layers.py 145.0 KB
Newer Older
Z
zhangjinchao01 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import functools
16
import collections
Z
zhangjinchao01 已提交
17 18 19 20 21 22 23 24

from paddle.trainer.config_parser import *
from .activations import LinearActivation, SigmoidActivation, TanhActivation, \
    ReluActivation, IdentityActivation, SoftmaxActivation
from .evaluators import *
from .poolings import MaxPooling, AvgPooling, BasePoolingType
from .attrs import *
from .default_decorators import *
25

Z
zhangjinchao01 已提交
26 27 28 29 30 31 32
try:
    import cPickle as pickle
except ImportError:
    import pickle
import copy

__all__ = ["full_matrix_projection", "AggregateLevel", "ExpandLevel",
33
           "identity_projection", "dotmul_projection", "dotmul_operator",
X
xuwei06 已提交
34
           "repeat_layer",
Z
zhangjinchao01 已提交
35 36 37
           "table_projection", "mixed_layer", "data_layer",
           "embedding_layer", "fc_layer", "grumemory",
           "pooling_layer", "lstmemory", "last_seq", "first_seq",
38
           "cos_sim", "hsigmoid", "conv_projection",
Z
zhangjinchao01 已提交
39 40
           "regression_cost", 'classification_cost', "LayerOutput",
           'img_conv_layer', 'img_pool_layer', 'batch_norm_layer',
41
           'img_cmrnorm_layer', 'addto_layer',
Z
zhangjinchao01 已提交
42 43
           'concat_layer', 'lstm_step_layer', 'recurrent_group',
           'memory', 'StaticInput', 'expand_layer', 'scaling_layer',
L
liaogang 已提交
44 45
           'power_layer', 'interpolation_layer', 'bilinear_interp_layer',
           'trans_layer', 'sum_to_one_norm_layer',
Z
zhangjinchao01 已提交
46 47 48 49 50 51
           'get_output_layer', 'LayerType', 'context_projection',
           'beam_search', 'maxid_layer', 'GeneratedInput', 'SubsequenceInput',
           'gru_step_layer', 'recurrent_layer',
           'BaseGeneratedInput', 'conv_operator', 'conv_shift_layer',
           'tensor_layer', 'selective_fc_layer', 'sampling_id_layer',
           'slope_intercept_layer', 'trans_full_matrix_projection',
52
           'linear_comb_layer',
Z
zhangjinchao01 已提交
53
           'convex_comb_layer', 'ctc_layer', 'crf_layer', 'crf_decoding_layer',
54
           'nce_layer',
Z
zhangjinchao01 已提交
55
           'cross_entropy_with_selfnorm', 'cross_entropy',
X
xuwei06 已提交
56
           'multi_binary_label_cross_entropy', 'sum_cost',
Z
zhangjinchao01 已提交
57
           'rank_cost', 'lambda_cost', 'huber_cost',
58
           'block_expand_layer',
Q
qijun 已提交
59
           'maxout_layer', 'out_prod_layer', 'print_layer', 
Q
qijun 已提交
60
           'spp_layer', 
Z
zhangjinchao01 已提交
61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78
           ]


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

    DATA = "data"
    MIXED_LAYER = "mixed"
    LSTMEMORY = "lstmemory"
    GRUMEMORY = "gated_recurrent"
    SEQUENCE_LAST_INSTANCE = "seqlastins"
    SEQUENCE_FIRST_INSTANCE = "seqfirstins"
    POOLING_MAX = "max"
    POOLING_AVG = 'average'
    FC_LAYER = "fc"
    COST = 'cost'
79 80
    COSINE_SIM_VEC = 'cos_vm'
    COSINE_SIM = 'cos'
Z
zhangjinchao01 已提交
81 82
    HSIGMOID = 'hsigmoid'
    CONV_LAYER = "conv"
83
    CONVTRANS_LAYER = "convt"
Z
zhangjinchao01 已提交
84 85 86 87 88 89 90 91 92 93 94 95 96 97 98
    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'

    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
99
    BILINEAR_INTERP_LAYER = 'bilinear_interp'
Z
zhangjinchao01 已提交
100 101 102
    POWER_LAYER = 'power'
    SCALING_LAYER = 'scaling'
    TRANS_LAYER = 'trans'
H
Haonan 已提交
103
    OUT_PROD_LAYER = 'out_prod'
X
xuwei06 已提交
104
    FEATURE_MAP_EXPAND_LAYER = 'featmap_expand'
Z
zhangjinchao01 已提交
105 106 107 108 109 110 111 112 113 114 115

    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"
116
    LINEAR_COMBINATION_LAYER = "convex_comb"
Z
zhangjinchao01 已提交
117
    BLOCK_EXPAND = "blockexpand"
118
    MAXOUT = "maxout"
Q
qijun 已提交
119
    SPP_LAYER = "spp"
Z
zhangjinchao01 已提交
120

121 122
    PRINT_LAYER = "print"

Z
zhangjinchao01 已提交
123 124 125
    CTC_LAYER = "ctc"
    CRF_LAYER = "crf"
    CRF_DECODING_LAYER = "crf_decoding"
126
    NCE_LAYER = 'nce'
Z
zhangjinchao01 已提交
127 128 129 130 131 132 133 134

    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
135
    SUM_COST = "sum_cost"
Z
zhangjinchao01 已提交
136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180

    @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):
    EACH_TIMESTEP = 'non-seq'
    EACH_SEQUENCE = 'seq'


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.
181
    :type parents: list|tuple|collections.Sequence
Z
zhangjinchao01 已提交
182 183 184
    """

    def __init__(self, name, layer_type, parents=None, activation=None,
185 186
                 num_filters=None, img_norm_type=None, size=None, outputs=None,
                 reverse=None):
Z
zhangjinchao01 已提交
187 188
        assert isinstance(name, basestring)
        assert isinstance(layer_type, basestring)
X
xuwei06 已提交
189
        assert size is not None
Z
zhangjinchao01 已提交
190 191 192
        assert LayerType.is_layer_type(layer_type)
        self.name = name
        self.layer_type = layer_type
193 194
        if parents is not None and type(parents) != list:
            parents = [parents]
Z
zhangjinchao01 已提交
195 196 197 198 199 200 201 202
        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
203
        self.reverse = reverse
Z
zhangjinchao01 已提交
204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219

    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"


ERROR_CLIPPING = 'error_clipping_threshold'
DROPOUT = 'drop_rate'
220
DEVICE = 'device'
Z
zhangjinchao01 已提交
221 222 223


def layer_support(*attrs):
224
    attrs_list = list(attrs)
225
    attrs_list.append(DEVICE)
Z
zhangjinchao01 已提交
226 227 228
    def decorator(method):
        @functools.wraps(method)
        def wrapper(*args, **kwargs):
229
            for attr in attrs_list:
Z
zhangjinchao01 已提交
230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291
                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)

        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
    """
    proj = FullMatrixProjection(input_layer_name=input.name,
                                size=size,
                                **param_attr.attr)
    proj.origin = input
    return proj


292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328
@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
    """
    proj = TransposedFullMatrixProjection(input_layer_name=input.name,
                                          size=size,
                                          **param_attr.attr)
    proj.origin = input
    return proj


Z
zhangjinchao01 已提交
329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405
@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
    """
    proj = TableProjection(input_layer_name=input.name,
                           size=size,
                           **param_attr.attr)
    proj.origin = input
    return proj


def identity_projection(input, offset=None):
    """
    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.
406
    :type input: LayerOutput
Z
zhangjinchao01 已提交
407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422
    :param offset: Offset, None if use default.
    :type offset: int
    :return: A IdentityProjection or IdentityOffsetProjection Object
    :rtype: IdentityProjection or IdentityOffsetProjection
    """
    if offset is None:
        proj = IdentityProjection(input_layer_name=input.name)
        proj.origin = input
    else:
        proj = IdentityOffsetProjection(input_layer_name=input.name,
                                        offset=offset)
        proj.origin = input
    return proj


@wrap_param_attr_default()
423
def dotmul_projection(input, param_attr=None):
Z
zhangjinchao01 已提交
424
    """
425
    DotMulProjection with a layer as input.
Z
zhangjinchao01 已提交
426 427 428 429 430 431 432 433 434 435 436 437 438
    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)

439 440 441 442 443 444 445 446
    :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
    """
    proj = DotMulProjection(input_layer_name=input.name,
447 448
                            size=input.size,
                            **param_attr.attr)
449
    proj.origin = input
450
    return proj
Z
zhangjinchao01 已提交
451

452 453

def dotmul_operator(a=None, b=None, scale=1, **kwargs):
454 455
    """
    DotMulOperator takes two inputs and performs element-wise multiplication:
456

Z
zhangjinchao01 已提交
457
    .. math::
458 459
       out.row[i] += scale * (x.row[i] .* y.row[i])

Z
zhangjinchao01 已提交
460 461
    where :math:`.*` means element-wise multiplication, and
    scale is a config scalar, its default value is one.
462

Z
zhangjinchao01 已提交
463
    The example usage is:
464

Z
zhangjinchao01 已提交
465
    .. code-block:: python
466 467 468

       op = dotmul_operator(x=layer1, y=layer2, scale=0.5)

469 470 471 472
    :param a: Input layer1
    :type a: LayerOutput
    :param b: Input layer2
    :type b: LayerOutput
Z
zhangjinchao01 已提交
473 474
    :param scale: config scalar, default value is one.
    :type scale: float
475 476
    :return: A DotMulOperator Object.
    :rtype: DotMulOperator
Z
zhangjinchao01 已提交
477
    """
478 479 480 481 482 483 484 485 486 487 488
    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.')
    a = kwargs.get('x', a)    # For Backward capacity.
    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

    op = DotMulOperator(input_layer_names=[a.name, b.name],
489
                        scale=scale)
490
    op.origin = [a, b]
491
    return op
Z
zhangjinchao01 已提交
492

493

Z
zhangjinchao01 已提交
494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574
@wrap_bias_attr_default(['padding_attr'])
def context_projection(input, context_len, context_start=None,
                       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

    proj = ContextProjection(input_layer_name=input.name,
                             context_length=context_len,
                             context_start=context_start,
                             trainable_padding=trainable,
                             **extra_dict)
    proj.origin = input
    return proj


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

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

    def __init__(self, name, size, act, bias_attr, layer_attr,
                 parents=None):
        """
        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
        """
        LayerOutput.__init__(self, name, LayerType.MIXED_LAYER, parents,
                             size=size, activation=act)
        self.bias_attr = bias_attr
        self.layer_attr = layer_attr
        self.inputs = []
        self.finalized = False

575
    def __iadd__(self, other):
Z
zhangjinchao01 已提交
576 577 578 579 580 581 582 583
        """
        + += operator
        :param other: Other projection.
        :type other: Projection
        :return: self.
        :rtype: MixedLayerType
        """
        if not self.finalized:
584
            assert isinstance(other, Projection) or isinstance(other, Operator)
Z
zhangjinchao01 已提交
585
            self.inputs.append(other)
586 587 588 589
            if isinstance(other, Projection):
                self.parents.append(other.origin)
            else:
                self.parents.extend(other.origin)
Z
zhangjinchao01 已提交
590 591 592 593 594 595 596 597 598 599 600
            return self
        else:
            raise MixedLayerType.AddToSealedMixedLayerException()

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

    def __exit__(self, *args, **kwargs):
        del args, kwargs  # unused parameter to suppress warning
        assert len(self.inputs) != 0
601
        ml = MixedLayer(
Z
zhangjinchao01 已提交
602 603 604 605 606 607 608
            name=self.name,
            size=self.size,
            active_type=self.activation.name,
            bias=ParamAttr.to_bias(self.bias_attr),
            inputs=self.inputs,
            **ExtraLayerAttribute.to_kwargs(self.layer_attr)
        )
609 610 611
        # update the size which might be computed inside MixedLayer
        # according to the operator's output size
        self.size = ml.config.size
Z
zhangjinchao01 已提交
612 613 614 615 616 617


@wrap_name_default("mixed")
@wrap_act_default(act=LinearActivation())
@wrap_bias_attr_default(has_bias=False)
@layer_support(ERROR_CLIPPING, DROPOUT)
618
def mixed_layer(size=0, input=None, name=None, act=None, bias_attr=False,
Z
zhangjinchao01 已提交
619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664
                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:
        with mixed_layer(name=name, size=size, act=act, bias_attr=bias_attr,
                         layer_attr=layer_attr) as m:
665
            if isinstance(input, collections.Sequence):
Z
zhangjinchao01 已提交
666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690
                for each in input:
                    m += each
            else:
                m += input
        return m


@layer_support()
def data_layer(name, size, layer_attr=None):
    """
    Define DataLayer For NeuralNetwork.

