attr: ParameterAttribute|None|False
    :param param_attr: Parameter Attribute.
    :type param_attr: ParameterAttribute|None|False
    :param layer_attr: Extra Layer attribute
    :type layer_attr: ExtraLayerAttribute|None
1334 1335 1336
    :param size: Stub parameter of size, but actually not used. If set this size
                 will get a warning.
    :type size: None
D
dangqingqing 已提交
1337
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1338 1339 1340 1341
    :rtype: LayerOutput
    """
    assert act.support_hppl
    assert gate_act.support_hppl
1342 1343 1344 1345 1346 1347 1348 1349 1350
    assert input.size is not None and input.size % 3 == 0
    if size is not None:
        if input.size / 3 == size:
            plog = logger.warning
        else:
            plog = logger.fatal
        plog("NOTE: the gru memory layer's size is set by previous input layer,"
             " and should be input size / 3. Set size explicitly will be "
             "ignored.")
Z
zhangjinchao01 已提交
1351

Q
qijun 已提交
1352 1353 1354 1355 1356 1357 1358 1359 1360
    Layer(
        name=name,
        type=LayerType.GRUMEMORY,
        active_type=act.name,
        active_gate_type=gate_act.name,
        reversed=reverse,
        bias=ParamAttr.to_bias(bias_attr),
        inputs=[Input(input.name, **param_attr.attr)],
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
1361

Q
qijun 已提交
1362 1363 1364 1365 1366
    return LayerOutput(
        name,
        LayerType.GRUMEMORY, [input],
        size=input.size / 3,
        reverse=reverse)
1367

Z
zhangjinchao01 已提交
1368 1369 1370

@wrap_name_default()
@layer_support()
Q
qijun 已提交
1371 1372
def last_seq(input,
             name=None,
L
Luo Tao 已提交
1373
             agg_level=AggregateLevel.TO_NO_SEQUENCE,
1374
             stride=-1,
Z
zhangjinchao01 已提交
1375 1376 1377 1378
             layer_attr=None):
    """
    Get Last Timestamp Activation of a sequence.

1379 1380 1381
    If stride > 0, this layer slides a window whose size is determined by stride,
    and return the last value of the window as the output. Thus, a long sequence
    will be shorten. Note that for sequence with sub-sequence, the default value
L
Luo Tao 已提交
1382
    of stride is -1.
1383

L
Luo Tao 已提交
1384 1385 1386 1387 1388 1389
    The simple usage is:

    .. code-block:: python

       seq = last_seq(input=layer)

Z
zhangjinchao01 已提交
1390 1391 1392 1393 1394
    :param agg_level: Aggregated level
    :param name: Layer name.
    :type name: basestring
    :param input: Input layer name.
    :type input: LayerOutput
1395
    :param stride: window size.
1396
    :type stride: Int
Z
zhangjinchao01 已提交
1397 1398
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
1399
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1400 1401
    :rtype: LayerOutput
    """
1402 1403 1404 1405 1406 1407
    if input.reverse is not None and input.reverse:
        logger.warning("You are getting the last instance of a sequence that"
                       " is a output of a REVERSED layer. There is no time"
                       " series information at all. Maybe you want to use"
                       " first_seq instead.")

L
Luo Tao 已提交
1408
    if agg_level == AggregateLevel.TO_SEQUENCE:
1409 1410
        assert stride == -1

Z
zhangjinchao01 已提交
1411 1412 1413 1414 1415
    Layer(
        name=name,
        type=LayerType.SEQUENCE_LAST_INSTANCE,
        inputs=[input.name],
        trans_type=agg_level,
1416
        stride=stride,
Q
qijun 已提交
1417 1418 1419 1420 1421 1422
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name,
        LayerType.SEQUENCE_LAST_INSTANCE,
        parents=[input],
        size=input.size)
Z
zhangjinchao01 已提交
1423 1424 1425 1426


@wrap_name_default()
@layer_support()
Q
qijun 已提交
1427 1428
def first_seq(input,
              name=None,
L
Luo Tao 已提交
1429
              agg_level=AggregateLevel.TO_NO_SEQUENCE,
1430
              stride=-1,
Z
zhangjinchao01 已提交
1431 1432 1433 1434
              layer_attr=None):
    """
    Get First Timestamp Activation of a sequence.

1435 1436 1437
    If stride > 0, this layer slides a window whose size is determined by stride,
    and return the first value of the window as the output. Thus, a long sequence
    will be shorten. Note that for sequence with sub-sequence, the default value
L
Luo Tao 已提交
1438
    of stride is -1.
1439

L
Luo Tao 已提交
1440 1441 1442 1443 1444 1445
    The simple usage is:

    .. code-block:: python

       seq = first_seq(input=layer)

Z
zhangjinchao01 已提交
1446 1447 1448 1449 1450
    :param agg_level: aggregation level
    :param name: Layer name.
    :type name: basestring
    :param input: Input layer name.
    :type input: LayerOutput
1451
    :param stride: window size.
1452
    :type stride: Int
Z
zhangjinchao01 已提交
1453 1454
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
1455
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1456 1457
    :rtype: LayerOutput
    """
1458 1459 1460 1461 1462 1463 1464

    if input.reverse is not None and not input.reverse:
        logger.warning('You are getting the first instance for a time series,'
                       ' and it is a normal recurrent layer output. There is no'
                       ' time series information at all. Maybe you want to use'
                       ' last_seq instead.')

L
Luo Tao 已提交
1465
    if agg_level == AggregateLevel.TO_SEQUENCE:
1466 1467
        assert stride == -1

Z
zhangjinchao01 已提交
1468 1469 1470 1471 1472
    Layer(
        name=name,
        type=LayerType.SEQUENCE_FIRST_INSTANCE,
        inputs=[input.name],
        trans_type=agg_level,
1473
        stride=stride,
Q
qijun 已提交
1474 1475 1476 1477 1478 1479
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name,
        LayerType.SEQUENCE_FIRST_INSTANCE,
        parents=[input],
        size=input.size)
Z
zhangjinchao01 已提交
1480 1481 1482


class ExpandLevel(object):
1483 1484 1485 1486 1487
    """
    Please refer to AggregateLevel first.

    ExpandLevel supports two modes:

L
Luo Tao 已提交
1488 1489
    - :code:`ExpandLevel.FROM_NO_SEQUENCE` means the expansion acts on
      :code:`NO_SEQUENCE`, which will be expanded to
1490 1491
      :code:`SEQUENCE` or :code:`SUB_SEQUENCE`.

L
Luo Tao 已提交
1492 1493
    - :code:`ExpandLevel.FROM_SEQUENCE` means the expansion acts on
      :code:`SEQUENCE`, which will be expanded to
1494 1495
      :code:`SUB_SEQUENCE`.
    """
L
Luo Tao 已提交
1496 1497
    FROM_NO_SEQUENCE = AggregateLevel.TO_NO_SEQUENCE
    FROM_SEQUENCE = AggregateLevel.TO_SEQUENCE
1498 1499
    # compatible with previous configuration
    FROM_TIMESTEP = FROM_NO_SEQUENCE
Z
zhangjinchao01 已提交
1500

1501

Z
zhangjinchao01 已提交
1502 1503
@wrap_name_default()
@layer_support()
Q
qijun 已提交
1504 1505
def expand_layer(input,
                 expand_as,
Z
zhangjinchao01 已提交
1506 1507
                 name=None,
                 bias_attr=False,
L
Luo Tao 已提交
1508
                 expand_level=ExpandLevel.FROM_NO_SEQUENCE,
Z
zhangjinchao01 已提交
1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519
                 layer_attr=None):
    """
    A layer for "Expand Dense data or (sequence data where the length of each
    sequence is one) to sequence data."

    The example usage is:

    .. code-block:: python

       expand = expand_layer(input=layer1,
                             expand_as=layer2,
L
Luo Tao 已提交
1520
                             expand_level=ExpandLevel.FROM_NO_SEQUENCE)
Z
zhangjinchao01 已提交
1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534

    :param input: Input layer
    :type input: LayerOutput
    :param expand_as: Expand as this layer's sequence info.
    :type expand_as: LayerOutput
    :param name: Layer name.
    :type name: basestring
    :param bias_attr: Bias attribute. None means default bias. False means no
                      bias.
    :type bias_attr: ParameterAttribute|None|False
    :param expand_level: whether input layer is timestep(default) or sequence.
    :type expand_level: ExpandLevel
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
1535
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1536 1537 1538 1539 1540 1541 1542 1543 1544
    :rtype: LayerOutput
    """

    Layer(
        inputs=[input.name, expand_as.name],
        name=name,
        bias=ParamAttr.to_bias(bias_attr=bias_attr),
        type=LayerType.EXPAND_LAYER,
        trans_type=expand_level,
Q
qijun 已提交
1545 1546 1547 1548 1549 1550
        **ExtraAttr.to_kwargs(layer_attr))
    return LayerOutput(
        name=name,
        size=input.size,
        layer_type=LayerType.EXPAND_LAYER,
        parents=[input, expand_as])
Z
zhangjinchao01 已提交
1551 1552


X
xuwei06 已提交
1553 1554
@wrap_name_default()
@layer_support()
Q
qijun 已提交
1555
def repeat_layer(input, num_repeats, name=None, layer_attr=None):
X
xuwei06 已提交
1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566
    """
    A layer for repeating the input for num_repeats times. This is equivalent
    to apply concat_layer() with num_repeats same input.

    .. math::
       y  = [x, x, \cdots, x]

    The example usage is:

    .. code-block:: python

L
Luo Tao 已提交
1567
       expand = repeat_layer(input=layer, num_repeats=4)
X
xuwei06 已提交
1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585

    :param input: Input layer
    :type input: LayerOutput
    :param num_repeats: Repeat the input so many times
    :type num_repeats: int
    :param name: Layer name.
    :type name: basestring
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
    :return: LayerOutput object.
    :rtype: LayerOutput
    """

    l = Layer(
        inputs=[input.name],
        name=name,
        num_filters=num_repeats,
        type=LayerType.FEATURE_MAP_EXPAND_LAYER,
Q
qijun 已提交
1586 1587 1588 1589 1590 1591 1592
        **ExtraAttr.to_kwargs(layer_attr))
    return LayerOutput(
        name=name,
        size=l.config.size,
        layer_type=LayerType.FEATURE_MAP_EXPAND_LAYER,
        parents=[input])

X
xuwei06 已提交
1593

1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605
@wrap_name_default("seqreshape")
@wrap_act_default(act=IdentityActivation())
@wrap_bias_attr_default(has_bias=False)
@layer_support()
def seq_reshape_layer(input,
                      reshape_size,
                      act=None,
                      name=None,
                      layer_attr=None,
                      bias_attr=None):
    """
    A layer for reshaping the sequence. Assume the input sequence has T instances,
1606
    the dimension of each instance is M, and the input reshape_size is N, then the
1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648
    output sequence has T*M/N instances, the dimension of each instance is N.

    Note that T*M/N must be an integer.

    The example usage is:

    .. code-block:: python

       reshape = seq_reshape_layer(input=layer, reshape_size=4)

    :param input: Input layer.
    :type input: LayerOutput
    :param reshape_size: the size of reshaped sequence.
    :type reshape_size: int
    :param name: Layer name.
    :type name: basestring
    :param act: Activation type.
    :type act: BaseActivation
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
    :param bias_attr: The Bias Attribute. If no bias, then pass False or
                      something not type of ParameterAttribute. None will get a
                      default Bias.
    :type bias_attr: ParameterAttribute or None or bool
    :return: LayerOutput object.
    :rtype: LayerOutput
    """

    Layer(
        inputs=[input.name],
        name=name,
        size=reshape_size,
        type=LayerType.SEQUENCE_RESHAPE,
        bias=ParamAttr.to_bias(bias_attr),
        **ExtraAttr.to_kwargs(layer_attr))
    return LayerOutput(
        name=name,
        size=reshape_size,
        layer_type=LayerType.SEQUENCE_RESHAPE,
        parents=[input])


Z
zhangjinchao01 已提交
1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676
@wrap_name_default()
@layer_support()
def interpolation_layer(input, weight, name=None, layer_attr=None):
    """
    This layer is for linear interpolation with two inputs,
    which is used in NEURAL TURING MACHINE.

    .. math::
       y.row[i] = w[i] * x_1.row[i] + (1 - w[i]) * x_2.row[i]

    where :math:`x_1` and :math:`x_2` are two (batchSize x dataDim) inputs,
    :math:`w` is (batchSize x 1) weight vector, and :math:`y` is
    (batchSize x dataDim) output.

    The example usage is:

    .. code-block:: python

       interpolation = interpolation_layer(input=[layer1, layer2], weight=layer3)

    :param input: Input layer.
    :type input: list|tuple
    :param weight: Weight layer.
    :type weight: LayerOutput
    :param name: Layer name.
    :type name: basestring
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
1677
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1678 1679
    :rtype: LayerOutput
    """
1680
    assert isinstance(input, collections.Sequence)
Z
zhangjinchao01 已提交
1681
    assert len(input) == 2
1682 1683 1684 1685 1686 1687 1688
    assert isinstance(input[0], LayerOutput) and isinstance(input[1],
                                                            LayerOutput)
    if input[0].size is not None and input[1].size is not None:
        assert input[0].size == input[1].size
    assert isinstance(weight, LayerOutput)
    if weight.size is not None:
        assert weight.size == 1
Z
zhangjinchao01 已提交
1689 1690 1691 1692
    Layer(
        name=name,
        type=LayerType.INTERPOLATION_LAYER,
        inputs=[weight.name, input[0].name, input[1].name],
Q
qijun 已提交
1693 1694 1695 1696 1697 1698
        **ExtraAttr.to_kwargs(layer_attr))
    return LayerOutput(
        name,
        LayerType.INTERPOLATION_LAYER,
        parents=[weight, input[0], input[1]],
        size=input[0].size)
Z
zhangjinchao01 已提交
1699 1700


L
liaogang 已提交
1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716
@wrap_name_default()
@layer_support()
def bilinear_interp_layer(input,
                          out_size_x=None,
                          out_size_y=None,
                          name=None,
                          layer_attr=None):
    """
    This layer is to implement bilinear interpolation on conv layer output.

    Please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation

    The simple usage is:

    .. code-block:: python

L
liaogang 已提交
1717
       bilinear = bilinear_interp_layer(input=layer1, out_size_x=64, out_size_y=64)
X
xuwei06 已提交
1718

L
liaogang 已提交
1719
    :param   input:        A input layer.
L
liaogang 已提交
1720
    :type    input:        LayerOutput.
L
liaogang 已提交
1721
    :param   out_size_x:   bilinear interpolation output width.
X
xuwei06 已提交
1722
    :type    out_size_x:   int|None
L
liaogang 已提交
1723
    :param   out_size_y:   bilinear interpolation output height.
L
liaogang 已提交
1724
    :type    out_size_y:   int|None
L
liaogang 已提交
1725
    :param   name:         The layer's name, which cna not be specified.
L
liaogang 已提交
1726
    :type    name:         None|basestring
L
liaogang 已提交
1727
    :param   layer_attr:   Extra Layer attribute.
L
liaogang 已提交
1728 1729 1730 1731 1732 1733 1734
    :type    layer_attr:   ExtraLayerAttribute
    :return: LayerOutput object.
    :rtype:  LayerOutput
    """
    assert input.layer_type == LayerType.CONV_LAYER
    assert isinstance(input.activation, LinearActivation)
    assert out_size_x > 0 and out_size_y > 0
L
liaogang 已提交
1735
    assert input.num_filters is not None
L
liaogang 已提交
1736
    num_channels = input.num_filters
Q
qijun 已提交
1737 1738 1739 1740 1741 1742 1743
    l = Layer(
        name=name,
        inputs=Input(
            input.name,
            bilinear_interp=BilinearInterp(
                out_size_x=out_size_x,
                out_size_y=out_size_y,
L
Luo Tao 已提交
1744
                channels=num_channels)),
Q
qijun 已提交
1745 1746 1747 1748 1749 1750 1751 1752 1753
        type=LayerType.BILINEAR_INTERP_LAYER,
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name,
        LayerType.BILINEAR_INTERP_LAYER,
        parents=[input],
        num_filters=num_channels,
        size=l.config.size)

L
liaogang 已提交
1754

Z
zhangjinchao01 已提交
1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781
@wrap_name_default()
@layer_support()
def power_layer(input, weight, name=None, layer_attr=None):
    """
    This layer applies a power function to a vector element-wise,
    which is used in NEURAL TURING MACHINE.

    .. math::
       y = x^w

    where :math:`x` is a input vector, :math:`w` is scalar weight,
    and :math:`y` is a output vector.

    The example usage is:

    .. code-block:: python

       power = power_layer(input=layer1, weight=layer2)

    :param input: Input layer.
    :type input: LayerOutput
    :param weight: Weight layer.
    :type weight: LayerOutput
    :param name: Layer name.
    :type name: basestring
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
1782
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1783 1784
    :rtype: LayerOutput
    """
1785 1786 1787
    assert isinstance(input, LayerOutput) and isinstance(weight, LayerOutput)
    if weight.size is not None:
        assert weight.size == 1
Z
zhangjinchao01 已提交
1788 1789 1790
    Layer(
        name=name,
        type=LayerType.POWER_LAYER,
1791
        inputs=[weight.name, input.name],
Q
qijun 已提交
1792 1793 1794
        **ExtraAttr.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.POWER_LAYER, parents=[input, weight], size=input.size)
Z
zhangjinchao01 已提交
1795 1796 1797 1798 1799 1800


@wrap_name_default()
@layer_support()
def scaling_layer(input, weight, name=None, layer_attr=None):
    """
1801
    A layer for multiplying input vector by weight scalar.
Z
zhangjinchao01 已提交
1802 1803

    .. math::
1804
       y  = w x
Z
zhangjinchao01 已提交
1805

1806 1807 1808 1809 1810
    where :math:`x` is size=dataDim input, :math:`w` is size=1 weight,
    and :math:`y` is size=dataDim output.

    Note that the above computation is for one sample. Multiple samples are
    processed in one batch.
Z
zhangjinchao01 已提交
1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825

    The example usage is:

    .. code-block:: python

       scale = scaling_layer(input=layer1, weight=layer2)

    :param input: Input layer.
    :type input: LayerOutput
    :param weight: Weight layer.
    :type weight: LayerOutput
    :param name: Layer name.
    :type name: basestring
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
1826
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1827 1828
    :rtype: LayerOutput
    """
1829 1830 1831
    assert isinstance(weight, LayerOutput) and isinstance(input, LayerOutput)
    if weight.size is not None:
        assert weight.size == 1
Z
zhangjinchao01 已提交
1832 1833 1834 1835
    Layer(
        name=name,
        type=LayerType.SCALING_LAYER,
        inputs=[weight.name, input.name],
Q
qijun 已提交
1836 1837 1838
        **ExtraAttr.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.SCALING_LAYER, parents=[weight, input], size=input.size)
Z
zhangjinchao01 已提交
1839 1840 1841 1842 1843 1844


@wrap_name_default()
@layer_support()
def trans_layer(input, name=None, layer_attr=None):
    """
1845
    A layer for transposing a minibatch matrix.
Z
zhangjinchao01 已提交
1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863

    .. math::
       y = x^\mathrm{T}

    where :math:`x` is (M x N) input, and :math:`y` is (N x M) output.

