# 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. """ """ # from activations import * from activations import LinearActivation, ReluActivation, SoftmaxActivation, \ IdentityActivation, TanhActivation, SequenceSoftmaxActivation from attrs import ExtraAttr from default_decorators import wrap_name_default, wrap_act_default, \ wrap_param_default from layers import * # There are too many layers used in network, so import * from poolings import MaxPooling, SumPooling from paddle.trainer.config_parser import * __all__ = ['sequence_conv_pool', 'simple_lstm', "simple_img_conv_pool", "img_conv_bn_pool", 'dropout_layer', 'lstmemory_group', 'lstmemory_unit', 'small_vgg', 'img_conv_group', 'vgg_16_network', 'gru_unit', 'gru_group', 'simple_gru', 'simple_attention', 'text_conv_pool', 'bidirectional_lstm', 'outputs'] ###################################################### # Text CNN # ###################################################### @wrap_name_default("sequence_conv_pooling") def sequence_conv_pool(input, context_len, hidden_size, name=None, context_start=None, pool_type=None, context_proj_layer_name=None, context_proj_param_attr=False, fc_layer_name=None, fc_param_attr=None, fc_bias_attr=None, fc_act=None, pool_bias_attr=None, fc_attr=None, context_attr=None, pool_attr=None): """ Text convolution pooling layers helper. Text input => Context Projection => FC Layer => Pooling => Output. :param name: name of output layer(pooling layer name) :type name: basestring :param input: name of input layer :type input: LayerOutput :param context_len: context projection length. See context_projection's document. :type context_len: int :param hidden_size: FC Layer size. :type hidden_size: int :param context_start: context projection length. See context_projection's context_start. :type context_start: int or None :param pool_type: pooling layer type. See pooling_layer's document. :type pool_type: BasePoolingType. :param context_proj_layer_name: context projection layer name. None if user don't care. :type context_proj_layer_name: basestring :param context_proj_param_attr: context projection parameter attribute. None if user don't care. :type context_proj_param_attr: ParameterAttribute or None. :param fc_layer_name: fc layer name. None if user don't care. :type fc_layer_name: basestring :param fc_param_attr: fc layer parameter attribute. None if user don't care. :type fc_param_attr: ParameterAttribute or None :param fc_bias_attr: fc bias parameter attribute. False if no bias, None if user don't care. :type fc_bias_attr: ParameterAttribute or None :param fc_act: fc layer activation type. None means tanh :type fc_act: BaseActivation :param pool_bias_attr: pooling layer bias attr. None if don't care. False if no bias. :type pool_bias_attr: ParameterAttribute or None. :param fc_attr: fc layer extra attribute. :type fc_attr: ExtraLayerAttribute :param context_attr: context projection layer extra attribute. :type context_attr: ExtraLayerAttribute :param pool_attr: pooling layer extra attribute. :type pool_attr: ExtraLayerAttribute :return: output layer name. :rtype: LayerOutput """ # Set Default Value to param context_proj_layer_name = "%s_conv_proj" % name \ if context_proj_layer_name is None else context_proj_layer_name with mixed_layer(name=context_proj_layer_name, size=input.size * context_len, act=LinearActivation(), layer_attr=context_attr) as m: m += context_projection(input, context_len=context_len, context_start=context_start, padding_attr=context_proj_param_attr) fc_layer_name = "%s_conv_fc" % name \ if fc_layer_name is None else fc_layer_name fl = fc_layer(name=fc_layer_name, input=m, size=hidden_size, act=fc_act, layer_attr=fc_attr, param_attr=fc_param_attr, bias_attr=fc_bias_attr) return pooling_layer(name=name, input=fl, pooling_type=pool_type, bias_attr=pool_bias_attr, layer_attr=pool_attr) text_conv_pool = sequence_conv_pool ############################################################################ # Images # ############################################################################ @wrap_name_default("conv_pool") def simple_img_conv_pool(input, filter_size, num_filters, pool_size, name=None, pool_type=None, act=None, groups=1, conv_stride=1, conv_padding=0, bias_attr=None, num_channel=None, param_attr=None, shared_bias=True, conv_layer_attr=None, pool_stride=1, pool_start=None, pool_padding=0, pool_layer_attr=None): """ Simple image convolution and pooling group. Input => conv => pooling :param name: group name :type name: basestring :param input: input layer name. :type input: LayerOutput :param filter_size: see img_conv_layer for details :type filter_size: int :param num_filters: see img_conv_layer for details :type num_filters: int :param pool_size: see img_pool_layer for details :type pool_size: int :param pool_type: see img_pool_layer for details :type pool_type: BasePoolingType :param act: see img_conv_layer for details :type act: BaseActivation :param groups: see img_conv_layer for details :type groups: int :param conv_stride: see img_conv_layer for details :type conv_stride: int :param conv_padding: see img_conv_layer for details :type conv_padding: int :param bias_attr: see img_conv_layer for details :type bias_attr: ParameterAttribute :param num_channel: see img_conv_layer for details :type num_channel: int :param param_attr: see img_conv_layer for details :type param_attr: ParameterAttribute :param shared_bias: see img_conv_layer for details :type shared_bias: bool :param conv_layer_attr: see img_conv_layer for details :type conv_layer_attr: ExtraLayerAttribute :param pool_stride: see img_conv_layer for details :type pool_stride: int :param pool_start: see img_conv_layer for details :type pool_start: int :param pool_padding: see img_conv_layer for details :type pool_padding: int :param pool_layer_attr: see img_conv_layer for details :type pool_layer_attr: ExtraLayerAttribute :return: Layer's output :rtype: LayerOutput """ _conv_ = img_conv_layer(name="%s_conv" % name, input=input, filter_size=filter_size, num_filters=num_filters, num_channels=num_channel, act=act, groups=groups, stride=conv_stride, padding=conv_padding, bias_attr=bias_attr, param_attr=param_attr, shared_biases=shared_bias, layer_attr=conv_layer_attr) return img_pool_layer(name="%s_pool" % name, input=_conv_, pool_size=pool_size, pool_type=pool_type, stride=pool_stride, start=pool_start, padding=pool_padding, layer_attr=pool_layer_attr) @wrap_name_default("conv_bn_pool") def img_conv_bn_pool(input, filter_size, num_filters, pool_size, name=None, pool_type=None, act=None, groups=1, conv_stride=1, conv_padding=0, conv_bias_attr=None, num_channel=None, conv_param_attr=None, shared_bias=True, conv_layer_attr=None, bn_param_attr=None, bn_bias_attr=None, bn_layer_attr=None, pool_stride=1, pool_start=None, pool_padding=0, pool_layer_attr=None): """ Convolution, batch normalization, pooling group. :param name: group name :type name: basestring :param input: layer's input :type input: LayerOutput :param filter_size: see img_conv_layer's document :type filter_size: int :param num_filters: see img_conv_layer's document :type num_filters: int :param pool_size: see img_pool_layer's document. :type pool_size: int :param pool_type: see img_pool_layer's document. :type pool_type: BasePoolingType :param act: see batch_norm_layer's document. :type act: BaseActivation :param groups: see img_conv_layer's document :type groups: int :param conv_stride: see img_conv_layer's document. :type conv_stride: int :param conv_padding: see img_conv_layer's document. :type conv_padding: int :param conv_bias_attr: see img_conv_layer's document. :type conv_bias_attr: ParameterAttribute :param num_channel: see img_conv_layer's document. :type num_channel: int :param conv_param_attr: see img_conv_layer's document. :type conv_param_attr: ParameterAttribute :param shared_bias: see img_conv_layer's document. :type shared_bias: bool :param conv_layer_attr: see img_conv_layer's document. :type conv_layer_attr: ExtraLayerOutput :param bn_param_attr: see batch_norm_layer's document. :type bn_param_attr: ParameterAttribute. :param bn_bias_attr: see batch_norm_layer's document. :param bn_layer_attr: ParameterAttribute. :param pool_stride: see img_pool_layer's document. :type pool_stride: int :param pool_start: see img_pool_layer's document. :type pool_start: int :param pool_padding: see img_pool_layer's document. :type pool_padding: int :param pool_layer_attr: see img_pool_layer's document. :type pool_layer_attr: ExtraLayerAttribute :return: Layer groups output :rtype: LayerOutput """ __conv__ = img_conv_layer(name="%s_conv" % name, input=input, filter_size=filter_size, num_filters=num_filters, num_channels=num_channel, act=LinearActivation(), groups=groups, stride=conv_stride, padding=conv_padding, bias_attr=conv_bias_attr, param_attr=conv_param_attr, shared_biases=shared_bias, layer_attr=conv_layer_attr) __bn__ = batch_norm_layer(name="%s_bn" % name, input=__conv__, act=act, bias_attr=bn_bias_attr, param_attr=bn_param_attr, layer_attr=bn_layer_attr) return img_pool_layer(name="%s_pool" % name, input=__bn__, pool_type=pool_type, pool_size=pool_size, stride=pool_stride, start=pool_start, padding=pool_padding, layer_attr=pool_layer_attr) @wrap_act_default(param_names=['conv_act'], act=ReluActivation()) @wrap_param_default(param_names=['pool_type'], default_factory=lambda _: MaxPooling()) def img_conv_group(input, conv_num_filter, pool_size, num_channels=None, conv_padding=1, conv_filter_size=3, conv_act=None, conv_with_batchnorm=False, conv_batchnorm_drop_rate=0, pool_stride=1, pool_type=None): """ Image Convolution Group, Used for vgg net. TODO(yuyang18): Complete docs :param conv_batchnorm_drop_rate: :param input: :param conv_num_filter: :param pool_size: :param num_channels: :param conv_padding: :param conv_filter_size: :param conv_act: :param conv_with_batchnorm: :param pool_stride: :param pool_type: :return: """ tmp = input # Type checks assert isinstance(tmp, LayerOutput) assert isinstance(conv_num_filter, list) or isinstance(conv_num_filter, tuple) for each_num_filter in conv_num_filter: assert isinstance(each_num_filter, int) assert isinstance(pool_size, int) def __extend_list__(obj): if not hasattr(obj, '__len__'): return [obj] * len(conv_num_filter) else: return obj conv_padding = __extend_list__(conv_padding) conv_filter_size = __extend_list__(conv_filter_size) conv_act = __extend_list__(conv_act) conv_with_batchnorm = __extend_list__(conv_with_batchnorm) conv_batchnorm_drop_rate = __extend_list__(conv_batchnorm_drop_rate) for i in xrange(len(conv_num_filter)): extra_kwargs = dict() if num_channels is not None: extra_kwargs['num_channels'] = num_channels num_channels = None if conv_with_batchnorm[i]: extra_kwargs['act'] = LinearActivation() else: extra_kwargs['act'] = conv_act[i] tmp = img_conv_layer(input=tmp, padding=conv_padding[i], filter_size=conv_filter_size[i], num_filters=conv_num_filter[i], **extra_kwargs) # logger.debug("tmp.num_filters = %d" % tmp.num_filters) if conv_with_batchnorm[i]: dropout = conv_batchnorm_drop_rate[i] if dropout == 0 or abs(dropout) < 1e-5: # dropout not set tmp = batch_norm_layer(input=tmp, act=conv_act[i]) else: tmp = batch_norm_layer(input=tmp, act=conv_act[i], layer_attr=ExtraAttr(drop_rate=dropout)) return img_pool_layer(input=tmp, stride=pool_stride, pool_size=pool_size, pool_type=pool_type) def small_vgg(input_image, num_channels, num_classes): def __vgg__(ipt, num_filter, times, dropouts, num_channels_=None): return