    The example usage is:

    ..  code-block:: python

        data = data_layer(name="input",
                          size=1000)

    :param name: Name of this data layer.
    :type name: basestring
    :param size: Size of this data layer.
    :type size: int
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
691
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717
    :rtype: LayerOutput
    """
    Layer(type=LayerType.DATA, name=name, size=size,
          **ExtraLayerAttribute.to_kwargs(layer_attr))

    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
718
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746
    :rtype: LayerOutput
    """
    with mixed_layer(name=name, size=size, act=LinearActivation(),
                     bias_attr=False,
                     layer_attr=layer_attr) as mix:
        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)
def fc_layer(input, size, act=None, name=None,
             param_attr=None, bias_attr=None, layer_attr=None):
    """
    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
747
    which is equal to:
Z
zhangjinchao01 已提交
748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769

    .. 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
770
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
771 772 773 774
    :rtype: LayerOutput
    """
    if isinstance(input, LayerOutput):
        input = [input]
775
        assert not isinstance(param_attr, collections.Sequence)
Z
zhangjinchao01 已提交
776 777
        param_attr = [param_attr]
    else:
778
        if isinstance(param_attr, collections.Sequence):
Z
zhangjinchao01 已提交
779 780 781 782
            assert len(input) == len(param_attr)
        else:
            param_attr = [copy.deepcopy(param_attr) for _ in range(len(input))]

783
    assert isinstance(input, collections.Sequence)
Z
zhangjinchao01 已提交
784 785

    Layer(
786 787
        inputs=[Input(ipt.name, **attr.attr) for ipt, attr in zip(
            input, param_attr)],
Z
zhangjinchao01 已提交
788 789 790 791 792 793 794 795 796 797
        name=name,
        type=LayerType.FC_LAYER,
        size=size,
        bias=ParamAttr.to_bias(bias_attr),
        active_type=act.name,
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )
    return LayerOutput(name, LayerType.FC_LAYER, input, activation=act,
                       size=size)

798

799 800 801 802
@wrap_name_default("print")
def print_layer(input, name=None):
    """
    Print the output value of input layers. This layer is useful for debugging.
803 804 805 806 807

    :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
808
    :return: LayerOutput
809
    """
810 811 812 813 814
    if isinstance(input, LayerOutput):
        input = [input]
    assert isinstance(input, collections.Sequence)  # list or tuple
    for each in input:
        assert isinstance(each, LayerOutput)
815 816 817 818 819 820

    Layer(
        name=name,
        type=LayerType.PRINT_LAYER,
        inputs=[l.name for l in input],
    )
821
    # this layer don't return anything, can not be input of other layer.
822

Z
zhangjinchao01 已提交
823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841

@wrap_name_default("seq_pooling")
@wrap_bias_attr_default(has_bias=False)
@wrap_param_default(['pooling_type'], default_factory=lambda _: MaxPooling())
@layer_support()
def pooling_layer(input, pooling_type=None, name=None, bias_attr=None,
                  agg_level=AggregateLevel.EACH_TIMESTEP,
                  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(),
                                agg_level=AggregateLevel.EACH_SEQUENCE)

C
caoying03 已提交
842 843
    :param agg_level: AggregateLevel.EACH_TIMESTEP or
                      AggregateLevel.EACH_SEQUENCE
Z
zhangjinchao01 已提交
844 845 846 847 848 849 850 851 852 853 854 855
    :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
856
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
857 858 859
    :rtype: LayerType
    """
    extra_dict = dict()
860
    # noinspection PyUnresolvedReferences
Z
zhangjinchao01 已提交
861 862
    if isinstance(pooling_type, AvgPooling):
        extra_dict['average_strategy'] = pooling_type.strategy
863 864 865 866
    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
867 868 869 870 871 872 873 874 875 876 877 878 879 880 881
    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,
        **extra_dict
    )

    return LayerOutput(name, pooling_type.name, parents=[input],
                       size=input.size)


Q
qijun 已提交
882

Z
zhangjinchao01 已提交
883 884 885 886 887 888 889 890
@wrap_bias_attr_default()
@wrap_param_attr_default()
@wrap_act_default(param_names=['gate_act'],
                  act=SigmoidActivation())
@wrap_act_default(param_names=["act", 'state_act'], act=TanhActivation())
@wrap_name_default("lstmemory")
@layer_support(DROPOUT)
def lstmemory(input, name=None, reverse=False, act=None,
891
              gate_act=None, size=None,
Z
zhangjinchao01 已提交
892 893 894 895 896 897 898 899 900
              state_act=None, bias_attr=None, param_attr=None,
              layer_attr=None):
    """
    Long Short-term Memory Cell.

    The memory cell was implemented as follow equations.

    ..  math::

L
luotao02 已提交
901
        i_t & = \\sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)
Z
zhangjinchao01 已提交
902

L
luotao02 已提交
903
        f_t & = \\sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)
Z
zhangjinchao01 已提交
904

L
luotao02 已提交
905
        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
906

L
luotao02 已提交
907
        o_t & = \\sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)
Z
zhangjinchao01 已提交
908

L
luotao02 已提交
909
        h_t & = o_t tanh(c_t)
Z
zhangjinchao01 已提交
910 911


C
caoying03 已提交
912
    NOTE: In PaddlePaddle's implementation, the multiplications
Z
zhangjinchao01 已提交
913
    :math:`W_{xi}x_{t}` , :math:`W_{xf}x_{t}`,
C
caoying03 已提交
914 915 916 917
    :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
918

C
caoying03 已提交
919
    NOTE: This is a low level user interface. You can use network.simple_lstm
Z
zhangjinchao01 已提交
920 921
    to config a simple plain lstm layer.

C
caoying03 已提交
922 923 924 925
    Please refer to **Generating Sequences With Recurrent Neural Networks** for
    more details about LSTM.

    Link_ goes as below.
Z
zhangjinchao01 已提交
926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948

    .. _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
949
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
950 951 952 953 954 955
    :rtype: LayerOutput
    """

    assert gate_act.support_hppl
    assert state_act.support_hppl
    assert act.support_hppl
956 957 958 959 960 961 962 963 964 965
    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
966 967 968 969 970 971 972 973 974 975 976

    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))

977 978 979
    return LayerOutput(name, LayerType.LSTMEMORY, [input], size=input.size / 4,
                       reverse=reverse)

Z
zhangjinchao01 已提交
980 981 982 983 984 985 986 987 988

@wrap_bias_attr_default()
@wrap_param_attr_default()
@wrap_act_default(param_names=['gate_act'],
                  act=SigmoidActivation())
@wrap_act_default(param_names=["act"], act=TanhActivation())
@wrap_name_default("gru")
@layer_support(DROPOUT)
def grumemory(input, name=None, reverse=False, act=None,
989
              gate_act=None, size=None,
Z
zhangjinchao01 已提交
990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011
              bias_attr=None, param_attr=None,
              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
1012 1013
    3. The candidate activation :math:`\\tilde{h_t}` is computed similarly to
    that of the traditional recurrent unit:
Z
zhangjinchao01 已提交
1014 1015 1016 1017 1018

    ..  math::

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

C
caoying03 已提交
1019 1020 1021
    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
1022 1023 1024 1025 1026

    ..  math::

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

C
caoying03 已提交
1027
    NOTE: In PaddlePaddle's implementation, the multiplication operations
Z
zhangjinchao01 已提交
1028
    :math:`W_{r}x_{t}`, :math:`W_{z}x_{t}` and :math:`W x_t` are not computed in
C
caoying03 已提交
1029 1030 1031
    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
1032

C
caoying03 已提交
1033 1034 1035
    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
1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046

    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.
1047
    :param reverse: Whether sequence process is reversed or not.
Z
zhangjinchao01 已提交
1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062
    :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
1063 1064 1065
    :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
1066
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1067 1068 1069 1070
    :rtype: LayerOutput
    """
    assert act.support_hppl
    assert gate_act.support_hppl
1071 1072 1073 1074 1075 1076 1077 1078 1079
    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
1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090

    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)
          )

1091 1092 1093
    return LayerOutput(name, LayerType.GRUMEMORY, [input], size=input.size / 3,
                       reverse=reverse)

Z
zhangjinchao01 已提交
1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108

@wrap_name_default()
@layer_support()
def last_seq(input, name=None, agg_level=AggregateLevel.EACH_TIMESTEP,
             layer_attr=None):
    """
    Get Last Timestamp Activation of a sequence.

    :param agg_level: Aggregated level
    :param name: Layer name.
    :type name: basestring
    :param input: Input layer name.
    :type input: LayerOutput
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
1109
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1110 1111
    :rtype: LayerOutput
    """
1112 1113 1114 1115 1116 1117
    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.")

Z
zhangjinchao01 已提交
1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142
    Layer(
        name=name,
        type=LayerType.SEQUENCE_LAST_INSTANCE,
        inputs=[input.name],
        trans_type=agg_level,
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )
    return LayerOutput(name, LayerType.SEQUENCE_LAST_INSTANCE, parents=[input],
                       size=input.size)


@wrap_name_default()
@layer_support()
def first_seq(input, name=None, agg_level=AggregateLevel.EACH_TIMESTEP,
              layer_attr=None):
    """
    Get First Timestamp Activation of a sequence.

    :param agg_level: aggregation level
    :param name: Layer name.
    :type name: basestring
    :param input: Input layer name.
    :type input: LayerOutput
    :param layer_attr: extra layer attributes.
    :type layer_attr: ExtraLayerAttribute.
D
dangqingqing 已提交
1143
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1144 1145
    :rtype: LayerOutput
    """
1146 1147 1148 1149 1150 1151 1152

    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.')

Z
zhangjinchao01 已提交
1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167
    Layer(
        name=name,
        type=LayerType.SEQUENCE_FIRST_INSTANCE,
        inputs=[input.name],
        trans_type=agg_level,
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )
    return LayerOutput(name, LayerType.SEQUENCE_FIRST_INSTANCE,
                       parents=[input], size=input.size)


class ExpandLevel(object):
    FROM_TIMESTEP = AggregateLevel.EACH_TIMESTEP
    FROM_SEQUENCE = AggregateLevel.EACH_SEQUENCE

1168

Z
zhangjinchao01 已提交
1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
@wrap_name_default()
@layer_support()
def expand_layer(input, expand_as,
                 name=None,
                 bias_attr=False,
                 expand_level=ExpandLevel.FROM_TIMESTEP,
                 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,
                             expand_level=ExpandLevel.FROM_TIMESTEP)

    :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
1201
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218
    :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,
        **ExtraAttr.to_kwargs(layer_attr)
    )
    return LayerOutput(name=name,
                       size=input.size,
                       layer_type=LayerType.EXPAND_LAYER,
                       parents=[input, expand_as])


X
xuwei06 已提交
1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260
@wrap_name_default()
@layer_support()
def repeat_layer(input, num_repeats,
                 name=None,
                 layer_attr=None):
    """
    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

       expand = repeat_layer(layer, 4)

    :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,
        **ExtraAttr.to_kwargs(layer_attr)
    )
    return LayerOutput(name=name,
                       size=l.config.size,
                       layer_type=LayerType.FEATURE_MAP_EXPAND_LAYER,
                       parents=[input])