    The example usage is:

    .. code-block:: python

       trans = trans_layer(input=layer)

    :param input: Input layer.
    :type input: LayerOutput
    :param name: Layer name.
    :type name: basestring
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
1864
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1865 1866 1867 1868 1869 1870
    :rtype: LayerOutput
    """
    Layer(
        name=name,
        type=LayerType.TRANS_LAYER,
        inputs=[input.name],
Q
qijun 已提交
1871 1872 1873
        **ExtraAttr.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.TRANS_LAYER, parents=[input], size=input.size)
Z
zhangjinchao01 已提交
1874 1875


1876 1877
@wrap_name_default()
@layer_support()
H
Haonan 已提交
1878
def rotate_layer(input, height, width, name=None, layer_attr=None):
1879
    """
H
Haonan 已提交
1880 1881
    A layer for rotating 90 degrees (clock-wise) for each feature channel,
    usually used when the input sample is some image or feature map.
1882 1883

    .. math::
H
Haonan 已提交
1884
       y(j,i,:) = x(M-i-1,j,:)
1885

H
Haonan 已提交
1886
    where :math:`x` is (M x N x C) input, and :math:`y` is (N x M x C) output.
1887 1888 1889 1890 1891 1892

    The example usage is:

    .. code-block:: python

       rot = rotate_layer(input=layer,
H
Haonan 已提交
1893 1894
                          height=100,
                          width=100)
1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907

    :param input: Input layer.
    :type input: LayerOutput
    :param height: The height of the sample matrix
    :type height: int
    :param name: Layer name.
    :type name: basestring
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
    :return: LayerOutput object.
    :rtype: LayerOutput
    """
    assert isinstance(input, LayerOutput)
H
Haonan 已提交
1908 1909 1910
    l = Layer(
        name=name,
        height=height,
H
Haonan 已提交
1911
        width=width,
H
Haonan 已提交
1912 1913 1914 1915 1916 1917 1918 1919
        type=LayerType.ROTATE_LAYER,
        inputs=[input.name],
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name=name,
        layer_type=LayerType.ROTATE_LAYER,
        parents=[input],
        size=l.config.size)
1920 1921


Z
zhangjinchao01 已提交
1922 1923
@wrap_name_default()
@layer_support()
1924
def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None):
Z
zhangjinchao01 已提交
1925 1926 1927 1928
    """
    Cosine Similarity Layer. The cosine similarity equation is here.

    ..  math::
D
dangqingqing 已提交
1929
        similarity = cos(\\theta) = {\\mathbf{a} \\cdot \\mathbf{b}
1930 1931 1932 1933 1934
        \\over \\|\\mathbf{a}\\| \\|\\mathbf{b}\\|}

    The size of a is M, size of b is M*N,
    Similarity will be calculated N times by step M. The output size is
    N. The scale will be multiplied to similarity.
Z
zhangjinchao01 已提交
1935

1936 1937
    Note that the above computation is for one sample. Multiple samples are
    processed in one batch.
Z
zhangjinchao01 已提交
1938

L
Luo Tao 已提交
1939 1940 1941 1942 1943 1944
    The example usage is:

    .. code-block:: python

       cos = cos_sim(a=layer1, b=layer2, size=3)

Z
zhangjinchao01 已提交
1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956
    :param name: layer name
    :type name: basestring
    :param a: input layer a
    :type a: LayerOutput
    :param b: input layer b
    :type b: LayerOutput
    :param scale: scale for cosine value. default is 5.
    :type scale: float
    :param size: layer size. NOTE size_a * size should equal size_b.
    :type size: int
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
1957
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1958 1959
    :rtype: LayerOutput
    """
1960
    assert isinstance(a, LayerOutput) and isinstance(b, LayerOutput)
1961 1962 1963 1964 1965 1966
    if size == 1:
        Layer(
            name=name,
            type=LayerType.COSINE_SIM,
            cos_scale=scale,
            inputs=[a.name, b.name],
Q
qijun 已提交
1967
            **ExtraLayerAttribute.to_kwargs(layer_attr))
1968
    else:
1969 1970
        if a.size is not None and b.size is not None:
            assert size == b.size / a.size
1971 1972 1973 1974 1975 1976
        Layer(
            name=name,
            type=LayerType.COSINE_SIM_VEC,
            size=size,
            cos_scale=scale,
            inputs=[a.name, b.name],
Q
qijun 已提交
1977
            **ExtraLayerAttribute.to_kwargs(layer_attr))
X
xuwei06 已提交
1978
    return LayerOutput(name, LayerType.COSINE_SIM, parents=[a, b], size=size)
Z
zhangjinchao01 已提交
1979

1980

Z
zhangjinchao01 已提交
1981 1982
@wrap_name_default()
@wrap_bias_attr_default(has_bias=True)
1983
@wrap_param_attr_default()
Z
zhangjinchao01 已提交
1984
@layer_support()
Q
qijun 已提交
1985 1986
def hsigmoid(input,
             label,
1987
             num_classes=None,
Q
qijun 已提交
1988 1989 1990 1991
             name=None,
             bias_attr=None,
             param_attr=None,
             layer_attr=None):
Z
zhangjinchao01 已提交
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
    """
    Organize the classes into a binary tree. At each node, a sigmoid function
    is used to calculate the probability of belonging to the right branch.
    This idea is from "F. Morin, Y. Bengio (AISTATS 05):
    Hierarchical Probabilistic Neural Network Language Model."

    The example usage is:

    ..  code-block:: python

        cost = hsigmoid(input=[layer1, layer2],
2003
                        label=data_layer)
Z
zhangjinchao01 已提交
2004 2005 2006 2007 2008 2009 2010

    :param input: Input layers. It could be a LayerOutput or list/tuple of
                 LayerOutput.
    :type input: LayerOutput|list|tuple
    :param label: Label layer.
    :type label: LayerOutput
    :param num_classes: number of classes.
2011
    :type num_classes: int|None
L
luotao02 已提交
2012 2013
    :param name: layer name
    :type name: basestring
Z
zhangjinchao01 已提交
2014 2015 2016
    :param bias_attr: Bias attribute. None means default bias.
                      False means no bias.
    :type bias_attr: ParameterAttribute|False
2017 2018
    :param param_attr: Parameter Attribute. None means default parameter.
    :type param_attr: ParameterAttribute|None
Z
zhangjinchao01 已提交
2019 2020
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
2021
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2022 2023 2024 2025
    :rtype: LayerOutput
    """
    if isinstance(input, LayerOutput):
        input = [input]
2026 2027 2028 2029 2030 2031 2032 2033 2034
        if not isinstance(param_attr, collections.Sequence):
            param_attr = [param_attr]
    else:
        if not isinstance(param_attr, collections.Sequence):
            param_attr = [param_attr] * len(input)
        else:
            assert len(param_attr) == len(input)

    assert isinstance(input, collections.Sequence)
Z
zhangjinchao01 已提交
2035 2036 2037
    assert isinstance(label, LayerOutput)
    assert label.layer_type == LayerType.DATA

2038 2039 2040 2041 2042
    if num_classes is None:
        num_classes = label.size
    if num_classes is None or num_classes <= 2:
        raise ValueError("hsigmoid label size must larger than 2.")

Z
zhangjinchao01 已提交
2043 2044
    ipts_for_layer = []
    parents = []
2045
    for each_input, each_param_attr in zip(input, param_attr):
Z
zhangjinchao01 已提交
2046
        assert isinstance(each_input, LayerOutput)
2047
        ipts_for_layer.append(Input(each_input.name, **each_param_attr.attr))
Z
zhangjinchao01 已提交
2048 2049 2050 2051
        parents.append(each_input)
    ipts_for_layer.append(label.name)
    parents.append(label)

X
xuwei06 已提交
2052
    l = Layer(
Z
zhangjinchao01 已提交
2053 2054 2055 2056 2057
        name=name,
        type=LayerType.HSIGMOID,
        num_classes=num_classes,
        bias=ParamAttr.to_bias(bias_attr),
        inputs=ipts_for_layer,
Q
qijun 已提交
2058 2059 2060
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.HSIGMOID, parents=parents, size=l.config.size)
Z
zhangjinchao01 已提交
2061

2062

Z
zhangjinchao01 已提交
2063 2064 2065 2066 2067
@wrap_name_default("conv")
@wrap_param_attr_default()
@wrap_bias_attr_default()
@wrap_act_default(act=ReluActivation())
@layer_support(DROPOUT)
Q
qijun 已提交
2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083
def img_conv_layer(input,
                   filter_size,
                   num_filters,
                   name=None,
                   num_channels=None,
                   act=None,
                   groups=1,
                   stride=1,
                   padding=0,
                   bias_attr=None,
                   param_attr=None,
                   shared_biases=True,
                   layer_attr=None,
                   filter_size_y=None,
                   stride_y=None,
                   padding_y=None,
2084 2085
                   trans=False,
                   layer_type=None):
Z
zhangjinchao01 已提交
2086
    """
2087
    Convolution layer for image. Paddle can support both square and non-square
2088
    input currently.
Z
zhangjinchao01 已提交
2089 2090 2091 2092

    The details of convolution layer, please refer UFLDL's `convolution
    <http://ufldl.stanford.edu/tutorial/supervised/
    FeatureExtractionUsingConvolution/>`_ .
X
xuwei06 已提交
2093

2094
    Convolution Transpose (deconv) layer for image. Paddle can support both square
2095
    and non-square input currently.
2096

X
xuwei06 已提交
2097
    The details of convolution transpose layer,
2098 2099 2100
    please refer to the following explanation and references therein
    <http://datascience.stackexchange.com/questions/6107/
    what-are-deconvolutional-layers/>`_ .
Z
zhangjinchao01 已提交
2101 2102 2103 2104
    The num_channel means input image's channel number. It may be 1 or 3 when
    input is raw pixels of image(mono or RGB), or it may be the previous layer's
    num_filters * num_group.

C
caoying03 已提交
2105 2106 2107
    There are several group of filter in PaddlePaddle implementation.
    Each group will process some channel of the inputs. For example, if an input
    num_channel = 256, group = 4, num_filter=32, the PaddlePaddle will create
Z
zhangjinchao01 已提交
2108
    32*4 = 128 filters to process inputs. The channels will be split into 4
C
caoying03 已提交
2109 2110
    pieces. First 256/4 = 64 channels will process by first 32 filters. The
    rest channels will be processed by rest group of filters.
Z
zhangjinchao01 已提交
2111

L
Luo Tao 已提交
2112 2113 2114 2115 2116 2117 2118 2119 2120 2121
    The example usage is:

    ..  code-block:: python

        conv = img_conv_layer(input=data, filter_size=1, filter_size_y=1,
                              num_channels=8,
                              num_filters=16, stride=1,
                              bias_attr=False,
                              act=ReluActivation())

Z
zhangjinchao01 已提交
2122 2123 2124 2125
    :param name: Layer name.
    :type name: basestring
    :param input: Layer Input.
    :type input: LayerOutput
2126 2127 2128
    :param filter_size: The x dimension of a filter kernel. Or input a tuple for
                        two image dimension.
    :type filter_size: int|tuple|list
C
caoying03 已提交
2129 2130 2131
    :param filter_size_y: The y dimension of a filter kernel. Since PaddlePaddle
                        currently supports rectangular filters, the filter's
                        shape will be (filter_size, filter_size_y).
2132
    :type filter_size_y: int|None
Z
zhangjinchao01 已提交
2133 2134 2135 2136 2137
    :param num_filters: Each filter group's number of filter
    :param act: Activation type. Default is tanh
    :type act: BaseActivation
    :param groups: Group size of filters.
    :type groups: int
2138 2139 2140
    :param stride: The x dimension of the stride. Or input a tuple for two image
                   dimension.
    :type stride: int|tuple|list
Z
zhangjinchao01 已提交
2141 2142
    :param stride_y: The y dimension of the stride.
    :type stride_y: int
2143 2144 2145
    :param padding: The x dimension of the padding. Or input a tuple for two
                    image dimension
    :type padding: int|tuple|list
Z
zhangjinchao01 已提交
2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159
    :param padding_y: The y dimension of the padding.
    :type padding_y: int
    :param bias_attr: Convolution bias attribute. None means default bias.
                      False means no bias.
    :type bias_attr: ParameterAttribute|False
    :param num_channels: number of input channels. If None will be set
                        automatically from previous output.
    :type num_channels: int
    :param param_attr: Convolution param attribute. None means default attribute
    :type param_attr: ParameterAttribute
    :param shared_biases: Is biases will be shared between filters or not.
    :type shared_biases: bool
    :param layer_attr: Layer Extra Attribute.
    :type layer_attr: ExtraLayerAttribute
2160 2161
    :param trans: true if it is a convTransLayer, false if it is a convLayer
    :type trans: bool
2162
    :param layer_type: specify the layer_type, default is None. If trans=True,
2163 2164
                       layer_type has to be "exconvt" or "cudnn_convt",
                       otherwise layer_type has to be either "exconv" or
2165
                       "cudnn_conv"
2166
    :type layer_type: String
D
dangqingqing 已提交
2167
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2168 2169 2170 2171 2172
    :rtype: LayerOutput
    """
    if num_channels is None:
        assert input.num_filters is not None
        num_channels = input.num_filters
2173

Z
zhangjinchao01 已提交
2174
    if filter_size_y is None:
2175 2176 2177 2178 2179 2180
        if isinstance(filter_size, collections.Sequence):
            assert len(filter_size) == 2
            filter_size, filter_size_y = filter_size
        else:
            filter_size_y = filter_size

Z
zhangjinchao01 已提交
2181
    if stride_y is None:
2182 2183 2184 2185 2186 2187
        if isinstance(stride, collections.Sequence):
            assert len(stride) == 2
            stride, stride_y = stride
        else:
            stride_y = stride

Z
zhangjinchao01 已提交
2188
    if padding_y is None:
2189 2190 2191 2192 2193 2194 2195 2196
        if isinstance(padding, collections.Sequence):
            assert len(padding) == 2
            padding, padding_y = padding
        else:
            padding_y = padding

    if param_attr.attr.get('initial_smart'):
        # special initial for conv layers.
Q
qijun 已提交
2197
        init_w = (2.0 / (filter_size**2 * num_channels))**0.5
2198 2199 2200 2201
        param_attr.attr["initial_mean"] = 0.0
        param_attr.attr["initial_std"] = init_w
        param_attr.attr["initial_strategy"] = 0
        param_attr.attr["initial_smart"] = False
2202

2203 2204
    if layer_type:
        if trans:
2205
            assert layer_type in ["exconvt", "cudnn_convt"]
2206 2207 2208 2209 2210
        else:
            assert layer_type in ["exconv", "cudnn_conv"]
        lt = layer_type
    else:
        lt = LayerType.CONVTRANS_LAYER if trans else LayerType.CONV_LAYER
Q
qijun 已提交
2211

X
xuwei06 已提交
2212
    l = Layer(
Z
zhangjinchao01 已提交
2213
        name=name,
Q
qijun 已提交
2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225
        inputs=Input(
            input.name,
            conv=Conv(
                filter_size=filter_size,
                padding=padding,
                stride=stride,
                channels=num_channels,
                groups=groups,
                filter_size_y=filter_size_y,
                padding_y=padding_y,
                stride_y=stride_y),
            **param_attr.attr),
Z
zhangjinchao01 已提交
2226 2227 2228 2229
        active_type=act.name,
        num_filters=num_filters,
        bias=ParamAttr.to_bias(bias_attr),
        shared_biases=shared_biases,
2230
        type=lt,
Q
qijun 已提交
2231 2232 2233 2234 2235 2236 2237 2238
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name,
        lt,
        parents=[input],
        activation=act,
        num_filters=num_filters,
        size=l.config.size)
Z
zhangjinchao01 已提交
2239 2240 2241 2242


@wrap_name_default("pool")
@layer_support()
Q
qijun 已提交
2243 2244 2245 2246 2247 2248 2249 2250 2251 2252
def img_pool_layer(input,
                   pool_size,
                   name=None,
                   num_channels=None,
                   pool_type=None,
                   stride=1,
                   padding=0,
                   layer_attr=None,
                   pool_size_y=None,
                   stride_y=None,
2253 2254
                   padding_y=None,
                   ceil_mode=True):
Z
zhangjinchao01 已提交
2255 2256 2257 2258 2259 2260 2261
    """
    Image pooling Layer.

    The details of pooling layer, please refer ufldl's pooling_ .

    .. _pooling: http://ufldl.stanford.edu/tutorial/supervised/Pooling/

L
Luo Tao 已提交
2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289
    - ceil_mode=True:

    ..  math::

        w = 1 + int(ceil(input\_width + 2 * padding - pool\_size) / float(stride))
        h = 1 + int(ceil(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))

    - ceil_mode=False:

    ..  math::

        w = 1 + int(floor(input\_width + 2 * padding - pool\_size) / float(stride))
        h = 1 + int(floor(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))

    The example usage is:

    ..  code-block:: python

        maxpool = img_pool_layer(input=conv,
                                 pool_size=3,
                                 pool_size_y=5,
                                 num_channels=8,
                                 stride=1,
                                 stride_y=2,
                                 padding=1,
                                 padding_y=2,
                                 pool_type=MaxPooling())

2290
    :param padding: pooling padding width.
Z
zhangjinchao01 已提交
2291
    :type padding: int
2292 2293
    :param padding_y: pooling padding height. It's equal to padding by default.
    :type padding_y: int|None
Z
zhangjinchao01 已提交
2294 2295 2296 2297
    :param name: name of pooling layer
    :type name: basestring.
    :param input: layer's input
    :type input: LayerOutput
2298
    :param pool_size: pooling window width
Z
zhangjinchao01 已提交
2299
    :type pool_size: int
2300 2301
    :param pool_size_y: pooling window height. It's eaqual to pool_size by default.
    :type pool_size_y: int|None
Z
zhangjinchao01 已提交
2302 2303
    :param num_channels: number of input channel.
    :type num_channels: int
2304
    :param pool_type: pooling type. MaxPooling or AvgPooling. Default is
Z
zhangjinchao01 已提交
2305 2306
                      MaxPooling.
    :type pool_type: BasePoolingType
2307
    :param stride: stride width of pooling.
Z
zhangjinchao01 已提交
2308
    :type stride: int
2309 2310
    :param stride_y: stride height of pooling. It is equal to stride by default.
    :type stride_y: int|None
Z
zhangjinchao01 已提交
2311 2312
    :param layer_attr: Extra Layer attribute.
    :type layer_attr: ExtraLayerAttribute
2313 2314 2315 2316
    :param ceil_mode: Wether to use ceil mode to calculate output height and with.
                      Defalut is True. If set false, Otherwise use floor.

    :type ceil_mode: bool
D
dangqingqing 已提交
2317 2318
    :return: LayerOutput object.
    :rtype: LayerOutput
Z
zhangjinchao01 已提交
2319 2320 2321 2322 2323 2324 2325 2326 2327 2328
    """
    if num_channels is None:
        assert input.num_filters is not None
        num_channels = input.num_filters

    if pool_type is None:
        pool_type = MaxPooling()
    elif isinstance(pool_type, AvgPooling):
        pool_type.name = 'avg'

2329
    type_name = pool_type.name + '-projection' \
Y
Yu Yang 已提交
2330
        if (
Y
Yu Yang 已提交
2331
        isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)) \
Y
Yu Yang 已提交
2332
        else pool_type.name
2333 2334 2335 2336 2337

    pool_size_y = pool_size if pool_size_y is None else pool_size_y
    stride_y = stride if stride_y is None else stride_y
    padding_y = padding if padding_y is None else padding_y

X
xuwei06 已提交
2338
    l = Layer(
Z
zhangjinchao01 已提交
2339 2340
        name=name,
        type=LayerType.POOL_LAYER,
Q
qijun 已提交
2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352
        inputs=[
            Input(
                input.name,
                pool=Pool(
                    pool_type=type_name,
                    channels=num_channels,
                    size_x=pool_size,
                    start=None,
                    stride=stride,
                    padding=padding,
                    size_y=pool_size_y,
                    stride_y=stride_y,
L
Luo Tao 已提交
2353
                    padding_y=padding_y))
Q
qijun 已提交
2354
        ],
2355
        ceil_mode=ceil_mode,
Q
qijun 已提交
2356 2357 2358 2359 2360 2361 2362
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name,
        LayerType.POOL_LAYER,
        parents=[input],
        num_filters=num_channels,
        size=l.config.size)
Z
zhangjinchao01 已提交
2363 2364


Q
qijun 已提交
2365 2366
@wrap_name_default("spp")
@layer_support()
Q
qijun 已提交
2367 2368 2369 2370 2371 2372
def spp_layer(input,
              name=None,
              num_channels=None,
              pool_type=None,
              pyramid_height=None,
              layer_attr=None):
Q
qijun 已提交
2373 2374 2375 2376 2377
    """
    Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
    The details please refer to
    `Kaiming He's paper <https://arxiv.org/abs/1406.4729>`_.