img_conv_group(input=ipt, num_channels=num_channels_, pool_size=2, pool_stride=2, conv_num_filter=[num_filter] * times, conv_filter_size=3, conv_act=ReluActivation(), conv_with_batchnorm=True, conv_batchnorm_drop_rate=dropouts, pool_type=MaxPooling()) tmp = __vgg__(input_image, 64, 2, [0.3, 0], num_channels) tmp = __vgg__(tmp, 128, 2, [0.4, 0]) tmp = __vgg__(tmp, 256, 3, [0.4, 0.4, 0]) tmp = __vgg__(tmp, 512, 3, [0.4, 0.4, 0]) tmp = img_pool_layer(input = tmp, stride = 2, pool_size = 2, pool_type = MaxPooling()) tmp = dropout_layer(input=tmp, dropout_rate=0.5) tmp = fc_layer(input=tmp, size=512, layer_attr=ExtraAttr(drop_rate=0.5), act=LinearActivation()) tmp = batch_norm_layer(input=tmp, act=ReluActivation()) return fc_layer(input=tmp, size=num_classes, act=SoftmaxActivation()) def vgg_16_network(input_image, num_channels, num_classes=1000): """ Same model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8 :param num_classes: :param input_image: :type input_image: LayerOutput :param num_channels: :type num_channels: int :return: """ tmp = img_conv_group(input=input_image, num_channels=num_channels, conv_padding=1, conv_num_filter=[64, 64], conv_filter_size=3, conv_act=ReluActivation(), pool_size=2, pool_stride=2, pool_type=MaxPooling()) tmp = img_conv_group(input=tmp, conv_num_filter=[128, 128], conv_padding=1, conv_filter_size=3, conv_act=ReluActivation(), pool_stride=2, pool_type=MaxPooling(), pool_size=2) tmp = img_conv_group(input=tmp, conv_num_filter=[256, 256, 256], conv_padding=1, conv_filter_size=3, conv_act=ReluActivation(), pool_stride=2, pool_type=MaxPooling(), pool_size=2) tmp = img_conv_group(input=tmp, conv_num_filter=[512, 512, 512], conv_padding=1, conv_filter_size=3, conv_act=ReluActivation(), pool_stride=2, pool_type=MaxPooling(), pool_size=2) tmp = img_conv_group(input=tmp, conv_num_filter=[512, 512, 512], conv_padding=1, conv_filter_size=3, conv_act=ReluActivation(), pool_stride=2, pool_type=MaxPooling(), pool_size=2) tmp = fc_layer(input=tmp, size=4096, act=ReluActivation(), layer_attr=ExtraAttr(drop_rate=0.5)) tmp = fc_layer(input=tmp, size=4096, act=ReluActivation(), layer_attr=ExtraAttr(drop_rate=0.5)) return fc_layer(input=tmp, size=num_classes, act=SoftmaxActivation()) ############################################################################ # Recurrent # ############################################################################ @wrap_name_default("lstm") def simple_lstm(input, size, name=None, reverse=False, mat_param_attr=None, bias_param_attr=None, inner_param_attr=None, act=None, gate_act=None, state_act=None, mixed_layer_attr=None, lstm_cell_attr=None): """ Simple LSTM Cell. It just combine a mix_layer with fully_matrix_projection and a lstmemory layer. The simple lstm cell was implemented as follow equations. .. math:: i_t = \\sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i) f_t = \\sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f) c_t = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c) o_t = \\sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o) h_t = o_t tanh(c_t) Please refer **Generating Sequences With Recurrent Neural Networks** if you want to know what lstm is. Link_ is here. .. _Link: http://arxiv.org/abs/1308.0850 :param name: lstm layer name. :type name: basestring :param input: input layer name. :type input: LayerOutput :param size: lstm layer size. :type size: int :param reverse: is lstm reversed :type reverse: bool :param mat_param_attr: mixed layer's matrix projection parameter attribute. :type mat_param_attr: ParameterAttribute :param bias_param_attr: bias parameter attribute. False means no bias, None means default bias. :type bias_param_attr: ParameterAttribute|False :param inner_param_attr: lstm cell parameter attribute. :type inner_param_attr: ParameterAttribute :param act: lstm final activate type :type act: BaseActivation :param gate_act: lstm gate activate type :type gate_act: BaseActivation :param