Z
zhangjinchao01 已提交
1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288
@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
1289
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1290 1291
    :rtype: LayerOutput
    """
1292
    assert isinstance(input, collections.Sequence)
Z
zhangjinchao01 已提交
1293
    assert len(input) == 2
1294 1295 1296 1297 1298 1299 1300
    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
1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311
    Layer(
        name=name,
        type=LayerType.INTERPOLATION_LAYER,
        inputs=[weight.name, input[0].name, input[1].name],
        **ExtraAttr.to_kwargs(layer_attr)
    )
    return LayerOutput(name, LayerType.INTERPOLATION_LAYER,
                       parents=[weight, input[0], input[1]],
                       size=input[0].size)


L
liaogang 已提交
1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327
@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
1328
       bilinear = bilinear_interp_layer(input=layer1, out_size_x=64, out_size_y=64)
L
liaogang 已提交
1329
    
L
liaogang 已提交
1330
    :param   input:        A input layer.
L
liaogang 已提交
1331
    :type    input:        LayerOutput.
L
liaogang 已提交
1332
    :param   out_size_x:   bilinear interpolation output width.
L
liaogang 已提交
1333
    :type    out_size_x:   int|None 
L
liaogang 已提交
1334
    :param   out_size_y:   bilinear interpolation output height.
L
liaogang 已提交
1335
    :type    out_size_y:   int|None
L
liaogang 已提交
1336
    :param   name:         The layer's name, which cna not be specified.
L
liaogang 已提交
1337
    :type    name:         None|basestring
L
liaogang 已提交
1338
    :param   layer_attr:   Extra Layer attribute.
L
liaogang 已提交
1339 1340 1341 1342 1343 1344 1345
    :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
1346
    assert input.num_filters is not None
L
liaogang 已提交
1347
    num_channels = input.num_filters
X
xuwei06 已提交
1348
    l = Layer(name=name,
L
liaogang 已提交
1349
          inputs=Input(input.name,
L
liaogang 已提交
1350
                       bilinear_interp=BilinearInterp(out_size_x=out_size_x,
L
liaogang 已提交
1351 1352 1353 1354
                                                      out_size_y=out_size_y,
                                                      num_channels=num_channels)),
          type=LayerType.BILINEAR_INTERP_LAYER,
          **ExtraLayerAttribute.to_kwargs(layer_attr))
L
liaogang 已提交
1355
    return LayerOutput(name, LayerType.BILINEAR_INTERP_LAYER, parents=[input],
X
xuwei06 已提交
1356
                       num_filters=num_channels, size=l.config.size)
L
liaogang 已提交
1357

Z
zhangjinchao01 已提交
1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384
@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
1385
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1386 1387
    :rtype: LayerOutput
    """
1388 1389 1390
    assert isinstance(input, LayerOutput) and isinstance(weight, LayerOutput)
    if weight.size is not None:
        assert weight.size == 1
Z
zhangjinchao01 已提交
1391 1392 1393
    Layer(
        name=name,
        type=LayerType.POWER_LAYER,
1394
        inputs=[weight.name, input.name],
Z
zhangjinchao01 已提交
1395 1396 1397 1398 1399 1400 1401 1402 1403 1404
        **ExtraAttr.to_kwargs(layer_attr)
    )
    return LayerOutput(name, LayerType.POWER_LAYER,
                       parents=[input, weight], size=input.size)


@wrap_name_default()
@layer_support()
def scaling_layer(input, weight, name=None, layer_attr=None):
    """
1405
    A layer for multiplying input vector by weight scalar.
Z
zhangjinchao01 已提交
1406 1407

    .. math::
1408
       y  = w x
Z
zhangjinchao01 已提交
1409

1410 1411 1412 1413 1414
    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
1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429

    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
1430
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1431 1432
    :rtype: LayerOutput
    """
1433 1434 1435
    assert isinstance(weight, LayerOutput) and isinstance(input, LayerOutput)
    if weight.size is not None:
        assert weight.size == 1
Z
zhangjinchao01 已提交
1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468
    Layer(
        name=name,
        type=LayerType.SCALING_LAYER,
        inputs=[weight.name, input.name],
        **ExtraAttr.to_kwargs(layer_attr)
    )
    return LayerOutput(name, LayerType.SCALING_LAYER, parents=[weight, input],
                       size=input.size)


@wrap_name_default()
@layer_support()
def trans_layer(input, name=None, layer_attr=None):
    """
    A layer for transposition.

    .. 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
1469
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
    :rtype: LayerOutput
    """
    Layer(
        name=name,
        type=LayerType.TRANS_LAYER,
        inputs=[input.name],
        **ExtraAttr.to_kwargs(layer_attr)
    )
    return LayerOutput(name, LayerType.TRANS_LAYER, parents=[input],
                       size=input.size)


@wrap_name_default()
@layer_support()
def cos_sim(a, b, scale=5, size=1, name=None, layer_attr=None):
    """
    Cosine Similarity Layer. The cosine similarity equation is here.

    ..  math::
D
dangqingqing 已提交
1489
        similarity = cos(\\theta) = {\\mathbf{a} \\cdot \\mathbf{b}
1490 1491 1492 1493 1494
        \\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
1495

1496 1497
    Note that the above computation is for one sample. Multiple samples are
    processed in one batch.
Z
zhangjinchao01 已提交
1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510

    :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
1511
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1512 1513
    :rtype: LayerOutput
    """
1514
    assert isinstance(a, LayerOutput) and isinstance(b, LayerOutput)
1515 1516 1517 1518 1519 1520 1521 1522 1523
    if size == 1:
        Layer(
            name=name,
            type=LayerType.COSINE_SIM,
            cos_scale=scale,
            inputs=[a.name, b.name],
            **ExtraLayerAttribute.to_kwargs(layer_attr)
        )
    else:
1524 1525
        if a.size is not None and b.size is not None:
            assert size == b.size / a.size
1526 1527 1528 1529 1530 1531 1532 1533
        Layer(
            name=name,
            type=LayerType.COSINE_SIM_VEC,
            size=size,
            cos_scale=scale,
            inputs=[a.name, b.name],
            **ExtraLayerAttribute.to_kwargs(layer_attr)
        )
X
xuwei06 已提交
1534
    return LayerOutput(name, LayerType.COSINE_SIM, parents=[a, b], size=size)
Z
zhangjinchao01 已提交
1535

1536

Z
zhangjinchao01 已提交
1537 1538
@wrap_name_default()
@wrap_bias_attr_default(has_bias=True)
1539
@wrap_param_attr_default()
Z
zhangjinchao01 已提交
1540
@layer_support()
C
caoying03 已提交
1541
def hsigmoid(input, label, num_classes, name=None, bias_attr=None,
1542
             param_attr=None, layer_attr=None):
Z
zhangjinchao01 已提交
1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563
    """
    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],
                        label=data_layer,
                        num_classes=3)

    :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.
    :type num_classes: int
L
luotao02 已提交
1564 1565
    :param name: layer name
    :type name: basestring
Z
zhangjinchao01 已提交
1566 1567 1568 1569 1570
    :param bias_attr: Bias attribute. None means default bias.
                      False means no bias.
    :type bias_attr: ParameterAttribute|False
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
1571
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1572 1573 1574 1575
    :rtype: LayerOutput
    """
    if isinstance(input, LayerOutput):
        input = [input]
1576 1577 1578 1579 1580 1581 1582 1583 1584
        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
1585 1586 1587 1588 1589
    assert isinstance(label, LayerOutput)
    assert label.layer_type == LayerType.DATA

    ipts_for_layer = []
    parents = []
1590
    for each_input, each_param_attr in zip(input, param_attr):
Z
zhangjinchao01 已提交
1591
        assert isinstance(each_input, LayerOutput)
1592
        ipts_for_layer.append(Input(each_input.name, **each_param_attr.attr))
Z
zhangjinchao01 已提交
1593 1594 1595 1596
        parents.append(each_input)
    ipts_for_layer.append(label.name)
    parents.append(label)

X
xuwei06 已提交
1597
    l = Layer(
Z
zhangjinchao01 已提交
1598 1599 1600 1601 1602 1603 1604
        name=name,
        type=LayerType.HSIGMOID,
        num_classes=num_classes,
        bias=ParamAttr.to_bias(bias_attr),
        inputs=ipts_for_layer,
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )
X
xuwei06 已提交
1605 1606
    return LayerOutput(name, LayerType.HSIGMOID, parents=parents,
                       size=l.config.size)
Z
zhangjinchao01 已提交
1607

1608

Z
zhangjinchao01 已提交
1609 1610 1611 1612 1613 1614 1615 1616 1617
@wrap_name_default("conv")
@wrap_param_attr_default()
@wrap_bias_attr_default()
@wrap_act_default(act=ReluActivation())
@layer_support(DROPOUT)
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,
1618 1619
                   filter_size_y=None, stride_y=None, padding_y=None,
                   trans=False):
Z
zhangjinchao01 已提交
1620 1621 1622 1623 1624 1625 1626
    """
    Convolution layer for image. Paddle only support square input currently and
    thus input image's width equals height.

    The details of convolution layer, please refer UFLDL's `convolution
    <http://ufldl.stanford.edu/tutorial/supervised/
    FeatureExtractionUsingConvolution/>`_ .
1627
    
1628 1629 1630 1631 1632 1633 1634
    Convolution Transpose (deconv) layer for image. Paddle only support square 
    input currently and thus input image's width equals height.

    The details of convolution transpose layer, 
    please refer to the following explanation and references therein
    <http://datascience.stackexchange.com/questions/6107/
    what-are-deconvolutional-layers/>`_ .
Z
zhangjinchao01 已提交
1635 1636 1637 1638
    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
1639 1640 1641
    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
1642
    32*4 = 128 filters to process inputs. The channels will be split into 4
C
caoying03 已提交
1643 1644
    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
1645 1646 1647 1648 1649

    :param name: Layer name.
    :type name: basestring
    :param input: Layer Input.
    :type input: LayerOutput
1650 1651 1652
    :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
1653 1654 1655
    :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).
1656
    :type filter_size_y: int|None
Z
zhangjinchao01 已提交
1657 1658 1659 1660 1661
    :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
1662 1663 1664
    :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
1665 1666
    :param stride_y: The y dimension of the stride.
    :type stride_y: int
1667 1668 1669
    :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
1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683
    :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
1684 1685
    :param trans: true if it is a convTransLayer, false if it is a convLayer
    :type trans: bool
D
dangqingqing 已提交
1686
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1687 1688 1689 1690 1691
    :rtype: LayerOutput
    """
    if num_channels is None:
        assert input.num_filters is not None
        num_channels = input.num_filters
1692

Z
zhangjinchao01 已提交
1693
    if filter_size_y is None:
1694 1695 1696 1697 1698 1699
        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
1700
    if stride_y is None:
1701 1702 1703 1704 1705 1706
        if isinstance(stride, collections.Sequence):
            assert len(stride) == 2
            stride, stride_y = stride
        else:
            stride_y = stride

Z
zhangjinchao01 已提交
1707
    if padding_y is None:
1708 1709 1710 1711 1712 1713 1714 1715
        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.
Z
zhangjinchao01 已提交
1716
        init_w = (2.0 / (filter_size ** 2 * num_channels)) ** 0.5
1717 1718 1719 1720
        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
1721
    
1722
    lt = LayerType.CONVTRANS_LAYER if trans else LayerType.CONV_LAYER
1723
    
X
xuwei06 已提交
1724
    l = Layer(
Z
zhangjinchao01 已提交
1725 1726 1727 1728
        name=name,
        inputs=Input(input.name, conv=Conv(
            filter_size=filter_size, padding=padding, stride=stride,
            channels=num_channels, groups=groups,
1729 1730 1731
            filter_size_y=filter_size_y, padding_y=padding_y,
            stride_y=stride_y),
                     **param_attr.attr),
Z
zhangjinchao01 已提交
1732 1733 1734 1735
        active_type=act.name,
        num_filters=num_filters,
        bias=ParamAttr.to_bias(bias_attr),
        shared_biases=shared_biases,
1736
        type=lt,
Z
zhangjinchao01 已提交
1737 1738
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )
1739
    return LayerOutput(name, lt, parents=[input],
X
xuwei06 已提交
1740 1741
                       activation=act, num_filters=num_filters,
                       size=l.config.size)
Z
zhangjinchao01 已提交
1742 1743 1744 1745 1746 1747


@wrap_name_default("pool")
@layer_support()
def img_pool_layer(input, pool_size, name=None,
                   num_channels=None, pool_type=None,
1748
                   stride=1, padding=0, layer_attr=None,
1749 1750
                   pool_size_y=None, stride_y=None, padding_y=None,
                   img_width=None):
Z
zhangjinchao01 已提交
1751 1752 1753 1754 1755 1756 1757
    """
    Image pooling Layer.