L
Luo Tao 已提交
2378 2379 2380 2381
    The example usage is:

    ..  code-block:: python

2382 2383 2384
        spp = spp_layer(input=data,
                        pyramid_height=2,
                        num_channels=16,
L
Luo Tao 已提交
2385 2386
                        pool_type=MaxPooling())

Q
qijun 已提交
2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414
    :param name: layer name.
    :type name: basestring
    :param input: layer's input.
    :type input: LayerOutput
    :param num_channels: number of input channel.
    :type num_channels: int
    :param pool_type: Pooling type. MaxPooling or AveragePooling. Default is MaxPooling.
    :type scale: BasePoolingType
    :param pyramid_height: pyramid height.
    :type pyramid_height: int
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
    :return: LayerOutput object.
    :rtype: LayerOutput
    """
    if num_channels is None:
        assert input.num_filters is not None
        num_channels = input.num_filters

    if pool_type is None:
        pool_type = MaxPooling()
    elif isinstance(pool_type, AvgPooling):
        pool_type.name = 'avg'

    type_name = pool_type.name
    if (isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)):
        type_name += '-projection'

Q
qijun 已提交
2415
    l = Layer(
Q
qijun 已提交
2416 2417
        name=name,
        type=LayerType.SPP_LAYER,
Q
qijun 已提交
2418 2419 2420 2421 2422
        inputs=Input(
            input.name,
            spp=SpatialPyramidPool(
                pool_type=type_name,
                channels=num_channels,
L
Luo Tao 已提交
2423
                pyramid_height=pyramid_height)),
Q
qijun 已提交
2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name,
        layer_type=LayerType.SPP_LAYER,
        parents=[input],
        num_filters=num_channels,
        size=l.config.size)


def __img_norm_layer__(name, input, size, norm_type, scale, power, num_channels,
                       blocked, layer_attr):
Z
zhangjinchao01 已提交
2435 2436 2437 2438
    if num_channels is None:
        assert input.num_filters is not None
        num_channels = input.num_filters

X
xuwei06 已提交
2439
    l = Layer(
Q
qijun 已提交
2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458
        name=name,
        type=LayerType.NORM_LAYER,
        inputs=Input(
            input.name,
            norm=Norm(
                norm_type=norm_type,
                channels=num_channels,
                size=size,
                scale=scale,
                pow=power,
                blocked=blocked)),
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name,
        layer_type=LayerType.NORM_LAYER,
        parents=[input],
        num_filters=num_channels,
        img_norm_type=norm_type,
        size=l.config.size)
Z
zhangjinchao01 已提交
2459 2460 2461 2462


@wrap_name_default("crmnorm")
@layer_support()
Q
qijun 已提交
2463 2464 2465 2466 2467 2468
def img_cmrnorm_layer(input,
                      size,
                      scale=0.0128,
                      power=0.75,
                      name=None,
                      num_channels=None,
2469
                      layer_attr=None):
Z
zhangjinchao01 已提交
2470
    """
2471
    Response normalization across feature maps.
D
dangqingqing 已提交
2472 2473
    The details please refer to
    `Alex's paper <http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf>`_.
Z
zhangjinchao01 已提交
2474

L
Luo Tao 已提交
2475 2476 2477
    The example usage is:

    ..  code-block:: python
2478

L
Luo Tao 已提交
2479 2480
        norm = img_cmrnorm_layer(input=net, size=5)

Z
zhangjinchao01 已提交
2481
    :param name: layer name.
D
dangqingqing 已提交
2482
    :type name: None|basestring
Z
zhangjinchao01 已提交
2483 2484
    :param input: layer's input.
    :type input: LayerOutput
2485
    :param size: Normalize in number of :math:`size` feature maps.
Z
zhangjinchao01 已提交
2486
    :type size: int
D
dangqingqing 已提交
2487
    :param scale: The hyper-parameter.
Z
zhangjinchao01 已提交
2488
    :type scale: float
D
dangqingqing 已提交
2489
    :param power: The hyper-parameter.
Z
zhangjinchao01 已提交
2490 2491 2492 2493 2494
    :type power: float
    :param num_channels: input layer's filers number or channels. If
                         num_channels is None, it will be set automatically.
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
2495
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2496 2497 2498
    :rtype: LayerOutput
    """
    return __img_norm_layer__(name, input, size, "cmrnorm-projection", scale,
2499
                              power, num_channels, 0, layer_attr)
Z
zhangjinchao01 已提交
2500 2501 2502 2503 2504 2505 2506 2507


@wrap_bias_attr_default()
@wrap_param_attr_default(default_factory=lambda _: ParamAttr(initial_mean=1.0,
                                                             initial_std=0.))
@wrap_act_default(act=ReluActivation())
@wrap_name_default("batch_norm")
@layer_support(DROPOUT)
Q
qijun 已提交
2508 2509 2510 2511 2512 2513 2514
def batch_norm_layer(input,
                     act=None,
                     name=None,
                     num_channels=None,
                     bias_attr=None,
                     param_attr=None,
                     layer_attr=None,
Z
zhangjinchao01 已提交
2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535
                     batch_norm_type=None,
                     moving_average_fraction=0.9,
                     use_global_stats=None):
    """
    Batch Normalization Layer. The notation of this layer as follow.

    :math:`x` is the input features over a mini-batch.

    ..  math::

        \\mu_{\\beta} &\\gets \\frac{1}{m} \\sum_{i=1}^{m} x_i \\qquad &//\\
        \ mini-batch\ mean \\\\
        \\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
        \\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\
        \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
        \\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\
        y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift

    The details of batch normalization please refer to this
    `paper <http://arxiv.org/abs/1502.03167>`_.

L
Luo Tao 已提交
2536 2537 2538
    The example usage is:

    ..  code-block:: python
2539

L
Luo Tao 已提交
2540 2541
        norm = batch_norm_layer(input=net, act=ReluActivation())

Z
zhangjinchao01 已提交
2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555
    :param name: layer name.
    :type name: basestring
    :param input: batch normalization input. Better be linear activation.
                Because there is an activation inside batch_normalization.
    :type input: LayerOutput
    :param batch_norm_type: We have batch_norm and cudnn_batch_norm. batch_norm
                            supports both CPU and GPU. cudnn_batch_norm requires
                            cuDNN version greater or equal to v4 (>=v4). But
                            cudnn_batch_norm is faster and needs less memory
                            than batch_norm. By default (None), we will
                            automaticly select cudnn_batch_norm for GPU and
                            batch_norm for CPU. Otherwise, select batch norm
                            type based on the specified type. If you use cudnn_batch_norm,
                            we suggested you use latest version, such as v5.1.
2556
    :type batch_norm_type: None|string, None or "batch_norm" or "cudnn_batch_norm"
Z
zhangjinchao01 已提交
2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583
    :param act: Activation Type. Better be relu. Because batch
                     normalization will normalize input near zero.
    :type act: BaseActivation
    :param num_channels: num of image channels or previous layer's number of
                         filters. None will automatically get from layer's
                         input.
    :type num_channels: int
    :param bias_attr: :math:`\\beta`, better be zero when initialize. So the
                      initial_std=0, initial_mean=1 is best practice.
    :type bias_attr: ParameterAttribute
    :param param_attr: :math:`\\gamma`, better be one when initialize. So the
                       initial_std=0, initial_mean=1 is best practice.
    :type param_attr: ParameterAttribute
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
    :param use_global_stats: whether use moving mean/variance statistics
                             during testing peroid. If None or True,
                             it will use moving mean/variance statistics during
                             testing. If False, it will use the mean
                             and variance of current batch of test data for
                             testing.
    :type use_global_stats: bool|None.
    :param moving_average_fraction: Factor used in the moving average
                                   computation, referred to as facotr,
                                   :math:`runningMean = newMean*(1-factor)
                                   + runningMean*factor`
    :type moving_average_fraction: float.
D
dangqingqing 已提交
2584
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603
    :rtype: LayerOutput
    """
    if not isinstance(act, ReluActivation):
        logger.log(logging.WARN,
                   "%s is not recommend for batch normalization's activation, "
                   "maybe the relu is better" % act.name)

    if not isinstance(input.activation, LinearActivation):
        logger.log(logging.WARN,
                   "The activation should be inside batch normalization, the "
                   "previous layer's activation may be Linear")

    if num_channels is None:
        if input.num_filters is not None:
            num_channels = input.num_filters
        else:
            num_channels = input.size
    assert (batch_norm_type is None) or (batch_norm_type == "batch_norm") or \
           (batch_norm_type == "cudnn_batch_norm")
X
xuwei06 已提交
2604
    l = Layer(
Z
zhangjinchao01 已提交
2605
        name=name,
Q
qijun 已提交
2606 2607
        inputs=Input(
            input.name, image=Image(channels=num_channels), **param_attr.attr),
Z
zhangjinchao01 已提交
2608 2609 2610 2611 2612 2613
        active_type=act.name,
        type=LayerType.BATCH_NORM_LAYER,
        batch_norm_type=batch_norm_type,
        bias=ParamAttr.to_bias(bias_attr),
        moving_average_fraction=moving_average_fraction,
        use_global_stats=use_global_stats,
Q
qijun 已提交
2614
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
2615

Q
qijun 已提交
2616 2617 2618 2619 2620 2621 2622
    return LayerOutput(
        name=name,
        layer_type=LayerType.BATCH_NORM_LAYER,
        parents=[input],
        activation=act,
        num_filters=num_channels,
        size=l.config.size)
Z
zhangjinchao01 已提交
2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649


@wrap_name_default()
@layer_support()
def sum_to_one_norm_layer(input, name=None, layer_attr=None):
    """
    A layer for sum-to-one normalization,
    which is used in NEURAL TURING MACHINE.

    .. math::
       out[i] = \\frac {in[i]} {\sum_{k=1}^N in[k]}

    where :math:`in` is a (batchSize x dataDim) input vector,
    and :math:`out` is a (batchSize x dataDim) output vector.

    The example usage is:

    .. code-block:: python

       sum_to_one_norm = sum_to_one_norm_layer(input=layer)

    :param input: Input layer.
    :type input: LayerOutput
    :param name: Layer name.
    :type name: basestring
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
2650
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2651 2652 2653 2654 2655 2656
    :rtype: LayerOutput
    """
    Layer(
        name=name,
        type=LayerType.SUM_TO_ONE_NORM_LAYER,
        inputs=[input.name],
Q
qijun 已提交
2657 2658 2659
        **ExtraAttr.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.SUM_TO_ONE_NORM_LAYER, parents=[input], size=input.size)
Z
zhangjinchao01 已提交
2660 2661 2662 2663 2664 2665


@wrap_name_default("addto")
@wrap_act_default(act=LinearActivation())
@wrap_bias_attr_default(has_bias=False)
@layer_support(DROPOUT)
Q
qijun 已提交
2666
def addto_layer(input, act=None, name=None, bias_attr=None, layer_attr=None):
Z
zhangjinchao01 已提交
2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688
    """
    AddtoLayer.

    ..  math::

        y = f(\\sum_{i} x_i + b)

    where :math:`y` is output, :math:`x` is input, :math:`b` is bias,
    and :math:`f` is activation function.

    The example usage is:

    ..  code-block:: python

        addto = addto_layer(input=[layer1, layer2],
                            act=ReluActivation(),
                            bias_attr=False)

    This layer just simply add all input layers together, then activate the sum
    inputs. Each input of this layer should be the same size, which is also the
    output size of this layer.

C
caoying03 已提交
2689 2690 2691
    There is no weight matrix for each input, because it just a simple add
    operation. If you want a complicated operation before add, please use
    mixed_layer.
Z
zhangjinchao01 已提交
2692 2693

    It is a very good way to set dropout outside the layers. Since not all
C
caoying03 已提交
2694 2695
    PaddlePaddle layer support dropout, you can add an add_to layer, set
    dropout here.
Z
zhangjinchao01 已提交
2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709
    Please refer to dropout_layer for details.

    :param name: Layer name.
    :type name: basestring
    :param input: Input layers. It could be a LayerOutput or list/tuple of
                 LayerOutput.
    :type input: LayerOutput|list|tuple
    :param act: Activation Type, default is tanh.
    :type act: BaseActivation
    :param bias_attr: Bias attribute. If False, means no bias. None is default
                      bias.
    :type bias_attr: ParameterAttribute|bool
    :param layer_attr: Extra Layer attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
2710
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2711 2712 2713 2714 2715 2716
    :rtype: LayerOutput
    """
    num_filters = None
    if isinstance(input, LayerOutput):
        input = [input]

2717
    assert isinstance(input, collections.Sequence)
Z
zhangjinchao01 已提交
2718 2719 2720 2721 2722 2723 2724
    ipts_for_layer = []
    for each_input in input:
        assert isinstance(each_input, LayerOutput)
        ipts_for_layer.append(Input(each_input.name))
        if each_input.num_filters is not None:
            num_filters = each_input.num_filters

X
xuwei06 已提交
2725
    l = Layer(
Q
qijun 已提交
2726 2727 2728
        name=name,
        type=LayerType.ADDTO_LAYER,
        inputs=ipts_for_layer,
Z
zhangjinchao01 已提交
2729 2730
        bias=ParamAttr.to_bias(bias_attr),
        active_type=act.name,
Q
qijun 已提交
2731
        **ExtraLayerAttribute.to_kwargs(layer_attr))
2732

Q
qijun 已提交
2733 2734 2735 2736 2737 2738 2739
    return LayerOutput(
        name,
        LayerType.ADDTO_LAYER,
        parents=input,
        activation=act,
        num_filters=num_filters,
        size=l.config.size)
Z
zhangjinchao01 已提交
2740 2741 2742 2743 2744


@wrap_act_default(act=IdentityActivation())
@wrap_name_default("concat")
@layer_support()
2745
def concat_layer(input, act=None, name=None, layer_attr=None, bias_attr=None):
Z
zhangjinchao01 已提交
2746 2747 2748 2749
    """
    Concat all input vector into one huge vector.
    Inputs can be list of LayerOutput or list of projection.

2750 2751 2752 2753 2754 2755
    The example usage is:

    ..  code-block:: python

        concat = concat_layer(input=[layer1, layer2])

Z
zhangjinchao01 已提交
2756 2757 2758
    :param name: Layer name.
    :type name: basestring
    :param input: input layers or projections
2759
    :type input: list|tuple|collections.Sequence
Z
zhangjinchao01 已提交
2760 2761 2762 2763
    :param act: Activation type.
    :type act: BaseActivation
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
2764
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2765 2766 2767 2768 2769 2770 2771 2772
    :rtype: LayerOutput
    """

    if isinstance(input, LayerOutput):
        input = [input]
    elif isinstance(input, Projection):
        input = [input]
    else:
2773
        assert isinstance(input, collections.Sequence)
Z
zhangjinchao01 已提交
2774 2775

    def __is_type__(o, tp):
2776
        if not isinstance(o, collections.Sequence):
Z
zhangjinchao01 已提交
2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797
            if o == tp:
                return True
            elif len(o.__bases__) == 0:
                return False
            else:
                for bs in o.__bases__:
                    if __is_type__(bs, tp):
                        return True
                return False
        else:
            tmp = map(lambda _x: __is_type__(_x, tp), o)
            a = tmp[0]
            for b in tmp[1:]:
                assert a == b
            return a

    def __reduce_concat_type__(a, b):
        assert __is_type__([a, b], Projection) or __is_type__([a, b],
                                                              LayerOutput)
        return a

Q
qijun 已提交
2798 2799
    is_concat_layer = __is_type__(
        reduce(__reduce_concat_type__, map(type, input)), LayerOutput)
Z
zhangjinchao01 已提交
2800

Q
qijun 已提交
2801 2802
    layer_type = (LayerType.CONCAT_LAYER
                  if is_concat_layer else LayerType.CONCAT_PROJ_LAYER)
Z
zhangjinchao01 已提交
2803

2804 2805
    if layer_type == LayerType.CONCAT_LAYER:
        assert not bias_attr
2806

2807
    layer = Layer(
Q
qijun 已提交
2808 2809
        name=name,
        type=layer_type,
Z
zhangjinchao01 已提交
2810 2811
        inputs=[x.name for x in input] if is_concat_layer else input,
        active_type=act.name,
2812
        bias=ParamAttr.to_bias(bias_attr),
Q
qijun 已提交
2813
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
2814

2815
    sz = layer.config.size
Z
zhangjinchao01 已提交
2816

Q
qijun 已提交
2817 2818 2819 2820 2821 2822 2823 2824
    return LayerOutput(
        name,
        layer_type=layer_type,
        parents=input if is_concat_layer else [x.origin for x in input],
        activation=act,
        size=sz)


2825 2826
@wrap_name_default("seqconcat")
@wrap_act_default(act=IdentityActivation())
2827
@wrap_bias_attr_default(has_bias=False)
2828 2829 2830 2831 2832
@layer_support()
def seq_concat_layer(a, b, act=None, name=None, layer_attr=None,
                     bias_attr=None):
    """
    Concat sequence a with sequence b.
2833

2834
    Inputs:
2835 2836 2837
      - a = [a1, a2, ..., an]
      - b = [b1, b2, ..., bn]
      - Note that the length of a and b should be the same.
2838

2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856
    Output: [a1, b1, a2, b2, ..., an, bn]

    The example usage is:

    ..  code-block:: python

        concat = seq_concat_layer(a=layer1, b=layer2)

    :param name: Layer name.
    :type name: basestring
    :param a: input sequence layer
    :type a: LayerOutput
    :param b: input sequence layer
    :type b: LayerOutput
    :param act: Activation type.
    :type act: BaseActivation
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
2857 2858 2859 2860
    :param bias_attr: The Bias Attribute. If no bias, then pass False or
                      something not type of ParameterAttribute. None will get a
                      default Bias.
    :type bias_attr: ParameterAttribute or None or bool
2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881
    :return: LayerOutput object.
    :rtype: LayerOutput
    """
    assert isinstance(a, LayerOutput) and isinstance(b, LayerOutput)
    assert a.size == b.size
    Layer(
        name=name,
        type=LayerType.SEQUENCE_CONCAT_LAYER,
        inputs=[a.name, b.name],
        active_type=act.name,
        bias=ParamAttr.to_bias(bias_attr),
        **ExtraLayerAttribute.to_kwargs(layer_attr))

    return LayerOutput(
        name,
        layer_type=LayerType.SEQUENCE_CONCAT_LAYER,
        parents=[a, b],
        activation=act,
        size=a.size)


2882
@wrap_name_default("memory", "memory_name")
Q
qijun 已提交
2883 2884
def memory(name,
           size,
2885
           memory_name=None,
Q
qijun 已提交
2886 2887 2888 2889
           is_seq=False,
           boot_layer=None,
           boot_bias=None,
           boot_bias_active_type=None,
Z
zhangjinchao01 已提交
2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909
           boot_with_const_id=None):
    """
    The memory layers is a layer cross each time step. Reference this output
    as previous time step layer :code:`name` 's output.

    The default memory is zero in first time step, previous time step's
    output in the rest time steps.

    If boot_bias, the first time step value is this bias and
    with activation.

    If boot_with_const_id, then the first time stop is a IndexSlot, the
    Arguments.ids()[0] is this :code:`cost_id`.

    If boot_layer is not null, the memory is just the boot_layer's output.
    Set :code:`is_seq` is true boot layer is sequence.

    The same name layer in recurrent group will set memory on each time
    step.

2910 2911 2912 2913 2914 2915 2916 2917 2918
    .. code-block:: python

       mem = memory(size=256, name='state')
       state = fc_layer(input=mem, size=256, name='state')

    If you do not want to specify the name, you can equivalently use set_input()
    to specify the layer needs to be remembered as the following:

    .. code-block:: python
L
Liu Yiqun 已提交
2919

2920 2921 2922 2923 2924 2925 2926
       mem = memory(size=256)
       state = fc_layer(input=mem, size=256)
       mem.set_input(mem)

    :param name: the name of the layer which this memory remembers.
                 If name is None, user should call set_input() to specify the
                 name of the layer which this memory remembers.
Z
zhangjinchao01 已提交
2927 2928 2929
    :type name: basestring
    :param size: size of memory.
    :type size: int
2930 2931 2932
    :param memory_name: the name of the memory.
                        It is ignored when name is provided.
    :type memory_name: basestring
Z
zhangjinchao01 已提交
2933 2934 2935 2936 2937 2938 2939 2940 2941 2942
    :param is_seq: is sequence for boot_layer
    :type is_seq: bool
    :param boot_layer: boot layer of memory.
    :type boot_layer: LayerOutput|None
    :param boot_bias: boot layer's bias
    :type boot_bias: ParameterAttribute|None
    :param boot_bias_active_type: boot layer's active type.
    :type boot_bias_active_type: BaseActivation
    :param boot_with_const_id: boot layer's id.
    :type boot_with_const_id: int
D
dangqingqing 已提交
2943
    :return: LayerOutput object which is a memory.
Z
zhangjinchao01 已提交
2944 2945 2946 2947 2948 2949 2950 2951 2952 2953
    :rtype: LayerOutput
    """
    if boot_bias_active_type is None:
        boot_bias_active_type = LinearActivation()

    assert boot_bias is None or isinstance(boot_bias, ParameterAttribute)
    if isinstance(boot_bias, ParameterAttribute):
        boot_bias = ParamAttr.to_bias(boot_bias)

    assert boot_layer is None or isinstance(boot_layer, LayerOutput)
2954 2955
    if name is not None:
        memory_name = None
Z
zhangjinchao01 已提交
2956

2957 2958 2959 2960 2961 2962 2963 2964 2965
    memory_name = Memory(
        name,
        size,
        is_sequence=is_seq,
        boot_layer=boot_layer.name if boot_layer is not None else None,
        boot_bias=boot_bias,
        boot_bias_active_type=boot_bias_active_type.name,
        boot_with_const_id=boot_with_const_id,
        memory_name=memory_name)
Q
qijun 已提交
2966 2967

    lout = LayerOutput(
2968
        name=memory_name,
Q
qijun 已提交
2969 2970 2971
        size=size,
        layer_type=LayerType.MEMORY,
        parents=[boot_layer] if boot_layer is not None else None)
Z
zhangjinchao01 已提交
2972 2973 2974 2975
    return lout


@wrap_bias_attr_default()
Q
qijun 已提交
2976 2977
@wrap_act_default(
    param_names=['gate_act', 'state_act'], act=SigmoidActivation())
Z
zhangjinchao01 已提交
2978 2979 2980
@wrap_act_default(act=TanhActivation())
@wrap_name_default('lstm_step')
@layer_support()
Q
qijun 已提交
2981 2982
def lstm_step_layer(input,
                    state,
2983
                    size=None,
Q
qijun 已提交
2984 2985 2986 2987 2988 2989
                    act=None,
                    name=None,
                    gate_act=None,
                    state_act=None,
                    bias_attr=None,
                    layer_attr=None):
Z
zhangjinchao01 已提交
2990 2991 2992 2993 2994 2995
    """
    LSTM Step Layer. It used in recurrent_group. The lstm equations are shown
    as follow.

    ..  math::

L
luotao02 已提交
2996
        i_t & = \\sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)
Z
zhangjinchao01 已提交
2997

L
luotao02 已提交
2998
        f_t & = \\sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)
Z
zhangjinchao01 已提交
2999

L
luotao02 已提交
3000
        c_t & = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)
Z
zhangjinchao01 已提交
3001

L
luotao02 已提交
3002
        o_t & = \\sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)
Z
zhangjinchao01 已提交
3003

L
luotao02 已提交
3004
        h_t & = o_t tanh(c_t)
Z
zhangjinchao01 已提交
3005 3006


L
luotao02 已提交
3007
    The input of lstm step is :math:`Wx_t + Wh_{t-1}`, and user should use
Z
zhangjinchao01 已提交
3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045
    :code:`mixed_layer` and :code:`full_matrix_projection` to calculate these
    input vector.

    The state of lstm step is :math:`c_{t-1}`. And lstm step layer will do

    ..  math::

        i_t = \\sigma(input + W_{ci}c_{t-1} + b_i)

        ...


    This layer contains two outputs. Default output is :math:`h_t`. The other
    output is :math:`o_t`, which name is 'state' and can use
    :code:`get_output_layer` to extract this output.

    :param name: Layer's name.
    :type name: basestring
    :param size: Layer's size. NOTE: lstm layer's size, should be equal as
                 :code:`input.size/4`, and should be equal as
                 :code:`state.size`.
    :type size: int
    :param input: input layer. :math:`Wx_t + Wh_{t-1}`
    :type input: LayerOutput
    :param state: State Layer. :math:`c_{t-1}`
    :type state: LayerOutput
    :param act: Activation type. Default is tanh
    :type act: BaseActivation
    :param gate_act: Gate Activation Type. Default is sigmoid, and should
                          be sigmoid only.
    :type gate_act: BaseActivation
    :param state_act: State Activation Type. Default is sigmoid, and should
                           be sigmoid only.
    :type state_act: BaseActivation
    :param bias_attr: Bias Attribute.
    :type bias_attr: ParameterAttribute
    :param layer_attr: layer's extra attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
3046
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3047 3048
    :rtype: LayerOutput
    """
3049 3050 3051

    assert size is None or state.size == size
    size = state.size
Z
zhangjinchao01 已提交
3052 3053 3054 3055 3056 3057 3058
    Layer(
        name=name,
        type=LayerType.LSTM_STEP_LAYER,
        active_type=act.name,
        active_gate_type=gate_act.name,
        active_state_type=state_act.name,
        bias=ParamAttr.to_bias(bias_attr),
3059
        size=state.size,
Q
qijun 已提交
3060 3061
        inputs=[input.name, state.name],
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
3062

Q
qijun 已提交
3063 3064 3065 3066 3067 3068 3069
    return LayerOutput(
        name=name,
        layer_type=LayerType.LSTM_STEP_LAYER,
        parents=[input, state],
        activation=act,
        size=size,
        outputs=['default', 'state'])
Z
zhangjinchao01 已提交
3070 3071 3072


@wrap_bias_attr_default()
W
wangyang59 已提交
3073
@wrap_param_attr_default()
Q
qijun 已提交
3074
@wrap_act_default(param_names=['gate_act'], act=SigmoidActivation())
Z
zhangjinchao01 已提交
3075 3076 3077
@wrap_act_default(act=TanhActivation())
@wrap_name_default('gru_step')
@layer_support()
Q
qijun 已提交
3078 3079 3080 3081 3082 3083 3084
def gru_step_layer(input,
                   output_mem,
                   size=None,
                   act=None,
                   name=None,
                   gate_act=None,
                   bias_attr=None,
W
wangyang59 已提交
3085
                   param_attr=None,
Q
qijun 已提交
3086
                   layer_attr=None):
Z
zhangjinchao01 已提交
3087 3088 3089 3090 3091 3092 3093 3094 3095 3096
    """

    :param input:
    :type input: LayerOutput
    :param output_mem:
    :param size:
    :param act:
    :param name:
    :param gate_act:
    :param bias_attr:
3097 3098
    :param param_attr: the parameter_attribute for transforming the output_mem
                       from previous step.
Z
zhangjinchao01 已提交
3099
    :param layer_attr:
D
dangqingqing 已提交
3100
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3101 3102 3103 3104 3105 3106 3107 3108
    :rtype: LayerOutput
    """
    assert input.size % 3 == 0
    if size is None:
        size = input.size / 3
    Layer(
        name=name,
        type=LayerType.GRU_STEP_LAYER,
3109 3110 3111 3112
        # The parameter here is for transforming the output_mem. The input has
        # already been transformed outside this module so it does not need
        # parameter associated with it.
        # The parameter here is instead grouped with input is due to
3113
        # backward model compatibility.
3114
        inputs=[Input(input.name, **param_attr.attr), output_mem.name],
Z
zhangjinchao01 已提交
3115 3116 3117 3118
        bias=ParamAttr.to_bias(bias_attr),
        size=size,
        active_type=act.name,
        active_gate_type=gate_act.name,
Q
qijun 已提交
3119
        **ExtraAttr.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
3120
    return LayerOutput(
Q
qijun 已提交
3121 3122
        name=name,
        layer_type=LayerType.GRU_STEP_LAYER,
Z
zhangjinchao01 已提交
3123
        parents=[input, output_mem],
Q
qijun 已提交
3124 3125
        size=size,
        activation=act)
Z
zhangjinchao01 已提交
3126 3127


Y
Yu Yang 已提交
3128 3129 3130 3131
@wrap_bias_attr_default()
@wrap_param_attr_default()
@wrap_act_default(param_names=['gate_act'], act=SigmoidActivation())
@wrap_act_default(act=TanhActivation())
Q
qijun 已提交
3132
@wrap_name_default('gru_step_naive')
Y
Yu Yang 已提交
3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199
@layer_support(ERROR_CLIPPING, DROPOUT)
def gru_step_naive_layer(input,
                         output_mem,
                         size=None,
                         name=None,
                         act=None,
                         gate_act=None,
                         bias_attr=None,
                         param_attr=None,
                         layer_attr=None):
    """
    GRU Step Layer, but using MixedLayer to generate. It support ERROR_CLIPPING
    and DROPOUT.

    :param input:
    :param output_mem:
    :param size:
    :param name:
    :param act:
    :param gate_act:
    :param bias_attr:
    :param param_attr:
    :param layer_attr:
    :return:
    """
    if input.size % 3 != 0:
        raise ValueError("GruStep input size must be divided by 3")
    if size is None:
        size = input.size / 3

    def __gate__(gate_name, offset):
        with mixed_layer(
                name=name + "_" + gate_name,
                size=size,
                layer_attr=layer_attr,
                bias_attr=bias_attr,
                act=gate_act) as gate:
            gate += identity_projection(input=input, offset=offset)
            gate += full_matrix_projection(
                input=output_mem, param_attr=param_attr)
        return gate

    update_gate = __gate__("update", 0)
    reset_gate = __gate__("reset", size)

    with mixed_layer(
            name=name + "_reset_output", bias_attr=False) as reset_output:
        reset_output += dotmul_operator(a=output_mem, b=reset_gate)

    with mixed_layer(
            name=name + "_output_candidate",
            size=size,
            layer_attr=layer_attr,
            bias_attr=bias_attr,
            act=act) as output_candidate:
        output_candidate += identity_projection(input=input, offset=2 * size)
        output_candidate += full_matrix_projection(
            input=reset_output, param_attr=param_attr)

    with mixed_layer(name=name) as output:
        output += identity_projection(output_mem)
        output += dotmul_operator(a=output_mem, b=update_gate, scale=-1.0)
        output += dotmul_operator(a=output_candidate, b=update_gate)

    return output


Z
zhangjinchao01 已提交
3200 3201 3202 3203
@wrap_name_default()
@layer_support()
def get_output_layer(input, arg_name, name=None, layer_attr=None):
    """
C
caoying03 已提交
3204 3205 3206 3207
    Get layer's output by name. In PaddlePaddle, a layer might return multiple
    values, but returns one layer's output. If the user wants to use another
    output besides the default one, please use get_output_layer first to get
    the output from input.
Z
zhangjinchao01 已提交
3208 3209 3210 3211 3212 3213 3214 3215 3216

    :param name: Layer's name.
    :type name: basestring
    :param input: get output layer's input. And this layer should contains
                   multiple outputs.
    :type input: LayerOutput
    :param arg_name: Output name from input.
    :type arg_name: basestring
    :param layer_attr: Layer's extra attribute.
D
dangqingqing 已提交
3217
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3218 3219 3220 3221 3222 3223 3224
    :rtype: LayerOutput
    """
    # GetOutputLayer
    assert arg_name in input.outputs, 'Get Output From an not existed input.' \
                                      ' The get output name is %s, which not' \
                                      ' in %s' % (
                                          arg_name, ",".join(input.outputs))
Q
qijun 已提交
3225 3226 3227 3228 3229 3230 3231
    Layer(
        name=name,
        type=LayerType.GET_OUTPUT_LAYER,
        inputs=[Input(
            input.name, input_layer_argument=arg_name)],
        size=input.size,
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
3232

Q
qijun 已提交
3233 3234 3235 3236 3237
    return LayerOutput(
        name=name,
        layer_type=LayerType.GET_OUTPUT_LAYER,
        parents=[input],
        size=input.size)
Z
zhangjinchao01 已提交
3238 3239 3240 3241 3242 3243 3244


@wrap_name_default()
@wrap_act_default()
@wrap_bias_attr_default()
@wrap_param_attr_default()
@layer_support()
Q
qijun 已提交
3245 3246 3247 3248 3249 3250 3251
def recurrent_layer(input,
                    act=None,
                    bias_attr=None,
                    param_attr=None,
                    name=None,
                    reverse=False,
                    layer_attr=None):
Z
zhangjinchao01 已提交
3252
    """
3253 3254
    Simple recurrent unit layer. It is just a fully connect layer through both
    time and neural network.
Z
zhangjinchao01 已提交
3255

3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282
    For each sequence [start, end] it performs the following computation\:

    ..  math::

        out_{i} = act(in_{i})     \\      \\      \\text{for} \\ i = start \\\\
        out_{i} = act(in_{i} + out_{i-1} * W) \\ \\ \\text{for} \\ start < i <= end

    If reversed is true, the order is reversed\:

    ..  math::

        out_{i} = act(in_{i})           \\    \\   \\text{for} \\ i = end  \\\\
        out_{i} = act(in_{i} + out_{i+1} * W) \\ \\ \\text{for} \\ start <= i < end


    :param input: Input Layer
    :type input: LayerOutput
    :param act: activation.
    :type act: BaseActivation
    :param bias_attr: bias attribute.
    :type bias_attr: ParameterAttribute
    :param param_attr: parameter attribute.
    :type param_attr: ParameterAttribute
    :param name: name of the layer
    :type name: basestring
    :param layer_attr: Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
3283
    :return: LayerOutput object.
3284
    :rtype: LayerOutput
Z
zhangjinchao01 已提交
3285
    """
Q
qijun 已提交
3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300
    Layer(
        name=name,
        type=LayerType.RECURRENT_LAYER,
        inputs=Input(input.name, **param_attr.attr),
        active_type=act.name,
        bias=ParamAttr.to_bias(bias_attr),
        reversed=reverse,
        **ExtraAttr.to_kwargs(layer_attr))
    return LayerOutput(
        name=name,
        layer_type=LayerType.RECURRENT_LAYER,
        parents=[input],
        size=input.size,
        activation=act,
        reverse=reverse)
Z
zhangjinchao01 已提交
3301 3302 3303 3304 3305 3306 3307


class StaticInput(object):
    """
    StaticInput is only used in recurrent_group which defines a read-only memory
    that can be a sequence or non-sequence.
    """
3308

Z
zhangjinchao01 已提交
3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327
    def __init__(self, input, is_seq=False, size=None):
        assert isinstance(input, LayerOutput)
        self.input = input
        self.is_seq = is_seq
        assert input.size is not None or size is not None
        if size is not None:
            input.size = size


class SubsequenceInput(object):
    """
    Input sequence has sub-sequence, used in recurrent_group.

    The example usage is:

    .. code-block:: python

       input = SubsequenceInput(layer)
    """
3328

Z
zhangjinchao01 已提交
3329 3330 3331 3332 3333 3334 3335
    def __init__(self, input):
        assert isinstance(input, LayerOutput)
        assert input.size is not None
        self.input = input


@wrap_name_default("recurrent_group")
L
Luo Tao 已提交
3336 3337 3338 3339 3340
def recurrent_group(step,
                    input,
                    reverse=False,
                    name=None,
                    targetInlink=None,
L
Luo Tao 已提交
3341
                    is_generating=False):
Z
zhangjinchao01 已提交
3342
    """
C
caoying03 已提交
3343 3344 3345 3346 3347
    Recurrent layer group is an extremely flexible recurrent unit in
    PaddlePaddle. As long as the user defines the calculation done within a
    time step, PaddlePaddle will iterate such a recurrent calculation over
    sequence input. This is extremely usefull for attention based model, or
    Neural Turning Machine like models.
Z
zhangjinchao01 已提交
3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391

    The basic usage (time steps) is:

    .. code-block:: python

       def step(input):
           output = fc_layer(input=layer,
                             size=1024,
                             act=LinearActivation(),
                             bias_attr=False)
           return output

       group = recurrent_group(input=layer,
                               step=step)

    You can see following configs for further usages:

    - time steps: lstmemory_group, paddle/gserver/tests/sequence_layer_group.conf, \
                  demo/seqToseq/seqToseq_net.py
    - sequence steps: paddle/gserver/tests/sequence_nest_layer_group.conf

    :param step: recurrent one time step function.The input of this function is
                 input of the group. The return of this function will be
                 recurrent group's return value.

                 The recurrent group scatter a sequence into time steps. And
                 for each time step, will invoke step function, and return
                 a time step result. Then gather each time step of output into
                 layer group's output.

    :type step: callable

    :param name: recurrent_group's name.
    :type name: basestring

    :param input: Input links array.

                  LayerOutput will be scattered into time steps.
                  SubsequenceInput will be scattered into sequence steps.
                  StaticInput will be imported to each time step, and doesn't change
                  through time. It's a mechanism to access layer outside step function.

    :type input: LayerOutput|StaticInput|SubsequenceInput|list|tuple

3392 3393
    :param reverse: If reverse is set true, the recurrent unit will process the
                    input sequence in a reverse order.
Z
zhangjinchao01 已提交
3394
    :type reverse: bool
3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405

    :param targetInlink: the input layer which share info with layer group's output

                         Param input specifies multiple input layers. For
                         SubsequenceInput inputs, config should assign one input
                         layer that share info(the number of sentences and the number
                         of words in each sentence) with all layer group's outputs.
                         targetInlink should be one of the layer group's input.

    :type targetInlink: LayerOutput|SubsequenceInput

L
Luo Tao 已提交
3406
    :param is_generating: If is generating, none of input type should be LayerOutput;
3407
                          else, for training or testing, one of the input type must
L
Luo Tao 已提交
3408
                          be LayerOutput.
L
Luo Tao 已提交
3409

L
Liu Yiqun 已提交
3410
    :type is_generating: bool
3411

D
dangqingqing 已提交
3412
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3413 3414 3415 3416 3417 3418 3419 3420 3421 3422
    :rtype: LayerOutput
    """
    model_type('recurrent_nn')

    def is_single_input(x):
        return isinstance(x, LayerOutput) or isinstance(x, StaticInput) \
               or isinstance(x, SubsequenceInput)

    if is_single_input(input):
        input = [input]
3423
    assert isinstance(input, collections.Sequence)
Z
zhangjinchao01 已提交
3424 3425 3426 3427 3428 3429

    def is_in_links(x):
        return isinstance(x, LayerOutput) or isinstance(x, SubsequenceInput)

    in_links = filter(is_in_links, input)

3430 3431 3432 3433 3434 3435 3436 3437 3438
    def targetInlink_in_inlinks():
        for inlink in in_links:
            if isinstance(inlink, SubsequenceInput):
                if targetInlink == inlink.input:
                    return True
            elif targetInlink == inlink:
                return True
        return False

Q
qijun 已提交
3439
    assert (targetInlink == None or targetInlink_in_inlinks())
3440
    targetInlinkName = None if targetInlink == None \
Y
Yu Yang 已提交
3441 3442
        else targetInlink.name if isinstance(targetInlink, LayerOutput) \
        else targetInlink.input.name
3443

Z
zhangjinchao01 已提交
3444 3445 3446 3447 3448 3449 3450 3451 3452 3453
    contains_sub_seq = [False]

    def map_in_links(x):
        if isinstance(x, SubsequenceInput):
            contains_sub_seq[0] = True
            return Link(name=x.input.name, has_subseq=True)
        else:
            return x.name

    RecurrentLayerGroupWithoutOutLinksBegin(
Q
qijun 已提交
3454 3455
        name=name,
        in_links=map(map_in_links, in_links),
3456 3457
        seq_reversed=reverse,
        target_inlinkname=targetInlinkName)
Z
zhangjinchao01 已提交
3458
    in_args = []
3459
    has_LayerOutput = False
Z
zhangjinchao01 已提交
3460 3461 3462 3463
    for each_input in input:
        assert is_single_input(each_input)
        if isinstance(each_input, LayerOutput):
            in_args.append(each_input)
3464
            has_LayerOutput = True
Z
zhangjinchao01 已提交
3465 3466
        elif isinstance(each_input, SubsequenceInput):
            in_args.append(each_input.input)
3467
            has_LayerOutput = True
Z
zhangjinchao01 已提交
3468 3469
        else:
            mem_name = "__%s_memory__" % each_input.input.name
Q
qijun 已提交
3470 3471 3472 3473 3474 3475 3476 3477 3478
            mem = memory(
                name=mem_name,
                is_seq=each_input.is_seq,
                size=each_input.input.size,
                boot_layer=each_input.input)
            with mixed_layer(
                    name=mem_name,
                    size=each_input.input.size,
                    act=IdentityActivation()) as mix:
Z
zhangjinchao01 已提交
3479 3480 3481
                mix += identity_projection(mem)
            in_args.append(mem)

L
Luo Tao 已提交
3482
    assert (is_generating != has_LayerOutput)
L
Luo Tao 已提交
3483

Z
zhangjinchao01 已提交
3484 3485 3486 3487 3488 3489 3490
    layer_outs = step(*in_args)

    if isinstance(layer_outs, LayerOutput):
        layer_outs = [layer_outs]

    for ot in layer_outs:
        assert isinstance(ot, LayerOutput)
3491
        ot.reverse = reverse
Z
zhangjinchao01 已提交
3492 3493 3494 3495 3496 3497 3498
        if contains_sub_seq[0]:
            RecurrentLayerGroupSetOutLink(Link(ot.name, has_subseq=True))
        else:
            RecurrentLayerGroupSetOutLink(ot.name)

    RecurrentLayerGroupEnd(name=name)

X
xuwei06 已提交
3499 3500 3501 3502 3503
    for layer_out in layer_outs:
        # Thee previous full_name is the name is the rnn group
        # We need a full_name outside the rnn group
        layer_out.full_name = MakeLayerNameInSubmodel(layer_out.name)

Z
zhangjinchao01 已提交
3504 3505 3506 3507 3508
    if len(layer_outs) == 1:
        return layer_outs[0]
    else:
        return layer_outs

3509

Z
zhangjinchao01 已提交
3510 3511 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526
class BaseGeneratedInput(object):
    def __init__(self):
        self.bos_id = None
        self.eos_id = None

    def before_real_step(self):
        raise NotImplementedError()

    def after_real_step(self, *args):
        raise NotImplementedError()


class GeneratedInput(BaseGeneratedInput):
    def after_real_step(self, input):
        return maxid_layer(input=input, name='__beam_search_predict__')

    def before_real_step(self):
Q
qijun 已提交
3527 3528 3529 3530 3531 3532 3533 3534 3535
        predict_id = memory(
            name='__beam_search_predict__',
            size=self.size,
            boot_with_const_id=self.bos_id)

        trg_emb = embedding_layer(
            input=predict_id,
            size=self.embedding_size,
            param_attr=ParamAttr(name=self.embedding_name))
Z
zhangjinchao01 已提交
3536 3537 3538
        return trg_emb

    def __init__(self, size, embedding_name, embedding_size):
3539
        super(GeneratedInput, self).__init__()
Z
zhangjinchao01 已提交
3540 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562
        self.size = size
        self.embedding_name = embedding_name
        self.embedding_size = embedding_size


@wrap_name_default()
def maxid_layer(input, name=None, layer_attr=None):
    """
    A layer for finding the id which has the maximal value for each sample.
    The result is stored in output.ids.