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

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

1758
    :param padding: pooling padding width.
Z
zhangjinchao01 已提交
1759
    :type padding: int
1760 1761
    :param padding_y: pooling padding height. It's equal to padding by default.
    :type padding_y: int|None
Z
zhangjinchao01 已提交
1762 1763 1764 1765
    :param name: name of pooling layer
    :type name: basestring.
    :param input: layer's input
    :type input: LayerOutput
1766
    :param pool_size: pooling window width
Z
zhangjinchao01 已提交
1767
    :type pool_size: int
1768 1769
    :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
1770 1771
    :param num_channels: number of input channel.
    :type num_channels: int
1772
    :param pool_type: pooling type. MaxPooling or AvgPooling. Default is
Z
zhangjinchao01 已提交
1773 1774
                      MaxPooling.
    :type pool_type: BasePoolingType
1775
    :param stride: stride width of pooling.
Z
zhangjinchao01 已提交
1776
    :type stride: int
1777 1778
    :param stride_y: stride height of pooling. It is equal to stride by default.
    :type stride_y: int|None
Z
zhangjinchao01 已提交
1779 1780
    :param layer_attr: Extra Layer attribute.
    :type layer_attr: ExtraLayerAttribute
1781 1782 1783
    :param img_width: the width of input feature map. If it is None, the input feature
                      map should be square.
    :type img_width: int|None
D
dangqingqing 已提交
1784 1785
    :return: LayerOutput object.
    :rtype: LayerOutput
Z
zhangjinchao01 已提交
1786 1787 1788 1789 1790 1791 1792 1793 1794 1795
    """
    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'

1796 1797 1798 1799 1800 1801 1802 1803
    type_name = pool_type.name + '-projection' \
      if (isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)) \
      else pool_type.name

    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
1804
    l = Layer(
Z
zhangjinchao01 已提交
1805 1806 1807 1808
        name=name,
        type=LayerType.POOL_LAYER,
        inputs=[Input(input.name,
                      pool=Pool(
1809
                          pool_type=type_name,
Z
zhangjinchao01 已提交
1810 1811
                          channels=num_channels,
                          size_x=pool_size,
1812
                          start=None,
Z
zhangjinchao01 已提交
1813
                          stride=stride,
1814 1815 1816 1817 1818
                          padding=padding,
                          size_y=pool_size_y,
                          stride_y=stride_y,
                          padding_y=padding_y,
                          img_width=img_width
Z
zhangjinchao01 已提交
1819 1820 1821 1822
                      ))],
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )
    return LayerOutput(name, LayerType.POOL_LAYER, parents=[input],
X
xuwei06 已提交
1823
                       num_filters=num_channels, size=l.config.size)
Z
zhangjinchao01 已提交
1824 1825


Q
qijun 已提交
1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865
@wrap_name_default("spp")
@layer_support()
def spp_layer(input, name=None, num_channels=None, pool_type=None,
              pyramid_height=None, img_width=None, layer_attr=None):
    """
    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>`_.

    :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 img_width: the width of input feature map. If it is None, the input feature
                      map should be square.
    :type img_width: int|None
    :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
1866
    l = Layer(
Q
qijun 已提交
1867 1868 1869 1870 1871 1872 1873 1874 1875 1876
        name=name,
        type=LayerType.SPP_LAYER,
        inputs=Input(input.name,
                     spp=SpatialPyramidPool(pool_type=type_name,
                                            channels=num_channels,
                                            pyramid_height=pyramid_height,
                                            img_width=img_width)
        ),
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )
Q
qijun 已提交
1877 1878
    return LayerOutput(name, layer_type=LayerType.SPP_LAYER, parents=[input],
                       num_filters=num_channels, size=l.config.size)
Q
qijun 已提交
1879 1880


Z
zhangjinchao01 已提交
1881
def __img_norm_layer__(name, input, size, norm_type, scale, power,
1882
                       num_channels, blocked, layer_attr):
Z
zhangjinchao01 已提交
1883 1884 1885 1886
    if num_channels is None:
        assert input.num_filters is not None
        num_channels = input.num_filters

X
xuwei06 已提交
1887
    l = Layer(
Z
zhangjinchao01 已提交
1888 1889 1890 1891 1892 1893 1894 1895 1896
        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],
X
xuwei06 已提交
1897 1898
                       num_filters=num_channels, img_norm_type=norm_type,
                       size=l.config.size)
Z
zhangjinchao01 已提交
1899 1900 1901 1902


@wrap_name_default("crmnorm")
@layer_support()
D
dangqingqing 已提交
1903 1904
def img_cmrnorm_layer(input, size, scale=0.0128, power=0.75,
                      name=None, num_channels=None,
1905
                      layer_attr=None):
Z
zhangjinchao01 已提交
1906
    """
1907
    Response normalization across feature maps.
D
dangqingqing 已提交
1908 1909
    The details please refer to
    `Alex's paper <http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf>`_.
Z
zhangjinchao01 已提交
1910 1911

    :param name: layer name.
D
dangqingqing 已提交
1912
    :type name: None|basestring
Z
zhangjinchao01 已提交
1913 1914
    :param input: layer's input.
    :type input: LayerOutput
1915
    :param size: Normalize in number of :math:`size` feature maps.
Z
zhangjinchao01 已提交
1916
    :type size: int
D
dangqingqing 已提交
1917
    :param scale: The hyper-parameter.
Z
zhangjinchao01 已提交
1918
    :type scale: float
D
dangqingqing 已提交
1919
    :param power: The hyper-parameter.
Z
zhangjinchao01 已提交
1920 1921 1922 1923 1924
    :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
1925
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
1926 1927 1928
    :rtype: LayerOutput
    """
    return __img_norm_layer__(name, input, size, "cmrnorm-projection", scale,
1929
                              power, num_channels, 0, layer_attr)
Z
zhangjinchao01 已提交
1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974


@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)
def batch_norm_layer(input, act=None, name=None, num_channels=None,
                     bias_attr=None, param_attr=None, layer_attr=None,
                     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>`_.

    :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.
1975
    :type batch_norm_type: None|string, None or "batch_norm" or "cudnn_batch_norm"
Z
zhangjinchao01 已提交
1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
    :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
2003
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
    :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
2023
    l = Layer(
Z
zhangjinchao01 已提交
2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038
        name=name,
        inputs=Input(input.name,
                     image=Image(channels=num_channels),
                     **param_attr.attr),
        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,
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )

    return LayerOutput(name=name, layer_type=LayerType.BATCH_NORM_LAYER,
                       parents=[input], activation=act,
X
xuwei06 已提交
2039 2040
                       num_filters=num_channels,
                       size=l.config.size)
Z
zhangjinchao01 已提交
2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067


@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
2068
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108
    :rtype: LayerOutput
    """
    Layer(
        name=name,
        type=LayerType.SUM_TO_ONE_NORM_LAYER,
        inputs=[input.name],
        **ExtraAttr.to_kwargs(layer_attr)
    )
    return LayerOutput(name, LayerType.SUM_TO_ONE_NORM_LAYER, parents=[input],
                       size=input.size)


@wrap_name_default("addto")
@wrap_act_default(act=LinearActivation())
@wrap_bias_attr_default(has_bias=False)
@layer_support(DROPOUT)
def addto_layer(input, act=None, name=None, bias_attr=None,
                layer_attr=None):
    """
    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
2109 2110 2111
    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
2112 2113

    It is a very good way to set dropout outside the layers. Since not all
C
caoying03 已提交
2114 2115
    PaddlePaddle layer support dropout, you can add an add_to layer, set
    dropout here.
Z
zhangjinchao01 已提交
2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129
    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
2130
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2131 2132 2133 2134 2135 2136
    :rtype: LayerOutput
    """
    num_filters = None
    if isinstance(input, LayerOutput):
        input = [input]

2137
    assert isinstance(input, collections.Sequence)
Z
zhangjinchao01 已提交
2138 2139 2140 2141 2142 2143 2144
    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
2145
    l = Layer(
Z
zhangjinchao01 已提交
2146 2147 2148 2149 2150
        name=name, type=LayerType.ADDTO_LAYER, inputs=ipts_for_layer,
        bias=ParamAttr.to_bias(bias_attr),
        active_type=act.name,
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )
2151

Z
zhangjinchao01 已提交
2152
    return LayerOutput(name, LayerType.ADDTO_LAYER, parents=input,
X
xuwei06 已提交
2153 2154
                       activation=act, num_filters=num_filters,
                       size=l.config.size)
Z
zhangjinchao01 已提交
2155 2156 2157 2158 2159


@wrap_act_default(act=IdentityActivation())
@wrap_name_default("concat")
@layer_support()
2160
def concat_layer(input, act=None, name=None, layer_attr=None, bias_attr=None):
Z
zhangjinchao01 已提交
2161 2162 2163 2164
    """
    Concat all input vector into one huge vector.
    Inputs can be list of LayerOutput or list of projection.

2165 2166 2167 2168 2169 2170
    The example usage is:

    ..  code-block:: python

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

Z
zhangjinchao01 已提交
2171 2172 2173
    :param name: Layer name.
    :type name: basestring
    :param input: input layers or projections
2174
    :type input: list|tuple|collections.Sequence
Z
zhangjinchao01 已提交
2175 2176 2177 2178
    :param act: Activation type.
    :type act: BaseActivation
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
2179
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2180 2181 2182 2183 2184 2185 2186 2187
    :rtype: LayerOutput
    """

    if isinstance(input, LayerOutput):
        input = [input]
    elif isinstance(input, Projection):
        input = [input]
    else:
2188
        assert isinstance(input, collections.Sequence)
Z
zhangjinchao01 已提交
2189 2190

    def __is_type__(o, tp):
2191
        if not isinstance(o, collections.Sequence):
Z
zhangjinchao01 已提交
2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218
            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

    is_concat_layer = __is_type__(reduce(__reduce_concat_type__,
                                         map(type, input)), LayerOutput)

    layer_type = (LayerType.CONCAT_LAYER if is_concat_layer
                  else LayerType.CONCAT_PROJ_LAYER)

2219 2220
    if layer_type == LayerType.CONCAT_LAYER:
        assert not bias_attr
2221

Z
zhangjinchao01 已提交
2222 2223 2224 2225
    Layer(
        name=name, type=layer_type,
        inputs=[x.name for x in input] if is_concat_layer else input,
        active_type=act.name,
2226
        bias=ParamAttr.to_bias(bias_attr),
Z
zhangjinchao01 已提交
2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )

    sz = 0
    for each_input in input:
        if each_input.size is not None:
            sz += each_input.size
        else:
            sz = None
            break

    return LayerOutput(name, layer_type=layer_type,
                       parents=input if is_concat_layer else [
                           x.origin for x in input],
                       activation=act, size=sz)


def memory(name, size, is_seq=False, boot_layer=None,
           boot_bias=None, boot_bias_active_type=None,
           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.

    :param name: memory's name.
    :type name: basestring
    :param size: size of memory.
    :type size: int
    :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
2281
    :return: LayerOutput object which is a memory.
Z
zhangjinchao01 已提交
2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322
    :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)

    agent_name = Memory(name, size,
                        is_seq,
                        boot_layer.name if boot_layer is not None else None,
                        boot_bias,
                        boot_bias_active_type.name,
                        boot_with_const_id)

    lout = LayerOutput(name=agent_name, size=size,
                       layer_type=LayerType.MEMORY,
                       parents=[boot_layer] if boot_layer is not None
                       else None)
    return lout


@wrap_bias_attr_default()
@wrap_act_default(param_names=['gate_act',
                               'state_act'],
                  act=SigmoidActivation())
@wrap_act_default(act=TanhActivation())
@wrap_name_default('lstm_step')
@layer_support()
def lstm_step_layer(input, state, size, act=None,
                    name=None, gate_act=None, state_act=None,
                    bias_attr=None, layer_attr=None):
    """
    LSTM Step Layer. It used in recurrent_group. The lstm equations are shown
    as follow.