    The example usage is:

    .. code-block:: python

       maxid = maxid_layer(input=layer)

    :param input: Input layer name.
    :type input: LayerOutput
    :param name: Layer name.
    :type name: basestring
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
3563
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3564 3565 3566 3567
    :rtype: LayerOutput
    """

    assert isinstance(input, LayerOutput)
Q
qijun 已提交
3568 3569 3570 3571 3572 3573 3574 3575 3576 3577
    l = Layer(
        name=name,
        type='maxid',
        inputs=[input.name],
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name=name,
        layer_type=LayerType.MAXID_LAYER,
        parents=[input],
        size=l.config.size)
Z
zhangjinchao01 已提交
3578

3579

H
Haonan 已提交
3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605
@wrap_name_default()
def out_prod_layer(input1, input2, name=None, layer_attr=None):
    """
    A layer for computing the outer product of two vectors
    The result is a matrix of size(input1) x size(input2)

    The example usage is:

    .. code-block:: python

       out_prod = out_prod_layer(input1=vec1, input2=vec2)

    :param name: Layer name.
    :type name: basestring
    :param input1: The first input layer name.
    :type input: LayerOutput
    :param input2: The second input layer name.
    :type input2: LayerOutput
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
    :return: LayerOutput object.
    :rtype: LayerOutput
    """

    assert isinstance(input1, LayerOutput)
    assert isinstance(input2, LayerOutput)
Q
qijun 已提交
3606 3607 3608 3609 3610 3611 3612 3613 3614 3615
    l = Layer(
        name=name,
        type=LayerType.OUT_PROD_LAYER,
        inputs=[input1.name, input2.name],
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name=name,
        layer_type=LayerType.OUT_PROD_LAYER,
        parents=[input1, input2],
        size=l.config.size)
3616

Z
zhangjinchao01 已提交
3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 3630 3631 3632

@wrap_name_default()
def eos_layer(input, eos_id, name=None, layer_attr=None):
    """
    A layer for checking EOS for each sample:
    - output_id = (input_id == conf.eos_id)

    The result is stored in output\_.ids.
    It is used by recurrent layer group.

    The example usage is:

    .. code-block:: python

       eos = eos_layer(input=layer, eos_id=id)

L
luotao02 已提交
3633 3634
    :param name: Layer name.
    :type name: basestring
Z
zhangjinchao01 已提交
3635 3636 3637 3638 3639 3640
    :param input: Input layer name.
    :type input: LayerOutput
    :param eos_id: end id of sequence
    :type eos_id: int
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
3641
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3642 3643
    :rtype: LayerOutput
    """
Q
qijun 已提交
3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654
    l = Layer(
        name=name,
        type=LayerType.EOSID_LAYER,
        eos_id=eos_id,
        inputs=[input.name],
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name=name,
        layer_type=LayerType.EOSID_LAYER,
        parents=[input],
        size=l.config.size)
Z
zhangjinchao01 已提交
3655 3656 3657


@wrap_name_default()
Q
qijun 已提交
3658 3659 3660 3661 3662 3663 3664
def beam_search(step,
                input,
                bos_id,
                eos_id,
                beam_size,
                max_length=500,
                name=None,
Z
zhangjinchao01 已提交
3665
                num_results_per_sample=None):
3666 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676
    """
    Beam search is a heuristic search algorithm used in sequence generation.
    It explores a graph by expanding the most promising nodes in a limited set
    to maintain tractability.

    The example usage is:

    .. code-block:: python

        def rnn_step(input):
            last_time_step_output = memory(name='rnn', size=512)
3677
            with mixed_layer(size=512, name='rnn') as simple_rnn:
3678 3679 3680 3681
                simple_rnn += full_matrix_projection(input)
                simple_rnn += last_time_step_output
            return simple_rnn

3682 3683 3684 3685 3686
        generated_word_embedding = GeneratedInput(
                               size=target_dictionary_dim,
                               embedding_name="target_language_embedding",
                               embedding_size=word_vector_dim)

3687 3688
        beam_gen = beam_search(name="decoder",
                               step=rnn_step,
3689 3690
                               input=[StaticInput(encoder_last),
                                      generated_word_embedding],
3691 3692
                               bos_id=0,
                               eos_id=1,
3693
                               beam_size=5)
3694 3695 3696 3697 3698 3699 3700 3701 3702

    Please see the following demo for more details:

    - machine translation : demo/seqToseq/translation/gen.conf \
                            demo/seqToseq/seqToseq_net.py

    :param name: Name of the recurrent unit that generates sequences.
    :type name: base string
    :param step: A callable function that defines the calculation in a time
3703
                 step, and it is applied to sequences with arbitrary length by
3704 3705 3706 3707 3708
                 sharing a same set of weights.

                 You can refer to the first parameter of recurrent_group, or
                 demo/seqToseq/seqToseq_net.py for more details.
    :type step: callable
3709 3710
    :param input: Input data for the recurrent unit, which should include the
                  previously generated words as a GeneratedInput object.
3711
    :type input: list
3712 3713 3714
    :param bos_id: Index of the start symbol in the dictionary. The start symbol
                   is a special token for NLP task, which indicates the
                   beginning of a sequence. In the generation task, the start
3715
                   symbol is essential, since it is used to initialize the RNN
3716 3717 3718 3719 3720 3721 3722 3723
                   internal state.
    :type bos_id: int
    :param eos_id: Index of the end symbol in the dictionary. The end symbol is
                   a special token for NLP task, which indicates the end of a
                   sequence. The generation process will stop once the end
                   symbol is generated, or a pre-defined max iteration number
                   is exceeded.
    :type eos_id: int
3724 3725
    :param max_length: Max generated sequence length.
    :type max_length: int
3726 3727 3728 3729 3730 3731 3732 3733 3734 3735
    :param beam_size: Beam search for sequence generation is an iterative search
                      algorithm. To maintain tractability, every iteration only
                      only stores a predetermined number, called the beam_size,
                      of the most promising next words. The greater the beam
                      size, the fewer candidate words are pruned.
    :type beam_size: int
    :param num_results_per_sample: Number of the generated results per input
                                  sequence. This number must always be less than
                                  beam size.
    :type num_results_per_sample: int
3736 3737
    :return: The generated word index.
    :rtype: LayerOutput
3738 3739
    """

Z
zhangjinchao01 已提交
3740 3741 3742 3743 3744
    if num_results_per_sample is None:
        num_results_per_sample = beam_size
    if num_results_per_sample > beam_size:
        logger.warning("num_results_per_sample should be less than beam_size")

Q
qijun 已提交
3745
    if isinstance(input, StaticInput) or isinstance(input, BaseGeneratedInput):
Z
zhangjinchao01 已提交
3746 3747 3748 3749 3750 3751
        input = [input]

    generated_input_index = -1

    real_input = []
    for i, each_input in enumerate(input):
3752 3753
        assert isinstance(each_input, StaticInput) or isinstance(
            each_input, BaseGeneratedInput)
Z
zhangjinchao01 已提交
3754 3755 3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769
        if isinstance(each_input, BaseGeneratedInput):
            assert generated_input_index == -1
            generated_input_index = i
        else:
            real_input.append(each_input)

    assert generated_input_index != -1

    gipt = input[generated_input_index]
    assert isinstance(gipt, BaseGeneratedInput)

    gipt.bos_id = bos_id
    gipt.eos_id = eos_id

    def __real_step__(*args):
        eos_name = "__%s_eos_layer__" % name
Q
qijun 已提交
3770 3771 3772 3773 3774 3775
        RecurrentLayerGroupSetGenerator(
            Generator(
                eos_layer_name=eos_name,
                max_num_frames=max_length,
                beam_size=beam_size,
                num_results_per_sample=num_results_per_sample))
Z
zhangjinchao01 已提交
3776 3777 3778 3779 3780 3781 3782 3783 3784 3785

        args = list(args)
        args.insert(generated_input_index, gipt.before_real_step())

        predict = gipt.after_real_step(step(*args))

        eos_layer(input=predict, eos_id=eos_id, name=eos_name)

        return predict

Q
qijun 已提交
3786
    tmp = recurrent_group(
L
Luo Tao 已提交
3787 3788 3789 3790
        step=__real_step__,
        input=real_input,
        reverse=False,
        name=name,
L
Luo Tao 已提交
3791
        is_generating=True)
3792

Z
zhangjinchao01 已提交
3793 3794
    return tmp

Q
qijun 已提交
3795

3796 3797
def __cost_input__(input, label, weight=None):
    """
3798
    inputs and parents for cost layers.
3799 3800 3801 3802
    """
    ipts = [Input(input.name), Input(label.name)]
    parents = [input, label]
    if weight is not None:
3803
        assert weight.size == 1
3804 3805 3806
        ipts.append(Input(weight.name))
        parents.append(weight)
    return ipts, parents
3807

Z
zhangjinchao01 已提交
3808 3809

@wrap_name_default()
L
luotao1 已提交
3810
@layer_support()
3811
def mse_cost(input, label, weight=None, name=None, coeff=1.0, layer_attr=None):
Z
zhangjinchao01 已提交
3812
    """
L
Luo Tao 已提交
3813 3814 3815 3816
    mean squared error cost:

    ..  math::

L
Liu Yiqun 已提交
3817
        \\frac{1}{N}\sum_{i=1}^N(t_i-y_i)^2
Z
zhangjinchao01 已提交
3818 3819

    :param name: layer name.
3820
    :type name: basestring
Z
zhangjinchao01 已提交
3821
    :param input: Network prediction.
3822
    :type input: LayerOutput
Z
zhangjinchao01 已提交
3823
    :param label: Data label.
3824 3825 3826 3827
    :type label: LayerOutput
    :param weight: The weight affects the cost, namely the scale of cost.
                   It is an optional argument.
    :type weight: LayerOutput
3828 3829
    :param coeff: The coefficient affects the gradient in the backward.
    :type coeff: float
L
luotao1 已提交
3830 3831
    :param layer_attr: layer's extra attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
3832
    :return: LayerOutput object.
3833
    :rtype: LayerOutput
Z
zhangjinchao01 已提交
3834
    """
3835 3836
    ipts, parents = __cost_input__(input, label, weight)

Q
qijun 已提交
3837 3838 3839 3840
    Layer(
        inputs=ipts,
        type="square_error",
        name=name,
3841
        coeff=coeff,
Q
qijun 已提交
3842
        **ExtraLayerAttribute.to_kwargs(layer_attr))
X
xuwei06 已提交
3843
    return LayerOutput(name, LayerType.COST, parents=parents, size=1)
Z
zhangjinchao01 已提交
3844 3845


L
Luo Tao 已提交
3846 3847 3848
regression_cost = mse_cost


Z
zhangjinchao01 已提交
3849
@wrap_name_default("cost")
3850
@layer_support()
Q
qijun 已提交
3851 3852 3853 3854
def classification_cost(input,
                        label,
                        weight=None,
                        name=None,
L
Liang Zhao 已提交
3855
                        top_k=None,
3856 3857
                        evaluator=classification_error_evaluator,
                        layer_attr=None):
Z
zhangjinchao01 已提交
3858 3859 3860 3861 3862 3863 3864 3865 3866
    """
    classification cost Layer.

    :param name: layer name.
    :type name: basestring
    :param input: input layer name. network output.
    :type input: LayerOutput
    :param label: label layer name. data_layer often.
    :type label: LayerOutput
3867 3868 3869
    :param weight: The weight affects the cost, namely the scale of cost.
                   It is an optional argument.
    :type weight: LayerOutput
L
Liang Zhao 已提交
3870 3871
    :param top_k: number k in top-k error rate
    :type top_k: int
Z
zhangjinchao01 已提交
3872
    :param evaluator: Evaluator method.
3873 3874
    :param layer_attr: layer's extra attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
3875
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3876 3877 3878 3879 3880
    :rtype: LayerOutput
    """
    assert input.layer_type != LayerType.DATA
    assert isinstance(input.activation, SoftmaxActivation)
    assert label.layer_type == LayerType.DATA
3881 3882 3883

    ipts, parents = __cost_input__(input, label, weight)

Q
qijun 已提交
3884 3885 3886 3887 3888
    Layer(
        name=name,
        type="multi-class-cross-entropy",
        inputs=ipts,
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
3889 3890 3891 3892 3893 3894 3895 3896 3897 3898

    def __add_evaluator__(e):
        assert callable(e)
        assert hasattr(e, 'is_evaluator')
        assert isinstance(e.is_evaluator, bool)
        assert e.is_evaluator
        assert hasattr(e, "for_classification")
        assert isinstance(e.for_classification, bool)
        assert e.for_classification

L
Liang Zhao 已提交
3899
        e(name=e.__name__, input=input, label=label, weight=weight, top_k=top_k)
Z
zhangjinchao01 已提交
3900

3901
    if not isinstance(evaluator, collections.Sequence):
Z
zhangjinchao01 已提交
3902 3903 3904 3905 3906
        evaluator = [evaluator]

    for each_evaluator in evaluator:
        __add_evaluator__(each_evaluator)

X
xuwei06 已提交
3907
    return LayerOutput(name, LayerType.COST, parents=parents, size=1)
Z
zhangjinchao01 已提交
3908

3909

Q
qijun 已提交
3910 3911 3912 3913 3914 3915 3916 3917 3918
def conv_operator(img,
                  filter,
                  filter_size,
                  num_filters,
                  num_channels=None,
                  stride=1,
                  padding=0,
                  filter_size_y=None,
                  stride_y=None,
3919 3920
                  padding_y=None,
                  trans=False):
Z
zhangjinchao01 已提交
3921 3922 3923 3924 3925 3926 3927 3928 3929 3930
    """
    Different from img_conv_layer, conv_op is an Operator, which can be used
    in mixed_layer. And conv_op takes two inputs to perform convolution.
    The first input is the image and the second is filter kernel. It only
    support GPU mode.

    The example usage is:

    .. code-block:: python

3931 3932
       op = conv_operator(img=input1,
                          filter=input2,
3933
                          filter_size=3,
Z
zhangjinchao01 已提交
3934 3935 3936
                          num_filters=64,
                          num_channels=64)

3937 3938 3939 3940
    :param img: input image
    :type img: LayerOutput
    :param filter: input filter
    :type filter: LayerOutput
Z
zhangjinchao01 已提交
3941 3942
    :param filter_size: The x dimension of a filter kernel.
    :type filter_size: int
C
caoying03 已提交
3943 3944 3945
    :param filter_size_y: The y dimension of a filter kernel. Since
                        PaddlePaddle now supports rectangular filters,
                        the filter's shape can be (filter_size, filter_size_y).
Z
zhangjinchao01 已提交
3946
    :type filter_size_y: int
3947 3948
    :param num_filters: channel of output data.
    :type num_filters: int
3949 3950
    :param num_channels: channel of input data.
    :type num_channels: int
Z
zhangjinchao01 已提交
3951
    :param stride: The x dimension of the stride.
L
luotao02 已提交
3952
    :type stride: int
Z
zhangjinchao01 已提交
3953
    :param stride_y: The y dimension of the stride.
L
luotao02 已提交
3954
    :type stride_y: int
Z
zhangjinchao01 已提交
3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967
    :param padding: The x dimension of padding.
    :type padding: int
    :param padding_y: The y dimension of padding.
    :type padding_y: int
    :return: A ConvOperator Object.
    :rtype: ConvOperator
    """
    if filter_size_y is None:
        filter_size_y = filter_size
    if stride_y is None:
        stride_y = stride
    if padding_y is None:
        padding_y = padding
3968

3969 3970
    if num_channels is None:
        num_channels = img.num_filters
3971 3972 3973

    assert isinstance(filter, LayerOutput)
    if filter.size is not None:
3974
        filter.size = filter_size * filter_size_y * num_filters * num_channels
3975

3976 3977 3978
    opCls = ConvTransOperator if trans else ConvOperator

    op = opCls(
Q
qijun 已提交
3979 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989
        input_layer_names=[img.name, filter.name],
        num_filters=num_filters,
        conv_conf=Conv(
            filter_size=filter_size,
            padding=padding,
            stride=stride,
            channels=num_channels,
            filter_size_y=filter_size_y,
            padding_y=padding_y,
            stride_y=stride_y,
            groups=1))
3990

3991
    op.origin = [img, filter]
Z
zhangjinchao01 已提交
3992 3993
    return op

Q
qijun 已提交
3994

3995
@wrap_param_attr_default()
Q
qijun 已提交
3996 3997 3998 3999 4000 4001 4002 4003 4004 4005
def conv_projection(input,
                    filter_size,
                    num_filters,
                    num_channels=None,
                    stride=1,
                    padding=0,
                    filter_size_y=None,
                    stride_y=None,
                    padding_y=None,
                    groups=1,
4006 4007
                    param_attr=None,
                    trans=False):
4008 4009 4010 4011 4012 4013 4014 4015 4016
    """
    Different from img_conv_layer and conv_op, conv_projection is an Projection,
    which can be used in mixed_layer and conat_layer. It use cudnn to implement
    conv and only support GPU mode.