    ..  math::

L
luotao02 已提交
2323
        i_t & = \\sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)
Z
zhangjinchao01 已提交
2324

L
luotao02 已提交
2325
        f_t & = \\sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)
Z
zhangjinchao01 已提交
2326

L
luotao02 已提交
2327
        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
2328

L
luotao02 已提交
2329
        o_t & = \\sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)
Z
zhangjinchao01 已提交
2330

L
luotao02 已提交
2331
        h_t & = o_t tanh(c_t)
Z
zhangjinchao01 已提交
2332 2333


L
luotao02 已提交
2334
    The input of lstm step is :math:`Wx_t + Wh_{t-1}`, and user should use
Z
zhangjinchao01 已提交
2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372
    :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
2373
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 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
    :rtype: LayerOutput
    """
    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),
        size=size, inputs=[input.name, state.name],
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )

    return LayerOutput(name=name, layer_type=LayerType.LSTM_STEP_LAYER,
                       parents=[input, state], activation=act,
                       size=size, outputs=['default', 'state'])


@wrap_bias_attr_default()
@wrap_act_default(param_names=['gate_act'],
                  act=SigmoidActivation())
@wrap_act_default(act=TanhActivation())
@wrap_name_default('gru_step')
@layer_support()
def gru_step_layer(input, output_mem, size=None, act=None,
                   name=None, gate_act=None,
                   bias_attr=None, layer_attr=None):
    """

    :param input:
    :type input: LayerOutput
    :param output_mem:
    :param size:
    :param act:
    :param name:
    :param gate_act:
    :param bias_attr:
    :param layer_attr:
D
dangqingqing 已提交
2412
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440
    :rtype: LayerOutput
    """
    assert input.size % 3 == 0
    if size is None:
        size = input.size / 3
    Layer(
        name=name,
        type=LayerType.GRU_STEP_LAYER,
        inputs=[
            input.name,
            output_mem.name
        ],
        bias=ParamAttr.to_bias(bias_attr),
        size=size,
        active_type=act.name,
        active_gate_type=gate_act.name,
        **ExtraAttr.to_kwargs(layer_attr)
    )
    return LayerOutput(
        name=name, layer_type=LayerType.GRU_STEP_LAYER,
        parents=[input, output_mem],
        size=size, activation=act)


@wrap_name_default()
@layer_support()
def get_output_layer(input, arg_name, name=None, layer_attr=None):
    """
C
caoying03 已提交
2441 2442 2443 2444
    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
2445 2446 2447 2448 2449 2450 2451 2452 2453

    :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
2454
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476
    :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))
    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))

    return LayerOutput(name=name, layer_type=LayerType.GET_OUTPUT_LAYER,
                       parents=[input], size=input.size)


@wrap_name_default()
@wrap_act_default()
@wrap_bias_attr_default()
@wrap_param_attr_default()
@layer_support()
def recurrent_layer(input, act=None, bias_attr=None,
2477
                    param_attr=None, name=None, reverse=False, layer_attr=None):
Z
zhangjinchao01 已提交
2478
    """
2479 2480
    Simple recurrent unit layer. It is just a fully connect layer through both
    time and neural network.
Z
zhangjinchao01 已提交
2481

2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508
    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
2509
    :return: LayerOutput object.
2510
    :rtype: LayerOutput
Z
zhangjinchao01 已提交
2511 2512 2513 2514 2515 2516
    """
    Layer(name=name,
          type=LayerType.RECURRENT_LAYER,
          inputs=Input(input.name, **param_attr.attr),
          active_type=act.name,
          bias=ParamAttr.to_bias(bias_attr),
2517
          reversed=reverse,
Z
zhangjinchao01 已提交
2518 2519
          **ExtraAttr.to_kwargs(layer_attr))
    return LayerOutput(name=name, layer_type=LayerType.RECURRENT_LAYER,
2520 2521
                       parents=[input], size=input.size, activation=act,
                       reverse=reverse)
Z
zhangjinchao01 已提交
2522 2523 2524 2525 2526 2527 2528


class StaticInput(object):
    """
    StaticInput is only used in recurrent_group which defines a read-only memory
    that can be a sequence or non-sequence.
    """
2529

Z
zhangjinchao01 已提交
2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548
    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)
    """
2549

Z
zhangjinchao01 已提交
2550 2551 2552 2553 2554 2555 2556
    def __init__(self, input):
        assert isinstance(input, LayerOutput)
        assert input.size is not None
        self.input = input


@wrap_name_default("recurrent_group")
2557
def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
Z
zhangjinchao01 已提交
2558
    """
C
caoying03 已提交
2559 2560 2561 2562 2563
    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
2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607

    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

2608 2609
    :param reverse: If reverse is set true, the recurrent unit will process the
                    input sequence in a reverse order.
Z
zhangjinchao01 已提交
2610
    :type reverse: bool
2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621

    :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

D
dangqingqing 已提交
2622
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2623 2624 2625 2626 2627 2628 2629 2630 2631 2632
    :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]
2633
    assert isinstance(input, collections.Sequence)
Z
zhangjinchao01 已提交
2634 2635 2636 2637 2638 2639

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

    in_links = filter(is_in_links, input)

2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653
    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

    assert(targetInlink == None or targetInlink_in_inlinks())
    targetInlinkName = None if targetInlink == None \
                            else targetInlink.name if isinstance(targetInlink, LayerOutput) \
                                                   else targetInlink.input.name

Z
zhangjinchao01 已提交
2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664
    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(
        name=name, in_links=map(map_in_links, in_links),
2665 2666
        seq_reversed=reverse,
        target_inlinkname=targetInlinkName)
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 2689 2690 2691
    in_args = []
    for each_input in input:
        assert is_single_input(each_input)
        if isinstance(each_input, LayerOutput):
            in_args.append(each_input)
        elif isinstance(each_input, SubsequenceInput):
            in_args.append(each_input.input)
        else:
            mem_name = "__%s_memory__" % each_input.input.name
            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:
                mix += identity_projection(mem)
            in_args.append(mem)

    layer_outs = step(*in_args)

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

    for ot in layer_outs:
        assert isinstance(ot, LayerOutput)
2692
        ot.reverse = reverse
Z
zhangjinchao01 已提交
2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704
        if contains_sub_seq[0]:
            RecurrentLayerGroupSetOutLink(Link(ot.name, has_subseq=True))
        else:
            RecurrentLayerGroupSetOutLink(ot.name)

    RecurrentLayerGroupEnd(name=name)

    if len(layer_outs) == 1:
        return layer_outs[0]
    else:
        return layer_outs

2705

Z
zhangjinchao01 已提交
2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733
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):
        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))
        return trg_emb

    def __init__(self, size, embedding_name, embedding_size):
2734
        super(GeneratedInput, self).__init__()
Z
zhangjinchao01 已提交
2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757
        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
2758
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2759 2760 2761 2762
    :rtype: LayerOutput
    """

    assert isinstance(input, LayerOutput)
X
xuwei06 已提交
2763
    l = Layer(name=name,
Z
zhangjinchao01 已提交
2764 2765 2766 2767 2768
          type='maxid',
          inputs=[input.name],
          **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(name=name,
                       layer_type=LayerType.MAXID_LAYER,
X
xuwei06 已提交
2769 2770
                       parents=[input],
                       size=l.config.size)
Z
zhangjinchao01 已提交
2771

2772

H
Haonan 已提交
2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798
@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)
X
xuwei06 已提交
2799
    l = Layer(name=name,
2800
          type=LayerType.OUT_PROD_LAYER,
H
Haonan 已提交
2801 2802 2803 2804
          inputs=[input1.name, input2.name],
          **ExtraLayerAttribute.to_kwargs(layer_attr))
    return LayerOutput(name=name,
                       layer_type=LayerType.OUT_PROD_LAYER,
X
xuwei06 已提交
2805 2806
                       parents=[input1, input2],
                       size=l.config.size)
2807

Z
zhangjinchao01 已提交
2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823

@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
2824 2825
    :param name: Layer name.
    :type name: basestring
Z
zhangjinchao01 已提交
2826 2827 2828 2829 2830 2831
    :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
2832
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
2833 2834
    :rtype: LayerOutput
    """
X
xuwei06 已提交
2835
    l = Layer(name=name,
Z
zhangjinchao01 已提交
2836 2837 2838 2839 2840
          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,
X
xuwei06 已提交
2841 2842
                       parents=[input],
                       size=l.config.size)
Z
zhangjinchao01 已提交
2843 2844 2845 2846 2847 2848


@wrap_name_default()
def beam_search(step, input, bos_id, eos_id, beam_size,
                max_length=500, name=None,
                num_results_per_sample=None):
2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859
    """
    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)
2860
            with mixed_layer(size=512, name='rnn') as simple_rnn:
2861 2862 2863 2864 2865 2866
                simple_rnn += full_matrix_projection(input)
                simple_rnn += last_time_step_output
            return simple_rnn

        beam_gen = beam_search(name="decoder",
                               step=rnn_step,
2867
                               input=[StaticInput(encoder_last)],
2868 2869
                               bos_id=0,
                               eos_id=1,
2870
                               beam_size=5)
2871 2872 2873 2874 2875 2876 2877 2878 2879

    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
2880
                 step, and it is applied to sequences with arbitrary length by
2881 2882 2883 2884 2885 2886
                 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
    :param input: Input data for the recurrent unit
2887
    :type input: list
2888 2889 2890
    :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
2891
                   symbol is essential, since it is used to initialize the RNN
2892 2893 2894 2895 2896 2897 2898 2899
                   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
2900 2901
    :param max_length: Max generated sequence length.
    :type max_length: int
2902 2903 2904 2905 2906 2907 2908 2909 2910 2911
    :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
2912 2913
    :return: The generated word index.
    :rtype: LayerOutput
2914 2915
    """

Z
zhangjinchao01 已提交
2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928
    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")

    if isinstance(input, StaticInput) or isinstance(input,
                                                    BaseGeneratedInput):
        input = [input]

    generated_input_index = -1

    real_input = []
    for i, each_input in enumerate(input):
2929 2930
        assert isinstance(each_input, StaticInput) or isinstance(
            each_input, BaseGeneratedInput)
Z
zhangjinchao01 已提交
2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963
        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
        RecurrentLayerGroupSetGenerator(Generator(
            eos_layer_name=eos_name,
            max_num_frames=max_length,
            beam_size=beam_size,
            num_results_per_sample=num_results_per_sample))

        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

    tmp = recurrent_group(step=__real_step__, input=real_input, reverse=False,
                          name=name)
2964

Z
zhangjinchao01 已提交
2965 2966
    return tmp

2967 2968
def __cost_input__(input, label, weight=None):
    """
2969
    inputs and parents for cost layers.
2970 2971 2972 2973 2974 2975 2976 2977
    """
    ipts = [Input(input.name), Input(label.name)]
    parents = [input, label]
    if weight is not None:
        assert weight.layer_type == LayerType.DATA
        ipts.append(Input(weight.name))
        parents.append(weight)
    return ipts, parents
2978

Z
zhangjinchao01 已提交
2979 2980

@wrap_name_default()
L
luotao1 已提交
2981 2982 2983
@layer_support()
def regression_cost(input, label, weight=None, name=None,
                    layer_attr=None):
Z
zhangjinchao01 已提交
2984 2985 2986 2987 2988 2989
    """
    Regression Layer.

    TODO(yuyang18): Complete this method.

    :param name: layer name.
2990
    :type name: basestring
Z
zhangjinchao01 已提交
2991
    :param input: Network prediction.
2992
    :type input: LayerOutput
Z
zhangjinchao01 已提交
2993
    :param label: Data label.
2994 2995 2996 2997
    :type label: LayerOutput
    :param weight: The weight affects the cost, namely the scale of cost.
                   It is an optional argument.
    :type weight: LayerOutput
L
luotao1 已提交
2998 2999
    :param layer_attr: layer's extra attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
3000
    :return: LayerOutput object.
3001
    :rtype: LayerOutput
Z
zhangjinchao01 已提交
3002
    """
3003 3004
    ipts, parents = __cost_input__(input, label, weight)

L
luotao1 已提交
3005 3006
    Layer(inputs=ipts, type="square_error", name=name,
          **ExtraLayerAttribute.to_kwargs(layer_attr))
X
xuwei06 已提交
3007
    return LayerOutput(name, LayerType.COST, parents=parents, size=1)
Z
zhangjinchao01 已提交
3008 3009 3010


@wrap_name_default("cost")
3011
@layer_support()
3012
def classification_cost(input, label, weight=None, name=None,
3013 3014
                        evaluator=classification_error_evaluator,
                        layer_attr=None):
Z
zhangjinchao01 已提交
3015 3016 3017 3018 3019 3020 3021 3022 3023
    """
    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
3024 3025 3026
    :param weight: The weight affects the cost, namely the scale of cost.
                   It is an optional argument.
    :type weight: LayerOutput
Z
zhangjinchao01 已提交
3027
    :param evaluator: Evaluator method.
3028 3029
    :param layer_attr: layer's extra attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
3030
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3031 3032 3033 3034 3035
    :rtype: LayerOutput
    """
    assert input.layer_type != LayerType.DATA
    assert isinstance(input.activation, SoftmaxActivation)
    assert label.layer_type == LayerType.DATA
3036 3037 3038

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

3039
    Layer(name=name, type="multi-class-cross-entropy", inputs=ipts,
3040
          **ExtraLayerAttribute.to_kwargs(layer_attr))
Z
zhangjinchao01 已提交
3041 3042 3043 3044 3045 3046 3047 3048 3049 3050

    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

3051
        e(name=e.__name__, input=input, label=label, weight=weight)
Z
zhangjinchao01 已提交
3052

3053
    if not isinstance(evaluator, collections.Sequence):
Z
zhangjinchao01 已提交
3054 3055 3056 3057 3058
        evaluator = [evaluator]

    for each_evaluator in evaluator:
        __add_evaluator__(each_evaluator)

X
xuwei06 已提交
3059
    return LayerOutput(name, LayerType.COST, parents=parents, size=1)
Z
zhangjinchao01 已提交
3060

3061

3062
def conv_operator(img, filter, filter_size, num_filters,
3063
                  num_channels=None, stride=1, padding=0,
Z
zhangjinchao01 已提交
3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074
                  filter_size_y=None, stride_y=None, padding_y=None):
    """
    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

3075 3076
       op = conv_operator(img=input1,
                          filter=input2,
3077
                          filter_size=3,
Z
zhangjinchao01 已提交
3078 3079 3080
                          num_filters=64,
                          num_channels=64)

3081 3082 3083 3084
    :param img: input image
    :type img: LayerOutput
    :param filter: input filter
    :type filter: LayerOutput
Z
zhangjinchao01 已提交
3085 3086
    :param filter_size: The x dimension of a filter kernel.
    :type filter_size: int
C
caoying03 已提交
3087 3088 3089
    :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
3090
    :type filter_size_y: int
3091 3092
    :param num_filters: channel of output data.
    :type num_filters: int
3093 3094
    :param num_channels: channel of input data.
    :type num_channels: int
Z
zhangjinchao01 已提交
3095
    :param stride: The x dimension of the stride.
L
luotao02 已提交
3096
    :type stride: int
Z
zhangjinchao01 已提交
3097
    :param stride_y: The y dimension of the stride.
L
luotao02 已提交
3098
    :type stride_y: int
Z
zhangjinchao01 已提交
3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111
    :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
3112

3113 3114
    if num_channels is None:
        num_channels = img.num_filters
3115 3116 3117

    assert isinstance(filter, LayerOutput)
    if filter.size is not None:
3118
        filter.size = filter_size * filter_size_y * num_filters * num_channels
3119

3120
    op = ConvOperator(input_layer_names=[img.name, filter.name],
3121
                      num_filters=num_filters,
Z
zhangjinchao01 已提交
3122 3123 3124
                      conv_conf=Conv(filter_size=filter_size,
                                     padding=padding,
                                     stride=stride,
3125
                                     channels=num_channels,
Z
zhangjinchao01 已提交
3126 3127
                                     filter_size_y=filter_size_y,
                                     padding_y=padding_y,
3128
                                     stride_y=stride_y,
3129
                                     groups=1))
3130
    op.origin = [img, filter]
Z
zhangjinchao01 已提交
3131 3132
    return op

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
@wrap_param_attr_default()
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, param_attr=None):
    """
    ConvProjection with a layer as input.
    It performs element-wise multiplication with weight.

    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

       proj = conv_projection(img=input1,
                              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
3165 3166
    :param num_channels: channel of input data.
    :type num_channels: int
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 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229
    :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
    :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.
        init_w = (2.0 / (filter_size ** 2 * num_channels)) ** 0.5
        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

    proj = ConvProjection(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)

    proj.origin = input
    return proj

Z
zhangjinchao01 已提交
3230 3231

@wrap_name_default()
L
luotao1 已提交
3232 3233
@layer_support()
def conv_shift_layer(a, b, name=None, layer_attr=None):
Z
zhangjinchao01 已提交
3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244
    """
    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:
3245 3246 3247 3248
     - 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
3249 3250 3251 3252 3253

    The example usage is:

    .. code-block:: python

3254
       conv_shift = conv_shift_layer(input=[layer1, layer2])
Z
zhangjinchao01 已提交
3255 3256 3257

    :param name: layer name
    :type name: basestring
3258 3259 3260 3261
    :param a: Input layer a.
    :type a: LayerOutput
    :param b: input layer b
    :type b: LayerOutput
L
luotao1 已提交
3262 3263
    :param layer_attr: layer's extra attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
3264
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3265 3266
    :rtype: LayerOutput
    """
3267 3268
    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
3269 3270 3271
    Layer(
        name=name,
        type=LayerType.CONV_SHIFT_LAYER,
3272
        inputs=[a.name, b.name],
L
luotao1 已提交
3273
        **ExtraLayerAttribute.to_kwargs(layer_attr)
Z
zhangjinchao01 已提交
3274 3275
    )

3276 3277
    return LayerOutput(name, LayerType.CONV_SHIFT_LAYER, parents=[a, b],
                       size=a.size)
Z
zhangjinchao01 已提交
3278 3279 3280 3281 3282


@wrap_name_default()
@wrap_param_attr_default()
@wrap_bias_attr_default()
3283
@wrap_act_default(act=LinearActivation())
Z
zhangjinchao01 已提交
3284
@layer_support(ERROR_CLIPPING, DROPOUT)
3285
def tensor_layer(a, b, size, act=None, name=None,
Z
zhangjinchao01 已提交
3286 3287 3288 3289 3290 3291
                 param_attr=None, bias_attr=None, layer_attr=None):
    """
    This layer performs tensor operation for two input.
    For example, each sample:

    .. math::
3292
       y_{i} = a * W_{i} * {b^\mathrm{T}}, i=0,1,...,K-1
Z
zhangjinchao01 已提交
3293 3294

    In this formular:
3295 3296
      - :math:`a`: the first input contains M elements.
      - :math:`b`: the second input contains N elements.
Z
zhangjinchao01 已提交
3297 3298
      - :math:`y_{i}`: the i-th element of y.
      - :math:`W_{i}`: the i-th learned weight, shape if [M, N]
3299
      - :math:`b^\mathrm{T}`: the transpose of :math:`b_{2}`.
Z
zhangjinchao01 已提交
3300 3301 3302 3303 3304

    The simple usage is:

    .. code-block:: python

3305
       tensor = tensor_layer(a=layer1, b=layer2, size=1000)
Z
zhangjinchao01 已提交
3306 3307 3308

    :param name: layer name
    :type name: basestring
3309 3310 3311 3312
    :param a: Input layer a.
    :type a: LayerOutput
    :param b: input layer b.
    :type b: LayerOutput
Z
zhangjinchao01 已提交
3313
    :param size: the layer dimension.
L
luotao02 已提交
3314
    :type size: int.
Z
zhangjinchao01 已提交
3315 3316 3317
    :param act: Activation Type. Default is tanh.
    :type act: BaseActivation
    :param param_attr: The Parameter Attribute.
3318
    :type param_attr: ParameterAttribute
Z
zhangjinchao01 已提交
3319 3320 3321 3322 3323 3324
    :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
3325
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3326 3327
    :rtype: LayerOutput
    """
3328
    assert isinstance(a, LayerOutput) and isinstance(b, LayerOutput)
Z
zhangjinchao01 已提交
3329 3330 3331 3332 3333 3334
    Layer(
        name=name,
        size=size,
        type=LayerType.TENSOR_LAYER,
        active_type=act.name,
        bias=ParamAttr.to_bias(bias_attr),
3335 3336
        inputs=[Input(a.name, **param_attr.attr),
                Input(b.name)],
Z
zhangjinchao01 已提交
3337 3338
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )
3339
    return LayerOutput(name, LayerType.TENSOR_LAYER, parents=[a, b],
Z
zhangjinchao01 已提交
3340 3341 3342 3343 3344 3345 3346
                       activation=act, size=size)


@wrap_name_default()
@wrap_param_attr_default()
@wrap_bias_attr_default()
@wrap_act_default()
L
luotao1 已提交
3347
@layer_support()
3348
def selective_fc_layer(input, select, size, act=None, name=None,
Z
zhangjinchao01 已提交
3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362
                       pass_generation=False,
                       has_selected_colums=True,
                       mul_ratio=0.02,
                       param_attr=None, bias_attr=None, layer_attr=None):
    """
    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

3363
       sel_fc = selective_fc_layer(input=input, size=128, act=TanhActivation())
Z
zhangjinchao01 已提交
3364 3365 3366 3367 3368

    :param name: The Layer Name.
    :type name: basestring
    :param input: The input layer.
    :type input: LayerOutput|list|tuple
3369 3370 3371
    :param select: The select layer. The output of select layer should be a
                   sparse binary matrix, and treat as the mask of selective fc.
    :type select: LayerOutput
Z
zhangjinchao01 已提交
3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383
    :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
3384
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3385 3386 3387 3388
    :rtype: LayerOutput
    """
    if isinstance(input, LayerOutput):
        input = [input]
3389
        assert not isinstance(param_attr, collections.Sequence)
Z
zhangjinchao01 已提交
3390 3391
        param_attr = [param_attr]
    else:
3392
        if isinstance(param_attr, collections.Sequence):
Z
zhangjinchao01 已提交
3393 3394 3395 3396
            assert len(input) == len(param_attr)
        else:
            param_attr = [copy.deepcopy(param_attr) for _ in range(len(input))]

3397 3398 3399 3400
    assert isinstance(input, collections.Sequence)
    assert isinstance(select, LayerOutput)
    if select.size is not None:
        assert select.size == size
Z
zhangjinchao01 已提交
3401
    Layer(
3402 3403
        inputs=[Input(ipt.name, **attr.attr) for ipt, attr in zip(
            input, param_attr)] + [select.name],
Z
zhangjinchao01 已提交
3404 3405 3406
        name=name,
        type=LayerType.SEL_FC_LAYER,
        size=size,
3407
        bias=ParameterAttribute.to_bias(bias_attr),
Z
zhangjinchao01 已提交
3408 3409 3410 3411 3412 3413
        active_type=act.name,
        selective_fc_pass_generation=pass_generation,
        has_selected_colums=has_selected_colums,
        selective_fc_full_mul_ratio=mul_ratio,
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )
3414 3415
    return LayerOutput(name, LayerType.SEL_FC_LAYER, list(input) + [select],
                       activation=act,
Z
zhangjinchao01 已提交
3416 3417 3418 3419
                       size=size)