    The example usage is:

    .. code-block:: python

D
dangqingqing 已提交
4017
       proj = conv_projection(input=input1,
4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029 4030 4031
                              filter_size=3,
                              num_filters=64,
                              num_channels=64)

    :param input: input layer
    :type input: LayerOutput
    :param filter_size: The x dimension of a filter kernel.
    :type filter_size: int
    :param filter_size_y: The y dimension of a filter kernel. Since
                          PaddlePaddle now supports rectangular filters,
                          the filter's shape can be (filter_size, filter_size_y).
    :type filter_size_y: int
    :param num_filters: channel of output data.
    :type num_filters: int
4032 4033
    :param num_channels: channel of input data.
    :type num_channels: int
4034 4035 4036 4037 4038 4039 4040 4041 4042 4043 4044 4045
    :param stride: The x dimension of the stride.
    :type stride: int
    :param stride_y: The y dimension of the stride.
    :type stride_y: int
    :param padding: The x dimension of padding.
    :type padding: int
    :param padding_y: The y dimension of padding.
    :type padding_y: int
    :param groups: The group number.
    :type groups: int
    :param param_attr: Convolution param attribute. None means default attribute
    :type param_attr: ParameterAttribute
4046 4047
    :param trans: whether it is convTrans or conv
    :type trans: boolean
4048 4049 4050 4051 4052 4053 4054 4055 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 4070 4071 4072 4073 4074 4075 4076 4077
    :return: A DotMulProjection Object.
    :rtype: DotMulProjection
    """
    if num_channels is None:
        assert input.num_filters is not None
        num_channels = input.num_filters

    if filter_size_y is None:
        if isinstance(filter_size, collections.Sequence):
            assert len(filter_size) == 2
            filter_size, filter_size_y = filter_size
        else:
            filter_size_y = filter_size

    if stride_y is None:
        if isinstance(stride, collections.Sequence):
            assert len(stride) == 2
            stride, stride_y = stride
        else:
            stride_y = stride

    if padding_y is None:
        if isinstance(padding, collections.Sequence):
            assert len(padding) == 2
            padding, padding_y = padding
        else:
            padding_y = padding

    if param_attr.attr.get('initial_smart'):
        # special initial for conv layers.
Q
qijun 已提交
4078
        init_w = (2.0 / (filter_size**2 * num_channels))**0.5
4079 4080 4081 4082 4083
        param_attr.attr["initial_mean"] = 0.0
        param_attr.attr["initial_std"] = init_w
        param_attr.attr["initial_strategy"] = 0
        param_attr.attr["initial_smart"] = False

4084 4085 4086
    projCls = ConvTransProjection if trans else ConvProjection

    proj = projCls(
Q
qijun 已提交
4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 4098
        input_layer_name=input.name,
        num_filters=num_filters,
        conv_conf=Conv(
            filter_size=filter_size,
            padding=padding,
            stride=stride,
            channels=num_channels,
            filter_size_y=filter_size_y,
            padding_y=padding_y,
            stride_y=stride_y,
            groups=groups),
        **param_attr.attr)
4099 4100 4101 4102

    proj.origin = input
    return proj

Z
zhangjinchao01 已提交
4103

D
dangqingqing 已提交
4104 4105 4106 4107 4108 4109 4110 4111 4112 4113 4114 4115 4116 4117 4118 4119 4120
@wrap_name_default("pad")
@layer_support()
def pad_layer(input,
              pad_c=None,
              pad_h=None,
              pad_w=None,
              name=None,
              layer_attr=None):
    """
    This operation pads zeros to the input data according to pad_c,pad_h
    and pad_w. pad_c, pad_h, pad_w specifies the which dimension and size
    of padding. And the input data shape is NCHW.

    For example, pad_c=[2,3] means padding 2 zeros before the
    input data and 3 zeros after the input data in channel dimension.
    pad_h means padding zeros in height dimension. pad_w means padding zeros
    in width dimension.
4121

D
dangqingqing 已提交
4122
    For example,
4123

4124 4125 4126 4127 4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139 4140 4141 4142 4143 4144
    .. code-block:: python

       input(2,2,2,3)  = [
                           [ [[1,2,3], [3,4,5]],
                             [[2,3,5], [1,6,7]] ],
                           [ [[4,3,1], [1,8,7]],
                             [[3,8,9], [2,3,5]] ]
                         ]

       pad_c=[1,1], pad_h=[0,0], pad_w=[0,0]

       output(2,4,2,3) = [
                           [ [[0,0,0], [0,0,0]],
                             [[1,2,3], [3,4,5]],
                             [[2,3,5], [1,6,7]],
                             [[0,0,0], [0,0,0]] ],
                           [ [[0,0,0], [0,0,0]],
                             [[4,3,1], [1,8,7]],
                             [[3,8,9], [2,3,5]],
                             [[0,0,0], [0,0,0]] ]
                         ]
D
dangqingqing 已提交
4145 4146

    The simply usage is:
D
dangqingqing 已提交
4147 4148 4149 4150 4151 4152 4153 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 4168 4169 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 4182 4183 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195 4196 4197 4198 4199 4200 4201 4202 4203 4204 4205 4206 4207

    .. code-block:: python

       pad = pad_layer(input=ipt,
                       pad_c=[4,4],
                       pad_h=[0,0],
                       pad_w=[2,2])

    :param input: layer's input.
    :type input: LayerOutput
    :param pad_c: padding size in channel dimension.
    :type pad_c: list|None
    :param pad_h: padding size in height dimension.
    :type pad_h: list|None
    :param pad_w: padding size in width dimension.
    :type pad_w: list|None
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
    :param name: layer name.
    :type name: basestring
    :return: LayerOutput object.
    :rtype: LayerOutput
    """
    if pad_c is not None:
        assert isinstance(pad_c, collections.Sequence) and len(pad_c) == 2
    else:
        pad_c = [0, 0]

    if pad_h is not None:
        assert isinstance(pad_h, collections.Sequence) and len(pad_h) == 2
    else:
        pad_h = [0, 0]

    if pad_w is not None:
        assert isinstance(pad_w, collections.Sequence) and len(pad_w) == 2
    else:
        pad_w = [0, 0]

    assert input.num_filters is not None
    in_ch = input.num_filters
    out_ch = in_ch + pad_c[0] + pad_c[1]

    l = Layer(
        name=name,
        type=LayerType.PAD_LAYER,
        inputs=Input(
            input.name,
            pad=Pad(
                channels=in_ch,
                pad_c=pad_c,
                pad_h=pad_h,
                pad_w=pad_w, )),
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name,
        layer_type=LayerType.PAD_LAYER,
        parents=[input],
        num_filters=out_ch,
        size=l.config.size)


Z
zhangjinchao01 已提交
4208
@wrap_name_default()
L
luotao1 已提交
4209 4210
@layer_support()
def conv_shift_layer(a, b, name=None, layer_attr=None):
Z
zhangjinchao01 已提交
4211 4212 4213 4214 4215 4216 4217 4218 4219 4220 4221
    """
    This layer performs cyclic convolution for two input. For example:
      - a[in]: contains M elements.
      - b[in]: contains N elements (N should be odd).
      - c[out]: contains M elements.

    .. math::

        c[i] = \sum_{j=-(N-1)/2}^{(N-1)/2}a_{i+j} * b_{j}

    In this formular:
4222 4223 4224 4225
     - a's index is computed modulo M. When it is negative, then get item from
       the right side (which is the end of array) to the left.
     - b's index is computed modulo N. When it is negative, then get item from
       the right size (which is the end of array) to the left.
Z
zhangjinchao01 已提交
4226 4227 4228 4229 4230

    The example usage is:

    .. code-block:: python

L
Luo Tao 已提交
4231
       conv_shift = conv_shift_layer(a=layer1, b=layer2)
Z
zhangjinchao01 已提交
4232 4233 4234

    :param name: layer name
    :type name: basestring
4235 4236
    :param a: Input layer a.
    :type a: LayerOutput
L
Luo Tao 已提交
4237
    :param b: input layer b.
4238
    :type b: LayerOutput
L
luotao1 已提交
4239 4240
    :param layer_attr: layer's extra attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
4241
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4242 4243
    :rtype: LayerOutput
    """
4244 4245
    assert isinstance(a, LayerOutput) and isinstance(b, LayerOutput)
    assert b.size is None or b.size % 2 == 1  # size of b must be odd.
Z
zhangjinchao01 已提交
4246 4247 4248
    Layer(
        name=name,
        type=LayerType.CONV_SHIFT_LAYER,
4249
        inputs=[a.name, b.name],
Q
qijun 已提交
4250
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
4251

Q
qijun 已提交
4252 4253
    return LayerOutput(
        name, LayerType.CONV_SHIFT_LAYER, parents=[a, b], size=a.size)
Z
zhangjinchao01 已提交
4254 4255 4256 4257 4258


@wrap_name_default()
@wrap_param_attr_default()
@wrap_bias_attr_default()
4259
@wrap_act_default(act=LinearActivation())
Z
zhangjinchao01 已提交
4260
@layer_support(ERROR_CLIPPING, DROPOUT)
Q
qijun 已提交
4261 4262 4263 4264 4265 4266 4267 4268
def tensor_layer(a,
                 b,
                 size,
                 act=None,
                 name=None,
                 param_attr=None,
                 bias_attr=None,
                 layer_attr=None):
Z
zhangjinchao01 已提交
4269 4270 4271 4272 4273
    """
    This layer performs tensor operation for two input.
    For example, each sample:

    .. math::
4274
       y_{i} = a * W_{i} * {b^\mathrm{T}}, i=0,1,...,K-1
Z
zhangjinchao01 已提交
4275 4276

    In this formular:
4277 4278
      - :math:`a`: the first input contains M elements.
      - :math:`b`: the second input contains N elements.
Z
zhangjinchao01 已提交
4279 4280
      - :math:`y_{i}`: the i-th element of y.
      - :math:`W_{i}`: the i-th learned weight, shape if [M, N]
4281
      - :math:`b^\mathrm{T}`: the transpose of :math:`b_{2}`.
Z
zhangjinchao01 已提交
4282 4283 4284 4285 4286

    The simple usage is:

    .. code-block:: python

4287
       tensor = tensor_layer(a=layer1, b=layer2, size=1000)
Z
zhangjinchao01 已提交
4288 4289 4290

    :param name: layer name
    :type name: basestring
4291 4292 4293 4294
    :param a: Input layer a.
    :type a: LayerOutput
    :param b: input layer b.
    :type b: LayerOutput
Z
zhangjinchao01 已提交
4295
    :param size: the layer dimension.
L
luotao02 已提交
4296
    :type size: int.
Z
zhangjinchao01 已提交
4297 4298 4299
    :param act: Activation Type. Default is tanh.
    :type act: BaseActivation
    :param param_attr: The Parameter Attribute.
4300
    :type param_attr: ParameterAttribute
Z
zhangjinchao01 已提交
4301 4302 4303 4304 4305 4306
    :param bias_attr: The Bias Attribute. If no bias, then pass False or
                      something not type of ParameterAttribute. None will get a
                      default Bias.
    :type bias_attr: ParameterAttribute|None|Any
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
4307
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4308 4309
    :rtype: LayerOutput
    """
4310
    assert isinstance(a, LayerOutput) and isinstance(b, LayerOutput)
Z
zhangjinchao01 已提交
4311 4312 4313 4314 4315 4316
    Layer(
        name=name,
        size=size,
        type=LayerType.TENSOR_LAYER,
        active_type=act.name,
        bias=ParamAttr.to_bias(bias_attr),
Q
qijun 已提交
4317 4318 4319 4320
        inputs=[Input(a.name, **param_attr.attr), Input(b.name)],
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.TENSOR_LAYER, parents=[a, b], activation=act, size=size)
Z
zhangjinchao01 已提交
4321 4322 4323 4324 4325 4326


@wrap_name_default()
@wrap_param_attr_default()
@wrap_bias_attr_default()
@wrap_act_default()
L
luotao1 已提交
4327
@layer_support()
Q
qijun 已提交
4328 4329
def selective_fc_layer(input,
                       size,
L
Luo Tao 已提交
4330
                       select=None,
Q
qijun 已提交
4331 4332
                       act=None,
                       name=None,
Z
zhangjinchao01 已提交
4333 4334 4335
                       pass_generation=False,
                       has_selected_colums=True,
                       mul_ratio=0.02,
Q
qijun 已提交
4336 4337 4338
                       param_attr=None,
                       bias_attr=None,
                       layer_attr=None):
Z
zhangjinchao01 已提交
4339 4340 4341 4342 4343 4344 4345 4346 4347 4348
    """
    Selectived fully connected layer. Different from fc_layer, the output
    of this layer maybe sparse. It requires an additional input to indicate
    several selected columns for output. If the selected columns is not
    specified, selective_fc_layer acts exactly like fc_layer.

    The simple usage is:

    .. code-block:: python

4349
       sel_fc = selective_fc_layer(input=input, size=128, act=TanhActivation())
Z
zhangjinchao01 已提交
4350 4351 4352 4353 4354

    :param name: The Layer Name.
    :type name: basestring
    :param input: The input layer.
    :type input: LayerOutput|list|tuple
4355 4356
    :param select: The select layer. The output of select layer should be a
                   sparse binary matrix, and treat as the mask of selective fc.
L
Luo Tao 已提交
4357
                   If is None, acts exactly like fc_layer.
4358
    :type select: LayerOutput
Z
zhangjinchao01 已提交
4359 4360 4361 4362 4363 4364 4365 4366 4367 4368 4369 4370
    :param size: The layer dimension.
    :type size: int
    :param act: Activation Type. Default is tanh.
    :type act: BaseActivation
    :param param_attr: The Parameter Attribute.
    :type param_attr: ParameterAttribute
    :param bias_attr: The Bias Attribute. If no bias, then pass False or
                      something not type of ParameterAttribute. None will get a
                      default Bias.
    :type bias_attr: ParameterAttribute|None|Any
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
4371
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4372 4373 4374 4375
    :rtype: LayerOutput
    """
    if isinstance(input, LayerOutput):
        input = [input]
4376
        assert not isinstance(param_attr, collections.Sequence)
Z
zhangjinchao01 已提交
4377 4378
        param_attr = [param_attr]
    else:
4379
        if isinstance(param_attr, collections.Sequence):
Z
zhangjinchao01 已提交
4380 4381 4382 4383
            assert len(input) == len(param_attr)
        else:
            param_attr = [copy.deepcopy(param_attr) for _ in range(len(input))]

4384 4385 4386 4387
    assert isinstance(input, collections.Sequence)
    assert isinstance(select, LayerOutput)
    if select.size is not None:
        assert select.size == size
Z
zhangjinchao01 已提交
4388
    Layer(
Q
qijun 已提交
4389 4390 4391
        inputs=[
            Input(ipt.name, **attr.attr) for ipt, attr in zip(input, param_attr)
        ] + [select.name],
Z
zhangjinchao01 已提交
4392 4393 4394
        name=name,
        type=LayerType.SEL_FC_LAYER,
        size=size,
4395
        bias=ParameterAttribute.to_bias(bias_attr),
Z
zhangjinchao01 已提交
4396 4397 4398 4399
        active_type=act.name,
        selective_fc_pass_generation=pass_generation,
        has_selected_colums=has_selected_colums,
        selective_fc_full_mul_ratio=mul_ratio,
Q
qijun 已提交
4400 4401 4402 4403 4404 4405 4406
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name,
        LayerType.SEL_FC_LAYER,
        list(input) + [select],
        activation=act,
        size=size)
Z
zhangjinchao01 已提交
4407 4408 4409


@wrap_name_default()
L
luotao1 已提交
4410 4411
@layer_support()
def sampling_id_layer(input, name=None, layer_attr=None):
Z
zhangjinchao01 已提交
4412 4413 4414 4415 4416 4417 4418 4419 4420 4421 4422 4423 4424 4425
    """
    A layer for sampling id from multinomial distribution from the input layer.
    Sampling one id for one sample.

    The simple usage is:

    .. code-block:: python

       samping_id = sampling_id_layer(input=input)

    :param input: The input layer.
    :type input: LayerOutput
    :param name: The Layer Name.
    :type name: basestring
L
luotao1 已提交
4426 4427
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
4428
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4429 4430
    :rtype: LayerOutput
    """
X
xuwei06 已提交
4431
    l = Layer(
Z
zhangjinchao01 已提交
4432 4433 4434
        name=name,
        type=LayerType.SAMPLING_ID_LAYER,
        inputs=[Input(input.name)],
Q
qijun 已提交
4435 4436 4437
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.SAMPLING_ID_LAYER, input, size=l.config.size)
Z
zhangjinchao01 已提交
4438 4439 4440


@wrap_name_default()
L
luotao1 已提交
4441
@layer_support()
Q
qijun 已提交
4442 4443 4444 4445
def slope_intercept_layer(input,
                          name=None,
                          slope=1.0,
                          intercept=0.0,
L
luotao1 已提交
4446
                          layer_attr=None):
Z
zhangjinchao01 已提交
4447 4448 4449 4450 4451 4452 4453 4454 4455 4456 4457 4458 4459 4460 4461 4462 4463 4464 4465 4466 4467
    """
    This layer for applying a slope and an intercept to the input
    element-wise. There is no activation and weight.

    ..  math::
        y = slope * x + intercept

    The simple usage is:

    .. code-block:: python

       scale = slope_intercept_layer(input=input, slope=-1.0, intercept=1.0)

    :param input: The input layer.
    :type input: LayerOutput
    :param name: The Layer Name.
    :type name: basestring
    :param slope: the scale factor.
    :type slope: float.
    :param intercept: the offset.
    :type intercept: float.
L
luotao1 已提交
4468 4469
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
4470
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4471 4472 4473 4474 4475 4476 4477 4478
    :rtype: LayerOutput
    """
    Layer(
        name=name,
        type=LayerType.SLOPE_INTERCEPT_LAYER,
        slope=slope,
        intercept=intercept,
        inputs=[Input(input.name)],
Q
qijun 已提交
4479 4480 4481
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.SLOPE_INTERCEPT_LAYER, input, size=input.size)
Z
zhangjinchao01 已提交
4482 4483 4484


@wrap_name_default()
L
luotao1 已提交
4485
@layer_support()
Q
qijun 已提交
4486
def linear_comb_layer(weights, vectors, size=None, name=None, layer_attr=None):
Z
zhangjinchao01 已提交
4487
    """
4488 4489 4490 4491
    A layer for weighted sum of vectors takes two inputs.
      - Input: size of weights is M
               size of vectors is M*N
      - Output: a vector of size=N
Z
zhangjinchao01 已提交
4492 4493 4494

    .. math::

4495
       z(i) = \sum_{j=0}^{M-1} x(j) y(i+Nj)
4496

4497 4498 4499 4500 4501
    where :math:`0 \le i \le N-1`

    Or in the matrix notation:

    .. math::
Z
zhangjinchao01 已提交
4502

4503
       z = x^\mathrm{T} Y
Z
zhangjinchao01 已提交
4504 4505

    In this formular:
4506 4507 4508 4509 4510 4511
      - :math:`x`: weights
      - :math:`y`: vectors.
      - :math:`z`: the output.

    Note that the above computation is for one sample. Multiple samples are
    processed in one batch.
Z
zhangjinchao01 已提交
4512 4513 4514 4515 4516

    The simple usage is:

    .. code-block:: python

4517
       linear_comb = linear_comb_layer(weights=weight, vectors=vectors,
Z
zhangjinchao01 已提交
4518 4519
                                       size=elem_dim)

4520 4521 4522 4523
    :param weights: The weight layer.
    :type weights: LayerOutput
    :param vectors: The vector layer.
    :type vectors: LayerOutput
Z
zhangjinchao01 已提交
4524 4525 4526 4527
    :param size: the dimension of this layer.
    :type size: int
    :param name: The Layer Name.
    :type name: basestring
L
luotao1 已提交
4528 4529
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
4530
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4531 4532
    :rtype: LayerOutput
    """
4533 4534 4535 4536
    assert isinstance(weights, LayerOutput) and isinstance(vectors, LayerOutput)
    if vectors.size is not None and weights.size is not None:
        assert vectors.size % weights.size == 0
        if size is None:
Q
qijun 已提交
4537
            size = vectors.size / weights.size
4538 4539
        else:
            assert size == vectors.size / weights.size
Z
zhangjinchao01 已提交
4540 4541
    Layer(
        name=name,
4542
        type=LayerType.LINEAR_COMBINATION_LAYER,
Z
zhangjinchao01 已提交
4543
        size=size,
4544
        inputs=[Input(weights.name), Input(vectors.name)],
Q
qijun 已提交
4545 4546 4547
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.LINEAR_COMBINATION_LAYER, [weights, vectors], size=size)
4548

4549

4550
convex_comb_layer = linear_comb_layer
Z
zhangjinchao01 已提交
4551

4552

Z
zhangjinchao01 已提交
4553
@wrap_name_default()
L
luotao1 已提交
4554
@layer_support()
Z
zhangjinchao01 已提交
4555 4556 4557 4558 4559 4560 4561
def block_expand_layer(input,
                       block_x=0,
                       block_y=0,
                       stride_x=0,
                       stride_y=0,
                       padding_x=0,
                       padding_y=0,
4562
                       num_channels=None,
L
luotao1 已提交
4563 4564
                       name=None,
                       layer_attr=None):
Z
zhangjinchao01 已提交
4565 4566
    """
    Expand feature map to minibatch matrix.
4567
       - matrix width is: block_y * block_x * num_channels
L
luotao02 已提交
4568
       - matirx height is: outputH * outputW
Z
zhangjinchao01 已提交
4569 4570 4571 4572 4573 4574 4575 4576 4577 4578

    .. math::

       outputH = 1 + (2 * padding_y + imgSizeH - block_y + stride_y - 1) / stride_y

       outputW = 1 + (2 * padding_x + imgSizeW - block_x + stride_x - 1) / stride_x

    The expand method is the same with ExpandConvLayer, but saved the transposed
    value. After expanding, output.sequenceStartPositions will store timeline.
    The number of time steps are outputH * outputW and the dimension of each
4579
    time step is block_y * block_x * num_channels. This layer can be used after
Z
zhangjinchao01 已提交
4580 4581
    convolution neural network, and before recurrent neural network.

4582 4583 4584 4585
    The simple usage is:

    .. code-block:: python

L
Luo Tao 已提交
4586
       block_expand = block_expand_layer(input=layer,
4587
                                         num_channels=128,
4588 4589 4590 4591 4592
                                         stride_x=1,
                                         stride_y=1,
                                         block_x=1,
                                         block_x=3)

Z
zhangjinchao01 已提交
4593 4594
    :param input: The input layer.
    :type input: LayerOutput
4595 4596
    :param num_channels: The channel number of input layer.
    :type num_channels: int|None
Z
zhangjinchao01 已提交
4597 4598 4599 4600 4601 4602 4603 4604 4605 4606 4607 4608 4609 4610
    :param block_x: The width of sub block.
    :type block_x: int
    :param block_y: The width of sub block.
    :type block_y: int
    :param stride_x: The stride size in horizontal direction.
    :type stride_x: int
    :param stride_y: The stride size in vertical direction.
    :type stride_y: int
    :param padding_x: The padding size in horizontal direction.
    :type padding_x: int
    :param padding_y: The padding size in vertical direction.
    :type padding_y: int
    :param name: The name of this layer, which can not specify.
    :type name: None|basestring.
L
luotao1 已提交
4611 4612
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
4613
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4614 4615
    :rtype: LayerOutput
    """
4616 4617 4618
    if num_channels is None:
        assert input.num_filters is not None
        num_channels = input.num_filters
Q
qijun 已提交
4619 4620 4621 4622 4623 4624 4625 4626 4627 4628 4629 4630 4631 4632 4633 4634 4635
    l = Layer(
        name=name,
        inputs=Input(
            input.name,
            block_expand=BlockExpand(
                channels=num_channels,
                block_x=block_x,
                block_y=block_y,
                stride_x=stride_x,
                stride_y=stride_y,
                padding_x=padding_x,
                padding_y=padding_y)),
        type=LayerType.BLOCK_EXPAND,
        **ExtraLayerAttribute.to_kwargs(layer_attr))

    return LayerOutput(
        name, LayerType.BLOCK_EXPAND, parents=[input], size=l.config.size)
Z
zhangjinchao01 已提交
4636 4637


4638 4639
@wrap_name_default()
@layer_support()
4640
def maxout_layer(input, groups, num_channels=None, name=None, layer_attr=None):
4641 4642 4643 4644 4645
    """
    A layer to do max out on conv layer output.
      - Input: output of a conv layer.
      - Output: feature map size same as input. Channel is (input channel) / groups.

4646
    So groups should be larger than 1, and the num of channels should be able
4647 4648
    to devided by groups.

4649
    Please refer to Paper:
4650 4651 4652 4653
      - Maxout Networks: http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf
      - Multi-digit Number Recognition from Street View \
        Imagery using Deep Convolutional Neural Networks: \
        https://arxiv.org/pdf/1312.6082v4.pdf
4654

4655 4656 4657 4658 4659 4660 4661 4662 4663 4664 4665 4666 4667 4668 4669 4670 4671 4672 4673 4674 4675 4676 4677 4678 4679 4680 4681 4682 4683
    The simple usage is:

    .. code-block:: python

       maxout = maxout_layer(input,
                             num_channels=128,
                             groups=4)

    :param input: The input layer.
    :type input: LayerOutput
    :param num_channels: The channel number of input layer. If None will be set
                     automatically from previous output.
    :type num_channels: int|None
    :param groups: The group number of input layer.
    :type groups: int
    :param name: The name of this layer, which can not specify.
    :type name: None|basestring.
    :param layer_attr: Extra Layer attribute.
    :type layer_attr: ExtraLayerAttribute
    :return: LayerOutput object.
    :rtype: LayerOutput
    """
    assert input.layer_type == LayerType.CONV_LAYER
    assert isinstance(input.activation, LinearActivation)
    assert groups > 1
    if num_channels is None:
        assert input.num_filters is not None
        num_channels = input.num_filters
    assert num_channels % groups == 0
Q
qijun 已提交
4684 4685 4686 4687 4688 4689 4690 4691 4692
    l = Layer(
        name=name,
        inputs=Input(
            input.name, maxout=MaxOut(
                channels=num_channels, groups=groups)),
        type=LayerType.MAXOUT,
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.MAXOUT, parents=[input], size=l.config.size)
4693 4694


Z
zhangjinchao01 已提交
4695
@wrap_name_default()
L
luotao1 已提交
4696
@layer_support()
Q
qijun 已提交
4697 4698 4699 4700 4701
def ctc_layer(input,
              label,
              size=None,
              name=None,
              norm_by_times=False,
L
luotao1 已提交
4702
              layer_attr=None):
Z
zhangjinchao01 已提交
4703 4704 4705 4706 4707
    """
    Connectionist Temporal Classification (CTC) is designed for temporal
    classication task. That is, for sequence labeling problems where the
    alignment between the inputs and the target labels is unknown.

4708 4709
    More details can be found by referring to `Connectionist Temporal
    Classification: Labelling Unsegmented Sequence Data with Recurrent
4710 4711
    Neural Networks <http://machinelearning.wustl.edu/mlpapers/paper_files/
    icml2006_GravesFGS06.pdf>`_
4712 4713 4714 4715 4716 4717 4718 4719

    Note:
        Considering the 'blank' label needed by CTC, you need to use
        (num_classes + 1) as the input size. num_classes is the category number.
        And the 'blank' is the last category index. So the size of 'input' layer, such as
        fc_layer with softmax activation, should be num_classes + 1. The size of ctc_layer
        should also be num_classes + 1.

Z
zhangjinchao01 已提交
4720 4721 4722 4723 4724 4725 4726 4727 4728
    The simple usage:

    .. code-block:: python

      ctc = ctc_layer(input=input,
                      label=label,
                      size=9055,
                      norm_by_times=True)

4729
    :param input: The input layer.
Z
zhangjinchao01 已提交
4730 4731 4732
    :type input: LayerOutput
    :param label: The data layer of label with variable length.
    :type label: LayerOutput
4733
    :param size: category numbers + 1.
Z
zhangjinchao01 已提交
4734
    :type size: int
4735 4736
    :param name: The name of this layer
    :type name: basestring|None
Z
zhangjinchao01 已提交
4737 4738
    :param norm_by_times: Whether to normalization by times. False by default.
    :type norm_by_times: bool
L
luotao1 已提交
4739 4740
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
4741
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4742 4743 4744 4745
    :rtype: LayerOutput
    """
    assert isinstance(input, LayerOutput)
    assert isinstance(label, LayerOutput)
4746 4747 4748 4749 4750
    if label.size is not None:
        if size is not None:
            assert size == label.size + 1
        else:
            size = label.size + 1
Z
zhangjinchao01 已提交
4751
    Layer(
4752 4753 4754 4755
        name=name,
        type=LayerType.CTC_LAYER,
        size=size,
        norm_by_times=norm_by_times,
L
luotao1 已提交
4756
        inputs=[input.name, label.name],
Q
qijun 已提交
4757
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
4758 4759
    return LayerOutput(name, LayerType.CTC_LAYER, [input, label], size=size)

4760

4761 4762 4763 4764 4765 4766 4767 4768 4769 4770 4771
@wrap_name_default()
@layer_support()
def warp_ctc_layer(input,
                   label,
                   size=None,
                   name=None,
                   blank=0,
                   norm_by_times=False,
                   layer_attr=None):
    """
    A layer intergrating the open-source `warp-ctc
L
Liu Yiqun 已提交
4772
    <https://github.com/baidu-research/warp-ctc>`_ library, which is used in
4773
    `Deep Speech 2: End-toEnd Speech Recognition in English and Mandarin
L
Liu Yiqun 已提交
4774 4775 4776 4777 4778 4779 4780 4781 4782 4783 4784 4785 4786 4787 4788 4789 4790
    <https://arxiv.org/pdf/1512.02595v1.pdf>`_, to compute Connectionist Temporal
    Classification (CTC) loss. Besides, another `warp-ctc
    <https://github.com/gangliao/warp-ctc>`_ repository, which is forked from
    the official one, is maintained to enable more compiling options. During the
    building process, PaddlePaddle will clone the source codes, build and
    install it to :code:`third_party/install/warpctc` directory.

    To use warp_ctc layer, you need to specify the path of :code:`libwarpctc.so`,
    using following methods:

    1. Set it in :code:`paddle.init` (python api) or :code:`paddle_init` (c api),
    such as :code:`paddle.init(use_gpu=True,
    warpctc_dir=your_paddle_source_dir/third_party/install/warpctc/lib)`.

    2. Set environment variable LD_LIBRARY_PATH on Linux or DYLD_LIBRARY_PATH
    on Mac OS. For instance, :code:`export
    LD_LIBRARY_PATH=your_paddle_source_dir/third_party/install/warpctc/lib:$LD_LIBRARY_PATH`.
4791 4792 4793 4794

    More details of CTC can be found by referring to `Connectionist Temporal
    Classification: Labelling Unsegmented Sequence Data with Recurrent
    Neural Networks <http://machinelearning.wustl.edu/mlpapers/paper_files/
L
Liu Yiqun 已提交
4795
    icml2006_GravesFGS06.pdf>`_.
4796 4797 4798

    Note:
        - Let num_classes represent the category number. Considering the 'blank'
L
Liu Yiqun 已提交
4799 4800 4801
          label needed by CTC, you need to use (num_classes + 1) as the input size.
          Thus, the size of both warp_ctc layer and 'input' layer should be set to
          num_classes + 1.
4802 4803
        - You can set 'blank' to any value ranged in [0, num_classes], which
          should be consistent as that used in your labels.
4804
        - As a native 'softmax' activation is interated to the warp-ctc library,
L
Luo Tao 已提交
4805
          'linear' activation is expected instead in the 'input' layer.
4806 4807 4808 4809 4810 4811 4812 4813 4814 4815 4816 4817 4818 4819 4820 4821 4822 4823 4824 4825 4826 4827 4828 4829 4830 4831 4832 4833 4834 4835 4836 4837 4838 4839 4840 4841 4842 4843 4844 4845 4846 4847 4848 4849 4850 4851 4852

    The simple usage:

    .. code-block:: python

      ctc = warp_ctc_layer(input=input,
                           label=label,
                           size=1001,
                           blank=1000,
                           norm_by_times=False)

    :param input: The input layer.
    :type input: LayerOutput
    :param label: The data layer of label with variable length.
    :type label: LayerOutput
    :param size: category numbers + 1.
    :type size: int
    :param name: The name of this layer, which can not specify.
    :type name: basestring|None
    :param blank: the 'blank' label used in ctc
    :type blank: int
    :param norm_by_times: Whether to normalization by times. False by default.
    :type norm_by_times: bool
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
    :return: LayerOutput object.
    :rtype: LayerOutput
    """
    assert isinstance(input, LayerOutput)
    assert isinstance(label, LayerOutput)
    if label.size is not None:
        if size is not None:
            assert size == label.size + 1
        else:
            size = label.size + 1
    Layer(
        name=name,
        type=LayerType.WARP_CTC_LAYER,
        size=size,
        blank=blank,
        norm_by_times=norm_by_times,
        inputs=[input.name, label.name],
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.WARP_CTC_LAYER, parents=[input, label], size=size)


Z
zhangjinchao01 已提交
4853
@wrap_name_default()
4854
@wrap_param_attr_default()
L
luotao1 已提交
4855
@layer_support()
Q
qijun 已提交
4856 4857 4858 4859 4860 4861
def crf_layer(input,
              label,
              size=None,
              weight=None,
              param_attr=None,
              name=None,
4862
              coeff=1.0,
L
luotao1 已提交
4863
              layer_attr=None):
Z
zhangjinchao01 已提交
4864 4865 4866 4867 4868 4869 4870 4871 4872 4873 4874 4875 4876 4877 4878
    """
    A layer for calculating the cost of sequential conditional random
    field model.

    The simple usage:

    .. code-block:: python

      crf = crf_layer(input=input,
                      label=label,
                      size=label_dim)

    :param input: The first input layer is the feature.
    :type input: LayerOutput
    :param label: The second input layer is label.
4879
    :type label: LayerOutput
Z
zhangjinchao01 已提交
4880 4881 4882 4883 4884 4885 4886 4887 4888
    :param size: The category number.
    :type size: int
    :param weight: The third layer is "weight" of each sample, which is an
                  optional argument.
    :type weight: LayerOutput
    :param param_attr: Parameter attribute. None means default attribute
    :type param_attr: ParameterAttribute
    :param name: The name of this layers. It is not necessary.
    :type name: None|basestring
4889 4890
    :param coeff: The coefficient affects the gradient in the backward.
    :type coeff: float
L
luotao1 已提交
4891 4892
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
4893
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4894 4895 4896 4897 4898
    :rtype: LayerOutput
    """
    assert isinstance(input, LayerOutput)
    assert isinstance(label, LayerOutput)
    assert weight is None or isinstance(weight, LayerOutput)
4899 4900 4901 4902 4903 4904
    if input.size is not None and label.size is not None:
        assert input.size == label.size
        if size is None:
            size = input.size
        else:
            assert size == input.size
Z
zhangjinchao01 已提交
4905

Q
qijun 已提交
4906
    ipts = [Input(input.name, **param_attr.attr), Input(label.name)]
Z
zhangjinchao01 已提交
4907 4908 4909 4910
    if weight is not None:
        ipts.append(Input(weight.name))

    Layer(
4911 4912 4913 4914
        name=name,
        type=LayerType.CRF_LAYER,
        size=size,
        inputs=ipts,
4915
        coeff=coeff,
Q
qijun 已提交
4916
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
4917 4918 4919
    parents = [input, label]
    if weight is not None:
        parents.append(weight)
X
xuwei06 已提交
4920 4921 4922 4923
    # The size for LayerOutput means the dimension of the output.
    # It's different from the meaning of crf layer, which is the number of
    # classes.
    return LayerOutput(name, LayerType.CRF_LAYER, parents, size=1)
Z
zhangjinchao01 已提交
4924

4925

Z
zhangjinchao01 已提交
4926
@wrap_name_default()
4927
@wrap_param_attr_default()
L
luotao1 已提交
4928
@layer_support()
Q
qijun 已提交
4929 4930 4931 4932 4933
def crf_decoding_layer(input,
                       size,
                       label=None,
                       param_attr=None,
                       name=None,
L
luotao1 已提交
4934
                       layer_attr=None):
Z
zhangjinchao01 已提交
4935 4936 4937 4938 4939 4940 4941
    """
    A layer for calculating the decoding sequence of sequential conditional
    random field model. The decoding sequence is stored in output.ids.
    If a second input is provided, it is treated as the ground-truth label, and
    this layer will also calculate error. output.value[i] is 1 for incorrect
    decoding or 0 for correct decoding.

L
Luo Tao 已提交
4942 4943 4944 4945 4946 4947 4948
    The simple usage:

    .. code-block:: python

      crf_decoding = crf_decoding_layer(input=input,
                                        size=label_dim)

Z
zhangjinchao01 已提交
4949 4950 4951 4952 4953 4954 4955 4956 4957 4958
    :param input: The first input layer.
    :type input: LayerOutput
    :param size: size of this layer.
    :type size: int
    :param label: None or ground-truth label.
    :type label: LayerOutput or None
    :param param_attr: Parameter attribute. None means default attribute
    :type param_attr: ParameterAttribute
    :param name: The name of this layers. It is not necessary.
    :type name: None|basestring
L
luotao1 已提交
4959 4960
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
4961
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4962 4963 4964 4965 4966 4967
    :rtype: LayerOutput
    """

    assert isinstance(input, LayerOutput)
    assert label is None or isinstance(label, LayerOutput)

4968
    ipts = [Input(input.name, **param_attr.attr)]
Z
zhangjinchao01 已提交
4969 4970 4971 4972
    if label is not None:
        ipts.append(Input(label.name))

    Layer(
4973 4974 4975 4976
        name=name,
        type=LayerType.CRF_DECODING_LAYER,
        size=size,
        inputs=ipts,
Q
qijun 已提交
4977
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
4978 4979 4980
    parents = [input]
    if label is not None:
        parents.append(label)
X
xuwei06 已提交
4981 4982 4983 4984
    # The size for LayerOutput means the dimension of the output.
    # It's different from the meaning of crf layer, which is the number of
    # classes.
    return LayerOutput(name, LayerType.CRF_DECODING_LAYER, parents, size=1)
Z
zhangjinchao01 已提交
4985

Q
qijun 已提交
4986

Y
Yu Yang 已提交
4987
@wrap_act_default(act=SigmoidActivation())
4988
@wrap_bias_attr_default(has_bias=True)
4989
@wrap_param_attr_default()
4990 4991
@wrap_name_default()
@layer_support()
Q
qijun 已提交
4992 4993
def nce_layer(input,
              label,
C
caoying03 已提交
4994
              num_classes=None,
Y
Yu Yang 已提交
4995
              act=None,
4996
              param_attr=None,
Q
qijun 已提交
4997 4998 4999 5000 5001 5002
              weight=None,
              num_neg_samples=10,
              neg_distribution=None,
              name=None,
              bias_attr=None,
              layer_attr=None):
5003 5004 5005 5006 5007 5008 5009 5010 5011
    """
    Noise-contrastive estimation.
    Implements the method in the following paper:
    A fast and simple algorithm for training neural probabilistic language models.