@wrap_name_default()
L
luotao1 已提交
3420 3421
@layer_support()
def sampling_id_layer(input, name=None, layer_attr=None):
Z
zhangjinchao01 已提交
3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433 3434 3435
    """
    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
3436 3437
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
3438
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3439 3440
    :rtype: LayerOutput
    """
X
xuwei06 已提交
3441
    l = Layer(
Z
zhangjinchao01 已提交
3442 3443 3444
        name=name,
        type=LayerType.SAMPLING_ID_LAYER,
        inputs=[Input(input.name)],
L
luotao1 已提交
3445
        **ExtraLayerAttribute.to_kwargs(layer_attr)
Z
zhangjinchao01 已提交
3446
    )
X
xuwei06 已提交
3447 3448
    return LayerOutput(name, LayerType.SAMPLING_ID_LAYER, input,
                       size=l.config.size)
Z
zhangjinchao01 已提交
3449 3450 3451


@wrap_name_default()
L
luotao1 已提交
3452 3453 3454
@layer_support()
def slope_intercept_layer(input, name=None, slope=1.0, intercept=0.0,
                          layer_attr=None):
Z
zhangjinchao01 已提交
3455 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475
    """
    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
3476 3477
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
3478
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3479 3480 3481 3482 3483 3484 3485 3486
    :rtype: LayerOutput
    """
    Layer(
        name=name,
        type=LayerType.SLOPE_INTERCEPT_LAYER,
        slope=slope,
        intercept=intercept,
        inputs=[Input(input.name)],
L
luotao1 已提交
3487
        **ExtraLayerAttribute.to_kwargs(layer_attr)
Z
zhangjinchao01 已提交
3488
    )
X
xuwei06 已提交
3489 3490
    return LayerOutput(name, LayerType.SLOPE_INTERCEPT_LAYER, input,
                       size=input.size)
Z
zhangjinchao01 已提交
3491 3492 3493


@wrap_name_default()
L
luotao1 已提交
3494 3495 3496
@layer_support()
def linear_comb_layer(weights, vectors, size=None, name=None,
                      layer_attr=None):
Z
zhangjinchao01 已提交
3497
    """
3498 3499 3500 3501
    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
3502 3503 3504

    .. math::

3505 3506 3507 3508 3509 3510
       z(i) = \sum_{j=0}^{M-1} x(j) y(i+Nj)
    where :math:`0 \le i \le N-1`

    Or in the matrix notation:

    .. math::
Z
zhangjinchao01 已提交
3511

3512
       z = x^\mathrm{T} Y
Z
zhangjinchao01 已提交
3513 3514

    In this formular:
3515 3516 3517 3518 3519 3520
      - :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
3521 3522 3523 3524 3525

    The simple usage is:

    .. code-block:: python

3526
       linear_comb = linear_comb_layer(weights=weight, vectors=vectors,
Z
zhangjinchao01 已提交
3527 3528
                                       size=elem_dim)

3529 3530 3531 3532
    :param weights: The weight layer.
    :type weights: LayerOutput
    :param vectors: The vector layer.
    :type vectors: LayerOutput
Z
zhangjinchao01 已提交
3533 3534 3535 3536
    :param size: the dimension of this layer.
    :type size: int
    :param name: The Layer Name.
    :type name: basestring
L
luotao1 已提交
3537 3538
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
3539
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3540 3541
    :rtype: LayerOutput
    """
3542 3543 3544 3545 3546 3547 3548
    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:
                size = vectors.size / weights.size
        else:
            assert size == vectors.size / weights.size
Z
zhangjinchao01 已提交
3549 3550
    Layer(
        name=name,
3551
        type=LayerType.LINEAR_COMBINATION_LAYER,
Z
zhangjinchao01 已提交
3552
        size=size,
3553
        inputs=[Input(weights.name), Input(vectors.name)],
L
luotao1 已提交
3554
        **ExtraLayerAttribute.to_kwargs(layer_attr)
Z
zhangjinchao01 已提交
3555
    )
3556 3557 3558
    return LayerOutput(name, LayerType.LINEAR_COMBINATION_LAYER,
                       [weights, vectors], size=size)

3559

3560
convex_comb_layer = linear_comb_layer
Z
zhangjinchao01 已提交
3561

3562

Z
zhangjinchao01 已提交
3563
@wrap_name_default()
L
luotao1 已提交
3564
@layer_support()
Z
zhangjinchao01 已提交
3565 3566 3567 3568 3569 3570 3571
def block_expand_layer(input,
                       block_x=0,
                       block_y=0,
                       stride_x=0,
                       stride_y=0,
                       padding_x=0,
                       padding_y=0,
3572
                       num_channels=None,
L
luotao1 已提交
3573 3574
                       name=None,
                       layer_attr=None):
Z
zhangjinchao01 已提交
3575 3576
    """
    Expand feature map to minibatch matrix.
3577
       - matrix width is: block_y * block_x * num_channels
L
luotao02 已提交
3578
       - matirx height is: outputH * outputW
Z
zhangjinchao01 已提交
3579 3580 3581 3582 3583 3584 3585 3586 3587 3588

    .. 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
3589
    time step is block_y * block_x * num_channels. This layer can be used after
Z
zhangjinchao01 已提交
3590 3591
    convolution neural network, and before recurrent neural network.

3592 3593 3594 3595 3596
    The simple usage is:

    .. code-block:: python

       block_expand = block_expand_layer(input,
3597
                                         num_channels=128,
3598 3599 3600 3601 3602
                                         stride_x=1,
                                         stride_y=1,
                                         block_x=1,
                                         block_x=3)

Z
zhangjinchao01 已提交
3603 3604
    :param input: The input layer.
    :type input: LayerOutput
3605 3606
    :param num_channels: The channel number of input layer.
    :type num_channels: int|None
Z
zhangjinchao01 已提交
3607 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 3620
    :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
3621 3622
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
3623
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3624 3625
    :rtype: LayerOutput
    """
3626 3627 3628
    if num_channels is None:
        assert input.num_filters is not None
        num_channels = input.num_filters
X
xuwei06 已提交
3629
    l = Layer(name=name,
3630 3631 3632 3633 3634 3635 3636 3637
          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)),
Z
zhangjinchao01 已提交
3638
          type=LayerType.BLOCK_EXPAND,
L
luotao1 已提交
3639
          **ExtraLayerAttribute.to_kwargs(layer_attr)
3640 3641
          )

X
xuwei06 已提交
3642 3643
    return LayerOutput(name, LayerType.BLOCK_EXPAND, parents=[input],
                       size=l.config.size)
Z
zhangjinchao01 已提交
3644 3645


3646 3647 3648 3649 3650 3651 3652 3653 3654 3655 3656 3657 3658 3659
@wrap_name_default()
@layer_support()
def maxout_layer(input,
                 groups,
                 num_channels=None,
                 size_x=None,
                 size_y=None,
                 name=None,
                 layer_attr=None):
    """
    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.

3660
    So groups should be larger than 1, and the num of channels should be able
3661 3662
    to devided by groups.

3663
    Please refer to Paper:
3664 3665 3666 3667
      - 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
3668

3669 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 3698 3699 3700 3701 3702 3703
    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 size_x: conv output width. If None will be set
                   automatically from previous output.
    :type size_x: int|None
    :param size_y: conv output height. If None will be set
                   automatically from previous output.
    :type size_y: int|None
    :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
X
xuwei06 已提交
3704
    l = Layer(name=name,
3705 3706 3707 3708 3709
          inputs=Input(input.name,
                       maxout=MaxOut(channels=num_channels,
                                     groups=groups)),
          type=LayerType.MAXOUT,
          **ExtraLayerAttribute.to_kwargs(layer_attr))
X
xuwei06 已提交
3710 3711
    return LayerOutput(name, LayerType.MAXOUT, parents=[input],
                       size=l.config.size)
3712 3713


Z
zhangjinchao01 已提交
3714
@wrap_name_default()
L
luotao1 已提交
3715 3716 3717
@layer_support()
def ctc_layer(input, label, size=None, name=None, norm_by_times=False,
              layer_attr=None):
Z
zhangjinchao01 已提交
3718 3719 3720 3721 3722
    """
    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.

3723 3724
    More details can be found by referring to `Connectionist Temporal
    Classification: Labelling Unsegmented Sequence Data with Recurrent
3725 3726
    Neural Networks <http://machinelearning.wustl.edu/mlpapers/paper_files/
    icml2006_GravesFGS06.pdf>`_
3727 3728 3729 3730 3731 3732 3733 3734

    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
3735 3736 3737 3738 3739 3740 3741 3742 3743
    The simple usage:

    .. code-block:: python

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

3744
    :param input: The input layer.
Z
zhangjinchao01 已提交
3745 3746 3747
    :type input: LayerOutput
    :param label: The data layer of label with variable length.
    :type label: LayerOutput
3748
    :param size: category numbers + 1.
Z
zhangjinchao01 已提交
3749
    :type size: int
3750 3751
    :param name: The name of this layer
    :type name: basestring|None
Z
zhangjinchao01 已提交
3752 3753
    :param norm_by_times: Whether to normalization by times. False by default.
    :type norm_by_times: bool
L
luotao1 已提交
3754 3755
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
3756
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3757 3758 3759 3760
    :rtype: LayerOutput
    """
    assert isinstance(input, LayerOutput)
    assert isinstance(label, LayerOutput)
3761 3762 3763 3764 3765
    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
3766
    Layer(
3767 3768 3769 3770
        name=name,
        type=LayerType.CTC_LAYER,
        size=size,
        norm_by_times=norm_by_times,
L
luotao1 已提交
3771 3772
        inputs=[input.name, label.name],
        **ExtraLayerAttribute.to_kwargs(layer_attr)
Z
zhangjinchao01 已提交
3773 3774 3775
    )
    return LayerOutput(name, LayerType.CTC_LAYER, [input, label], size=size)

3776

Z
zhangjinchao01 已提交
3777
@wrap_name_default()
3778
@wrap_param_attr_default()
L
luotao1 已提交
3779 3780 3781
@layer_support()
def crf_layer(input, label, size=None, weight=None, param_attr=None, name=None,
              layer_attr=None):
Z
zhangjinchao01 已提交
3782 3783 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796
    """
    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.
3797
    :type label: LayerOutput
Z
zhangjinchao01 已提交
3798 3799 3800 3801 3802 3803 3804 3805 3806
    :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
L
luotao1 已提交
3807 3808
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
3809
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3810 3811 3812 3813 3814
    :rtype: LayerOutput
    """
    assert isinstance(input, LayerOutput)
    assert isinstance(label, LayerOutput)
    assert weight is None or isinstance(weight, LayerOutput)
3815 3816 3817 3818 3819 3820
    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
3821

3822
    ipts = [Input(input.name, **param_attr.attr),
Z
zhangjinchao01 已提交
3823 3824 3825 3826 3827
            Input(label.name)]
    if weight is not None:
        ipts.append(Input(weight.name))

    Layer(
3828 3829 3830 3831
        name=name,
        type=LayerType.CRF_LAYER,
        size=size,
        inputs=ipts,
L
luotao1 已提交
3832
        **ExtraLayerAttribute.to_kwargs(layer_attr)
Z
zhangjinchao01 已提交
3833 3834 3835 3836
    )
    parents = [input, label]
    if weight is not None:
        parents.append(weight)
X
xuwei06 已提交
3837 3838 3839 3840
    # 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
3841

3842

Z
zhangjinchao01 已提交
3843
@wrap_name_default()
3844
@wrap_param_attr_default()
L
luotao1 已提交
3845 3846 3847
@layer_support()
def crf_decoding_layer(input, size, label=None, param_attr=None, name=None,
                       layer_attr=None):
Z
zhangjinchao01 已提交
3848 3849 3850 3851 3852 3853 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864
    """
    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.