    The example usage is:

    .. code-block:: python

C
caoying03 已提交
5012 5013
       cost = nce_layer(input=[layer1, layer2], label=layer2,
                        param_attr=[attr1, attr2], weight=layer3,
5014 5015 5016 5017 5018 5019 5020 5021 5022 5023 5024
                        num_classes=3, neg_distribution=[0.1,0.3,0.6])

    :param name: layer name
    :type name: basestring
    :param input: input layers. It could be a LayerOutput of list/tuple of LayerOutput.
    :type input: LayerOutput|list|tuple|collections.Sequence
    :param label: label layer
    :type label: LayerOutput
    :param weight: weight layer, can be None(default)
    :type weight: LayerOutput
    :param num_classes: number of classes.
5025
    :type num_classes: int
Y
Yu Yang 已提交
5026 5027
    :param act: Activation, default is Sigmoid.
    :type act: BaseActivation
5028 5029
    :param param_attr: The Parameter Attribute|list.
    :type param_attr: ParameterAttribute
5030
    :param num_neg_samples: number of negative samples. Default is 10.
5031
    :type num_neg_samples: int
5032 5033 5034 5035 5036 5037 5038 5039 5040 5041 5042 5043 5044
    :param neg_distribution: The distribution for generating the random negative labels.
                             A uniform distribution will be used if not provided.
                             If not None, its length must be equal to num_classes.
    :type neg_distribution: list|tuple|collections.Sequence|None
    :param bias_attr: Bias parameter attribute. True if no bias.
    :type bias_attr: ParameterAttribute|None|False
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
    :return: layer name.
    :rtype: LayerOutput
    """
    if isinstance(input, LayerOutput):
        input = [input]
5045 5046 5047 5048 5049 5050 5051 5052
        assert not isinstance(param_attr, collections.Sequence)
        param_attr = [param_attr]
    else:
        if isinstance(param_attr, collections.Sequence):
            assert len(input) == len(param_attr)
        else:
            param_attr = [copy.deepcopy(param_attr) for _ in range(len(input))]

5053
    assert isinstance(input, collections.Sequence)
5054

5055 5056
    assert isinstance(label, LayerOutput)
    assert label.layer_type == LayerType.DATA
C
caoying03 已提交
5057 5058
    if num_classes is None:
        num_classes = label.size
5059 5060 5061
    if neg_distribution is not None:
        assert isinstance(neg_distribution, collections.Sequence)
        assert len(neg_distribution) == num_classes
5062
        assert abs(sum(neg_distribution) - 1.0) < 1e-5
Y
Yu Yang 已提交
5063 5064
    if not isinstance(act, BaseActivation):
        raise TypeError()
5065

5066 5067
    ipts_for_layer = []
    parents = []
5068
    for each_input, attr in zip(input, param_attr):
5069
        assert isinstance(each_input, LayerOutput)
5070
        ipts_for_layer.append(Input(each_input.name, **attr.attr))
5071 5072 5073 5074 5075 5076 5077 5078 5079 5080
        parents.append(each_input)
    ipts_for_layer.append(label.name)
    parents.append(label)

    if weight is not None:
        assert isinstance(weight, LayerOutput)
        assert weight.layer_type == LayerType.DATA
        ipts_for_layer.append(weight.name)
        parents.append(weight)

X
xuwei06 已提交
5081
    l = Layer(
5082 5083 5084 5085
        name=name,
        type=LayerType.NCE_LAYER,
        num_classes=num_classes,
        neg_sampling_dist=neg_distribution,
Y
Yu Yang 已提交
5086
        active_type=act.name,
5087 5088 5089
        num_neg_samples=num_neg_samples,
        inputs=ipts_for_layer,
        bias=ParamAttr.to_bias(bias_attr),
Q
qijun 已提交
5090 5091
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
Y
Yu Yang 已提交
5092 5093 5094 5095 5096
        name,
        LayerType.NCE_LAYER,
        parents=parents,
        size=l.config.size,
        activation=act)
Q
qijun 已提交
5097

5098

Z
zhangjinchao01 已提交
5099 5100 5101
"""
following are cost Layers.
"""
5102 5103


Z
zhangjinchao01 已提交
5104
@wrap_name_default()
L
luotao1 已提交
5105
@layer_support()
Q
qijun 已提交
5106 5107 5108 5109 5110 5111 5112
def rank_cost(left,
              right,
              label,
              weight=None,
              name=None,
              coeff=1.0,
              layer_attr=None):
Z
zhangjinchao01 已提交
5113
    """
5114
    A cost Layer for learning to rank using gradient descent. Details can refer
5115 5116
    to `papers <http://research.microsoft.com/en-us/um/people/cburges/papers/
    ICML_ranking.pdf>`_.
Z
zhangjinchao01 已提交
5117 5118 5119 5120 5121
    This layer contains at least three inputs. The weight is an optional
    argument, which affects the cost.

    .. math::

L
luotao02 已提交
5122
       C_{i,j} & = -\\tilde{P_{ij}} * o_{i,j} + log(1 + e^{o_{i,j}})
Z
zhangjinchao01 已提交
5123

L
luotao02 已提交
5124
       o_{i,j} & =  o_i - o_j
Z
zhangjinchao01 已提交
5125

L
luotao02 已提交
5126
       \\tilde{P_{i,j}} & = \\{0, 0.5, 1\\} \ or \ \\{0, 1\\}
Z
zhangjinchao01 已提交
5127 5128 5129 5130 5131 5132 5133 5134 5135 5136 5137 5138 5139 5140 5141 5142 5143 5144 5145 5146 5147 5148 5149 5150 5151 5152 5153 5154 5155

    In this formula:
      - :math:`C_{i,j}` is the cross entropy cost.
      - :math:`\\tilde{P_{i,j}}` is the label. 1 means positive order
        and 0 means reverse order.
      - :math:`o_i` and :math:`o_j`: the left output and right output.
        Their dimension is one.

    The simple usage:

    .. code-block:: python

      cost = rank_cost(left=out_left,
                       right=out_right,
                       label=label)

    :param left: The first input, the size of this layer is 1.
    :type left: LayerOutput
    :param right: The right input, the size of this layer is 1.
    :type right: LayerOutput
    :param label: Label is 1 or 0, means positive order and reverse order.
    :type label: LayerOutput
    :param weight: The weight affects the cost, namely the scale of cost.
                   It is an optional argument.
    :type weight: LayerOutput
    :param name: The name of this layers. It is not necessary.
    :type name: None|basestring
    :param coeff: The coefficient affects the gradient in the backward.
    :type coeff: float
L
luotao1 已提交
5156 5157
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
5158
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
5159 5160 5161 5162 5163 5164 5165 5166 5167 5168 5169 5170
    :rtype: LayerOutput
    """
    assert left.size == 1
    assert right.size == 1
    assert label.size == 1

    ipts = [left.name, right.name, label.name]
    parents = [left, right, label]
    if weight is not None:
        ipts.append(weight.name)
        parents.append(weight)

Q
qijun 已提交
5171 5172 5173 5174 5175 5176
    Layer(
        name=name,
        type=LayerType.RANK_COST,
        inputs=ipts,
        coeff=coeff,
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
5177

X
xuwei06 已提交
5178
    return LayerOutput(name, LayerType.RANK_COST, parents=parents, size=1)
Z
zhangjinchao01 已提交
5179

5180

Z
zhangjinchao01 已提交
5181
@wrap_name_default()
L
luotao1 已提交
5182
@layer_support()
Q
qijun 已提交
5183 5184 5185 5186 5187 5188
def lambda_cost(input,
                score,
                name,
                NDCG_num=5,
                max_sort_size=-1,
                layer_attr=None):
Z
zhangjinchao01 已提交
5189 5190 5191 5192 5193 5194 5195 5196 5197 5198 5199 5200
    """
    lambdaCost for lambdaRank LTR approach.

    The simple usage:

    .. code-block:: python

      cost = lambda_cost(input=input,
                         score=score,
                         NDCG_num=8,
                         max_sort_size=-1)

5201
    :param input: Samples of the same query should be loaded as sequence.
Z
zhangjinchao01 已提交
5202 5203 5204 5205 5206 5207 5208 5209 5210 5211 5212
    :type input: LayerOutput
    :param score: The 2nd input. Score of each sample.
    :type input: LayerOutput
    :param NDCG_num: The size of NDCG (Normalized Discounted Cumulative Gain),
                     e.g., 5 for NDCG@5. It must be less than for equal to the
                     minimum size of lists.
    :type NDCG_num: int
    :param max_sort_size: The size of partial sorting in calculating gradient.
                          If max_sort_size = -1, then for each list, the
                          algorithm will sort the entire list to get gradient.
                          In other cases, max_sort_size must be greater than or
C
caoying03 已提交
5213 5214 5215
                          equal to NDCG_num. And if max_sort_size is greater
                          than the size of a list, the algorithm will sort the
                          entire list of get gradient.
Z
zhangjinchao01 已提交
5216 5217 5218
    :type max_sort_size: int
    :param name: The name of this layers. It is not necessary.
    :type name: None|basestring
L
luotao1 已提交
5219 5220
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
5221
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
5222 5223
    :rtype: LayerOutput
    """
5224 5225 5226
    assert isinstance(input, LayerOutput) and isinstance(score, LayerOutput)
    if score.size is not None:
        assert score.size == 1
Q
qijun 已提交
5227 5228 5229 5230 5231 5232 5233
    Layer(
        name=name,
        type=LayerType.LAMBDA_COST,
        inputs=[input.name, score.name],
        NDCG_num=NDCG_num,
        max_sort_size=max_sort_size,
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
5234

Q
qijun 已提交
5235 5236
    return LayerOutput(
        name, LayerType.LAMBDA_COST, parents=[input, score], size=1)
Z
zhangjinchao01 已提交
5237

5238

Z
zhangjinchao01 已提交
5239
@wrap_name_default()
L
luotao1 已提交
5240
@layer_support()
5241 5242 5243 5244 5245 5246
def cross_entropy(input,
                  label,
                  name=None,
                  coeff=1.0,
                  weight=None,
                  layer_attr=None):
Z
zhangjinchao01 已提交
5247 5248 5249 5250 5251
    """
    A loss layer for multi class entropy.

    .. code-block:: python

X
xuwei06 已提交
5252
       cost = cross_entropy(input=input_layer,
L
Luo Tao 已提交
5253
                            label=label_layer)
Z
zhangjinchao01 已提交
5254 5255 5256 5257 5258 5259 5260

    :param input: The first input layer.
    :type input: LayerOutput.
    :param label: The input label.
    :type input: LayerOutput.
    :param name: The name of this layers. It is not necessary.
    :type name: None|basestring.
5261 5262
    :param coeff: The cost is multiplied with coeff.
                  The coefficient affects the gradient in the backward.
Z
zhangjinchao01 已提交
5263
    :type coeff: float.
5264 5265 5266 5267
    :param weight: The cost of each sample is multiplied with each weight.
                   The weight should be a layer with size=1. Note that gradient
                   will not be calculated for weight.
    :type weight: LayerOutout
L
luotao1 已提交
5268 5269
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
5270
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
5271 5272 5273
    :rtype: LayerOutput.
    """

5274
    ipts, parents = __cost_input__(input, label, weight)
Q
qijun 已提交
5275 5276 5277
    Layer(
        name=name,
        type=LayerType.CROSS_ENTROPY,
5278
        inputs=ipts,
Q
qijun 已提交
5279 5280
        coeff=coeff,
        **ExtraLayerAttribute.to_kwargs(layer_attr))
5281
    return LayerOutput(name, LayerType.CROSS_ENTROPY, parents=parents, size=1)
Z
zhangjinchao01 已提交
5282

5283

Z
zhangjinchao01 已提交
5284
@wrap_name_default()
L
luotao1 已提交
5285
@layer_support()
Q
qijun 已提交
5286 5287 5288 5289
def cross_entropy_with_selfnorm(input,
                                label,
                                name=None,
                                coeff=1.0,
L
luotao1 已提交
5290 5291
                                softmax_selfnorm_alpha=0.1,
                                layer_attr=None):
Z
zhangjinchao01 已提交
5292 5293
    """
    A loss layer for multi class entropy with selfnorm.
5294
    Input should be a vector of positive numbers, without normalization.
Z
zhangjinchao01 已提交
5295 5296 5297

    .. code-block:: python

X
xuwei06 已提交
5298
       cost = cross_entropy_with_selfnorm(input=input_layer,
L
Luo Tao 已提交
5299
                                          label=label_layer)
Z
zhangjinchao01 已提交
5300 5301 5302 5303 5304 5305 5306 5307 5308 5309 5310

    :param input: The first input layer.
    :type input: LayerOutput.
    :param label: The input label.
    :type input: LayerOutput.
    :param name: The name of this layers. It is not necessary.
    :type name: None|basestring.
    :param coeff: The coefficient affects the gradient in the backward.
    :type coeff: float.
    :param softmax_selfnorm_alpha: The scale factor affects the cost.
    :type softmax_selfnorm_alpha: float.
L
luotao1 已提交
5311 5312
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
5313
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
5314 5315
    :rtype: LayerOutput.
    """
Q
qijun 已提交
5316 5317 5318 5319 5320 5321 5322
    Layer(
        name=name,
        type=LayerType.CROSS_ENTROPY_WITH_SELFNORM,
        inputs=[input.name, label.name],
        coeff=coeff,
        softmax_selfnorm_alpha=softmax_selfnorm_alpha,
        **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
5323

Q
qijun 已提交
5324 5325 5326 5327 5328
    return LayerOutput(
        name,
        LayerType.CROSS_ENTROPY_WITH_SELFNORM,
        parents=[input, label],
        size=1)
Z
zhangjinchao01 已提交
5329

5330

X
xuwei06 已提交
5331 5332 5333 5334 5335 5336 5337 5338
@wrap_name_default()
@layer_support()
def sum_cost(input, name=None, layer_attr=None):
    """
    A loss layer which calculate the sum of the input as loss

    .. code-block:: python

L
Luo Tao 已提交
5339
       cost = sum_cost(input=input_layer)
X
xuwei06 已提交
5340 5341 5342 5343 5344 5345 5346 5347 5348 5349

    :param input: The first input layer.
    :type input: LayerOutput.
    :param name: The name of this layers. It is not necessary.
    :type name: None|basestring.
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
    :return: LayerOutput object.
    :rtype: LayerOutput.
    """
L
Luo Tao 已提交
5350
    assert isinstance(input, LayerOutput)
Q
qijun 已提交
5351 5352 5353 5354 5355
    Layer(
        name=name,
        type=LayerType.SUM_COST,
        inputs=[input.name],
        **ExtraLayerAttribute.to_kwargs(layer_attr))
X
xuwei06 已提交
5356

Q
qijun 已提交
5357
    return LayerOutput(name, LayerType.SUM_COST, parents=[input], size=1)
X
xuwei06 已提交
5358 5359


Z
zhangjinchao01 已提交
5360
@wrap_name_default()
L
luotao1 已提交
5361 5362
@layer_support()
def huber_cost(input, label, name=None, coeff=1.0, layer_attr=None):
Z
zhangjinchao01 已提交
5363 5364 5365 5366 5367
    """
    A loss layer for huber loss.

    .. code-block:: python

X
xuwei06 已提交
5368
       cost = huber_cost(input=input_layer,
L
Luo Tao 已提交
5369
                         label=label_layer)
Z
zhangjinchao01 已提交
5370 5371 5372 5373 5374 5375 5376 5377 5378

    :param input: The first input layer.
    :type input: LayerOutput.
    :param label: The input label.
    :type input: LayerOutput.
    :param name: The name of this layers. It is not necessary.
    :type name: None|basestring.
    :param coeff: The coefficient affects the gradient in the backward.
    :type coeff: float.
L
luotao1 已提交
5379 5380
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
5381
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
5382 5383
    :rtype: LayerOutput.
    """
5384 5385 5386
    assert isinstance(input, LayerOutput)
    if input.size is not None:
        assert input.size == 1
Q
qijun 已提交
5387 5388 5389 5390 5391 5392
    Layer(
        name=name,
        type=LayerType.HUBER,
        inputs=[input.name, label.name],
        coeff=coeff,
        **ExtraLayerAttribute.to_kwargs(layer_attr))
X
xuwei06 已提交
5393
    return LayerOutput(name, LayerType.HUBER, parents=[input, label], size=1)
Z
zhangjinchao01 已提交
5394

5395

Z
zhangjinchao01 已提交
5396
@wrap_name_default()
L
luotao1 已提交
5397
@layer_support()
Q
qijun 已提交
5398 5399 5400 5401
def multi_binary_label_cross_entropy(input,
                                     label,
                                     name=None,
                                     coeff=1.0,
L
luotao1 已提交
5402
                                     layer_attr=None):
Z
zhangjinchao01 已提交
5403 5404 5405 5406 5407
    """
    A loss layer for multi binary label cross entropy.

    .. code-block:: python

X
xuwei06 已提交
5408
       cost = multi_binary_label_cross_entropy(input=input_layer,
L
Luo Tao 已提交
5409
                                               label=label_layer)
Z
zhangjinchao01 已提交
5410 5411 5412 5413 5414 5415 5416 5417 5418

    :param input: The first input layer.
    :type input: LayerOutput
    :param label: The input label.
    :type input: LayerOutput
    :param name: The name of this layers. It is not necessary.
    :type name: None|basestring
    :param coeff: The coefficient affects the gradient in the backward.
    :type coeff: float
L
luotao1 已提交
5419 5420
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
5421
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
5422 5423 5424
    :rtype: LayerOutput
    """

5425 5426
    if input.activation is None or \
            not isinstance(input.activation, SigmoidActivation):
Q
qijun 已提交
5427 5428 5429 5430 5431 5432 5433 5434 5435 5436 5437 5438 5439 5440 5441 5442
        logger.log(
            logging.WARN,
            "%s is not recommend for multi_binary_label_cross_entropy's activation, "
            "maybe the sigmoid is better" % repr(input.activation))

    Layer(
        name=name,
        type=LayerType.MULTI_BIN_LABEL_CROSS_ENTROPY,
        inputs=[input.name, label.name],
        coeff=coeff,
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name,
        LayerType.MULTI_BIN_LABEL_CROSS_ENTROPY,
        parents=[input, label],
        size=1)
D
dangqingqing 已提交
5443 5444 5445 5446


@wrap_name_default()
@layer_support()
5447
def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None):
D
dangqingqing 已提交
5448 5449
    """
    This is a L1 loss but more smooth. It requires that the
D
dangqingqing 已提交
5450
    size of input and label are equal. The formula is as follows,
D
dangqingqing 已提交
5451 5452 5453 5454 5455 5456 5457 5458 5459

    .. math::

        L = \sum_{i} smooth_{L1}(input_i - label_i)

    in which

    .. math::

5460
        smooth_{L1}(x) = \\begin{cases} 0.5x^2& \\text{if}  \\ |x| < 1 \\\\ |x|-0.5& \\text{otherwise} \end{cases}
D
dangqingqing 已提交
5461

D
dangqingqing 已提交
5462 5463 5464
    More details can be found by referring to `Fast R-CNN
    <https://arxiv.org/pdf/1504.08083v2.pdf>`_

D
dangqingqing 已提交
5465 5466
    .. code-block:: python

5467 5468
       cost = smooth_l1_cost(input=input_layer,
                             label=label_layer)
D
dangqingqing 已提交
5469 5470 5471 5472 5473 5474 5475

    :param input: The input layer.
    :type input: LayerOutput
    :param label: The input label.
    :type input: LayerOutput
    :param name: The name of this layers. It is not necessary.
    :type name: None|basestring
5476 5477
    :param coeff: The coefficient affects the gradient in the backward.
    :type coeff: float
D
dangqingqing 已提交
5478 5479 5480 5481 5482 5483 5484 5485 5486 5487 5488 5489 5490
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
    :return: LayerOutput object.
    :rtype: LayerOutput
    """
    assert isinstance(input, LayerOutput)
    assert isinstance(label, LayerOutput)
    assert input.size == label.size

    Layer(
        name=name,
        type=LayerType.SMOOTH_L1,
        inputs=[input.name, label.name],
5491
        coeff=coeff,
D
dangqingqing 已提交
5492 5493 5494
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name, LayerType.SMOOTH_L1, parents=[input, label], size=1)
W
wwhu 已提交
5495 5496 5497 5498 5499 5500 5501 5502 5503 5504 5505 5506 5507 5508 5509 5510 5511 5512 5513 5514 5515 5516 5517 5518 5519 5520 5521 5522 5523 5524 5525 5526 5527 5528 5529 5530 5531 5532 5533 5534 5535 5536 5537 5538 5539 5540 5541 5542 5543 5544 5545


@wrap_name_default()
def multiplex_layer(input, name=None, layer_attr=None):
    """
    This layer multiplex multiple layers according to the index,
    which is provided by the first input layer.
    inputs[0]: the index of the layer to output of size batchSize.
    inputs[1:N]; the candidate output data.
    For each index i from 0 to batchSize -1, the output is the i-th row of the
    (index[i] + 1)-th layer.

    For each i-th row of output:
    .. math::
        y[i][j] = x_{x_{0}[i] + 1}[i][j], j = 0,1, ... , (x_{1}.width - 1)

    where, y is output. :math:`x_{k}` is the k-th input layer and
    :math:`k = x_{0}[i] + 1`.

    .. code-block:: python

       maxid = multiplex_layer(input=layers)

    :param input: Input layers.
    :type input: list of LayerOutput
    :param name: Layer name.
    :type name: basestring
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
    :return: LayerOutput object.
    :rtype: LayerOutput
    """

    assert isinstance(input, collections.Sequence)
    assert len(input) > 2, 'multiplex_layer should have more than 2 inputs'
    for i in range(1, len(input)):
        assert isinstance(input[i], LayerOutput)
        assert input[i].size == input[1].size, \
            "All the input layers except the first one should have the same size"

    l = Layer(
        name=name,
        type='multiplex',
        inputs=[x.name for x in input],
        size=input[1].size,
        **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(
        name=name,
        layer_type=LayerType.MULTIPLEX_LAYER,
        parents=input,
        size=l.config.size)