    :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
3865 3866
    :param layer_attr: Extra Layer config.
    :type layer_attr: ExtraLayerAttribute|None
D
dangqingqing 已提交
3867
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
3868 3869 3870 3871 3872 3873
    :rtype: LayerOutput
    """

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

3874
    ipts = [Input(input.name, **param_attr.attr)]
Z
zhangjinchao01 已提交
3875 3876 3877 3878
    if label is not None:
        ipts.append(Input(label.name))

    Layer(
3879 3880 3881 3882
        name=name,
        type=LayerType.CRF_DECODING_LAYER,
        size=size,
        inputs=ipts,
L
luotao1 已提交
3883
        **ExtraLayerAttribute.to_kwargs(layer_attr)
Z
zhangjinchao01 已提交
3884 3885 3886 3887
    )
    parents = [input]
    if label is not None:
        parents.append(label)
X
xuwei06 已提交
3888 3889 3890 3891
    # 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
3892

3893 3894 3895 3896 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913 3914 3915 3916 3917 3918 3919
@wrap_bias_attr_default(has_bias=True)
@wrap_name_default()
@layer_support()
def nce_layer(input, label, num_classes, weight=None,
              num_neg_samples=10, neg_distribution=None,
              name=None, bias_attr=None, layer_attr=None):
    """
    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

       cost = nce_layer(input=layer1, label=layer2, weight=layer3,
                        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.
3920
    :type num_classes: int
3921
    :param num_neg_samples: number of negative samples. Default is 10.
3922
    :type num_neg_samples: int
3923 3924 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 3942
    :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]
    assert isinstance(input, collections.Sequence)
    assert isinstance(label, LayerOutput)
    assert label.layer_type == LayerType.DATA
    if neg_distribution is not None:
        assert isinstance(neg_distribution, collections.Sequence)
        assert len(neg_distribution) == num_classes
        assert sum(neg_distribution) == 1
3943

3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 3956 3957 3958
    ipts_for_layer = []
    parents = []
    for each_input in input:
        assert isinstance(each_input, LayerOutput)
        ipts_for_layer.append(each_input.name)
        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
3959
    l = Layer(
3960 3961 3962 3963 3964 3965 3966 3967 3968
        name=name,
        type=LayerType.NCE_LAYER,
        num_classes=num_classes,
        neg_sampling_dist=neg_distribution,
        num_neg_samples=num_neg_samples,
        inputs=ipts_for_layer,
        bias=ParamAttr.to_bias(bias_attr),
        **ExtraLayerAttribute.to_kwargs(layer_attr)
    )
X
xuwei06 已提交
3969 3970
    return LayerOutput(name, LayerType.NCE_LAYER, parents=parents,
                       size=l.config.size)
3971

Z
zhangjinchao01 已提交
3972 3973 3974
"""
following are cost Layers.
"""
3975 3976


Z
zhangjinchao01 已提交
3977
@wrap_name_default()
L
luotao1 已提交
3978 3979
@layer_support()
def rank_cost(left, right, label, weight=None, name=None, coeff=1.0, layer_attr=None):
Z
zhangjinchao01 已提交
3980
    """
3981
    A cost Layer for learning to rank using gradient descent. Details can refer
3982 3983
    to `papers <http://research.microsoft.com/en-us/um/people/cburges/papers/
    ICML_ranking.pdf>`_.
Z
zhangjinchao01 已提交
3984 3985 3986 3987 3988
    This layer contains at least three inputs. The weight is an optional
    argument, which affects the cost.

    .. math::

L
luotao02 已提交
3989
       C_{i,j} & = -\\tilde{P_{ij}} * o_{i,j} + log(1 + e^{o_{i,j}})
Z
zhangjinchao01 已提交
3990

L
luotao02 已提交
3991
       o_{i,j} & =  o_i - o_j
Z
zhangjinchao01 已提交
3992

L
luotao02 已提交
3993
       \\tilde{P_{i,j}} & = \\{0, 0.5, 1\\} \ or \ \\{0, 1\\}
Z
zhangjinchao01 已提交
3994 3995 3996 3997 3998 3999 4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 4014 4015 4016 4017 4018 4019 4020 4021 4022

    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
4023 4024
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
4025
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041
    :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)

    Layer(name=name,
          type=LayerType.RANK_COST,
          inputs=ipts,
          coeff=coeff,
L
luotao1 已提交
4042
          **ExtraLayerAttribute.to_kwargs(layer_attr)
4043
          )
Z
zhangjinchao01 已提交
4044

X
xuwei06 已提交
4045
    return LayerOutput(name, LayerType.RANK_COST, parents=parents, size=1)
Z
zhangjinchao01 已提交
4046

4047

Z
zhangjinchao01 已提交
4048
@wrap_name_default()
L
luotao1 已提交
4049 4050
@layer_support()
def lambda_cost(input, score, name, NDCG_num=5, max_sort_size=-1, layer_attr=None):
Z
zhangjinchao01 已提交
4051 4052 4053 4054 4055 4056 4057 4058 4059 4060 4061 4062
    """
    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)

4063
    :param input: Samples of the same query should be loaded as sequence.
Z
zhangjinchao01 已提交
4064 4065 4066 4067 4068 4069 4070 4071 4072 4073 4074
    :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
4075 4076 4077
                          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
4078 4079 4080
    :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
4081 4082
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
4083
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4084 4085
    :rtype: LayerOutput
    """
4086 4087 4088
    assert isinstance(input, LayerOutput) and isinstance(score, LayerOutput)
    if score.size is not None:
        assert score.size == 1
Z
zhangjinchao01 已提交
4089 4090 4091 4092
    Layer(name=name,
          type=LayerType.LAMBDA_COST,
          inputs=[input.name, score.name],
          NDCG_num=NDCG_num,
L
luotao1 已提交
4093 4094
          max_sort_size=max_sort_size,
          **ExtraLayerAttribute.to_kwargs(layer_attr)
4095
          )
Z
zhangjinchao01 已提交
4096

X
xuwei06 已提交
4097 4098
    return LayerOutput(name, LayerType.LAMBDA_COST, parents=[input, score],
                       size=1)
Z
zhangjinchao01 已提交
4099

4100

Z
zhangjinchao01 已提交
4101
@wrap_name_default()
L
luotao1 已提交
4102 4103
@layer_support()
def cross_entropy(input, label, name=None, coeff=1.0, layer_attr=None):
Z
zhangjinchao01 已提交
4104 4105 4106 4107 4108
    """
    A loss layer for multi class entropy.

    .. code-block:: python

L
Luo Tao 已提交
4109 4110
       cost = cross_entropy(input=input_layer, 
                            label=label_layer)
Z
zhangjinchao01 已提交
4111 4112 4113 4114 4115 4116 4117 4118 4119

    :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
4120 4121
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
4122
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4123 4124 4125 4126 4127 4128 4129
    :rtype: LayerOutput.
    """

    Layer(name=name,
          type=LayerType.CROSS_ENTROPY,
          inputs=[input.name, label.name],
          coeff=coeff,
L
luotao1 已提交
4130
          **ExtraLayerAttribute.to_kwargs(layer_attr)
4131
          )
X
xuwei06 已提交
4132 4133
    return LayerOutput(name, LayerType.CROSS_ENTROPY, parents=[input, label],
                       size=1)
Z
zhangjinchao01 已提交
4134

4135

Z
zhangjinchao01 已提交
4136
@wrap_name_default()
L
luotao1 已提交
4137
@layer_support()
Z
zhangjinchao01 已提交
4138
def cross_entropy_with_selfnorm(input, label, name=None, coeff=1.0,
L
luotao1 已提交
4139 4140
                                softmax_selfnorm_alpha=0.1,
                                layer_attr=None):
Z
zhangjinchao01 已提交
4141 4142 4143 4144 4145
    """
    A loss layer for multi class entropy with selfnorm.

    .. code-block:: python

L
Luo Tao 已提交
4146 4147
       cost = cross_entropy_with_selfnorm(input=input_layer, 
                                          label=label_layer)
Z
zhangjinchao01 已提交
4148 4149 4150 4151 4152 4153 4154 4155 4156 4157 4158

    :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
4159 4160
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
4161
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4162 4163 4164 4165 4166 4167 4168
    :rtype: LayerOutput.
    """
    Layer(name=name,
          type=LayerType.CROSS_ENTROPY_WITH_SELFNORM,
          inputs=[input.name, label.name],
          coeff=coeff,
          softmax_selfnorm_alpha=softmax_selfnorm_alpha,
L
luotao1 已提交
4169
          **ExtraLayerAttribute.to_kwargs(layer_attr)
4170
          )
Z
zhangjinchao01 已提交
4171 4172 4173

    return LayerOutput(name,
                       LayerType.CROSS_ENTROPY_WITH_SELFNORM,
X
xuwei06 已提交
4174
                       parents=[input, label], size=1)
Z
zhangjinchao01 已提交
4175

4176

X
xuwei06 已提交
4177 4178 4179 4180 4181 4182 4183 4184
@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
4185
       cost = sum_cost(input=input_layer)
X
xuwei06 已提交
4186 4187 4188 4189 4190 4191 4192 4193 4194 4195

    :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
4196
    assert isinstance(input, LayerOutput)
X
xuwei06 已提交
4197 4198 4199 4200 4201 4202 4203 4204
    Layer(name=name,
          type=LayerType.SUM_COST,
          inputs=[input.name],
          **ExtraLayerAttribute.to_kwargs(layer_attr)
          )

    return LayerOutput(name,
                       LayerType.SUM_COST,
L
Luo Tao 已提交
4205 4206
                       parents=[input],
                       size=1)
X
xuwei06 已提交
4207 4208


Z
zhangjinchao01 已提交
4209
@wrap_name_default()
L
luotao1 已提交
4210 4211
@layer_support()
def huber_cost(input, label, name=None, coeff=1.0, layer_attr=None):
Z
zhangjinchao01 已提交
4212 4213 4214 4215 4216
    """
    A loss layer for huber loss.

    .. code-block:: python

L
Luo Tao 已提交
4217 4218
       cost = huber_cost(input=input_layer, 
                         label=label_layer)
Z
zhangjinchao01 已提交
4219 4220 4221 4222 4223 4224 4225 4226 4227

    :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
4228 4229
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
4230
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4231 4232
    :rtype: LayerOutput.
    """
4233 4234 4235
    assert isinstance(input, LayerOutput)
    if input.size is not None:
        assert input.size == 1
Z
zhangjinchao01 已提交
4236 4237 4238 4239
    Layer(name=name,
          type=LayerType.HUBER,
          inputs=[input.name, label.name],
          coeff=coeff,
L
luotao1 已提交
4240
          **ExtraLayerAttribute.to_kwargs(layer_attr)
4241
          )
X
xuwei06 已提交
4242
    return LayerOutput(name, LayerType.HUBER, parents=[input, label], size=1)
Z
zhangjinchao01 已提交
4243

4244

Z
zhangjinchao01 已提交
4245
@wrap_name_default()
L
luotao1 已提交
4246 4247 4248
@layer_support()
def multi_binary_label_cross_entropy(input, label, name=None, coeff=1.0,
                                     layer_attr=None):
Z
zhangjinchao01 已提交
4249 4250 4251 4252 4253
    """
    A loss layer for multi binary label cross entropy.

    .. code-block:: python

L
Luo Tao 已提交
4254 4255
       cost = multi_binary_label_cross_entropy(input=input_layer, 
                                               label=label_layer)
Z
zhangjinchao01 已提交
4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266

    :param input: The first input layer.
    :type input: LayerOutput
    :param label: The input label.
    :type input: LayerOutput
    :param type: The type of cost.
    :type type: basestring
    :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
4267 4268
    :param layer_attr: Extra Layer Attribute.
    :type layer_attr: ExtraLayerAttribute
D
dangqingqing 已提交
4269
    :return: LayerOutput object.
Z
zhangjinchao01 已提交
4270 4271 4272
    :rtype: LayerOutput
    """

4273 4274
    if input.activation is None or \
            not isinstance(input.activation, SigmoidActivation):
Z
zhangjinchao01 已提交
4275
        logger.log(logging.WARN,
4276 4277
                   "%s is not recommend for multi_binary_label_cross_entropy's activation, "
                   "maybe the sigmoid is better" % repr(input.activation))
Z
zhangjinchao01 已提交
4278 4279 4280 4281 4282

    Layer(name=name,
          type=LayerType.MULTI_BIN_LABEL_CROSS_ENTROPY,
          inputs=[input.name, label.name],
          coeff=coeff,
L
luotao1 已提交
4283
          **ExtraLayerAttribute.to_kwargs(layer_attr)
4284
          )
Z
zhangjinchao01 已提交
4285
    return LayerOutput(name, LayerType.MULTI_BIN_LABEL_CROSS_ENTROPY,
X
xuwei06 已提交
4286
                       parents=[input, label], size=1)