state_act: lstm state activate type. :type state_act: BaseActivation :param mixed_layer_attr: mixed layer's extra attribute. :type mixed_layer_attr: ExtraLayerAttribute :param lstm_cell_attr: lstm layer's extra attribute. :type lstm_cell_attr: ExtraLayerAttribute :return: lstm layer name. :rtype: LayerOutput """ fc_name = 'lstm_transform_%s' % name with mixed_layer(name=fc_name, size=size * 4, act=IdentityActivation(), layer_attr=mixed_layer_attr, bias_attr=False) as m: m += full_matrix_projection(input, param_attr=mat_param_attr) return lstmemory(name=name, input=m, reverse=reverse, bias_attr=bias_param_attr, param_attr=inner_param_attr, act=act, gate_act=gate_act, state_act=state_act, layer_attr=lstm_cell_attr) @wrap_name_default('lstm_unit') def lstmemory_unit(input, name=None, size=None, mixed_bias_attr=None, mixed_layer_attr=None, param_attr=None, lstm_bias_attr=None, act=None, gate_act=None, state_act=None, lstm_layer_attr=None, get_output_layer_attr=None): """ TODO(yuyang18): complete docs @param input: @param name: @param size: @param mixed_bias_attr: @param mixed_layer_attr: @param param_attr: @param lstm_bias_attr: @param act: @param gate_act: @param state_act: @param lstm_layer_attr: @param get_output_layer_attr: @return: """ if size is None: assert input.size % 4 == 0 size = input.size / 4 out_mem = memory(name=name, size=size) state_mem = memory(name="%s_state" % name, size=size) with mixed_layer(name="%s_input_recurrent" % name, size=size * 4, bias_attr=mixed_bias_attr, layer_attr=mixed_layer_attr, act=IdentityActivation()) as m: m += identity_projection(input=input) m += full_matrix_projection(input=out_mem, param_attr=param_attr) lstm_out = lstm_step_layer( name=name, input=m, state=state_mem, size=size, bias_attr=lstm_bias_attr, act=act, gate_act=gate_act, state_act=state_act, layer_attr=lstm_layer_attr ) get_output_layer(name='%s_state' % name, input=lstm_out, arg_name='state', layer_attr=get_output_layer_attr) return lstm_out @wrap_name_default('lstm_group') def lstmemory_group(input, size=None, name=None, reverse=False, param_attr=None, mix_bias_attr=None, lstm_bias_attr=None, act=None, gate_act=None, state_act=None, mixed_layer_attr=None, lstm_layer_attr=None, get_output_layer_attr=None): """ TODO(yuyang18): complete docs @param input: @param size: @param name: @param reverse: @param param_attr: @param mix_bias_attr: @param lstm_bias_attr: @param act: @param gate_act: @param state_act: @param mixed_layer_attr: @param lstm_layer_attr: @param get_output_layer_attr: @return: """ def __lstm_step__(ipt): return lstmemory_unit(input=ipt, name=name, size=size, mixed_bias_attr=mix_bias_attr, mixed_layer_attr=mixed_layer_attr, param_attr=param_attr, lstm_bias_attr=lstm_bias_attr, act=act, gate_act=gate_act, state_act=state_act, lstm_layer_attr=lstm_layer_attr, get_output_layer_attr=get_output_layer_attr) return recurrent_group(name='%s_recurrent_group' % name, step=__lstm_step__, reverse=reverse, input=input) @wrap_name_default('gru_unit') def gru_unit(input, size=None, name=None, gru_bias_attr=None, act=None, gate_act=None, gru_layer_attr=None): """ :param input: :type input: LayerOutput :param name: :param size: :param gru_bias_attr: :param act: :param gate_act: :param gru_layer_attr: :return: """ assert input.size % 3 == 0 if size is None: size = input.size / 3 out_mem = memory(name=name, size=size) gru_out = gru_step_layer( name=name, input=input, output_mem=out_mem, size=size, bias_attr=gru_bias_attr, act=act, gate_act=gate_act, layer_attr=gru_layer_attr ) return gru_out @wrap_name_default('gru_group') def gru_group(input, size=None, name=None, reverse=False, gru_bias_attr=None, act=None, gate_act=None, gru_layer_attr=None): def __gru_step__(ipt): return gru_unit( input=ipt, name=name, size=size, gru_bias_attr=gru_bias_attr, act=act, gate_act=gate_act, gru_layer_attr=gru_layer_attr ) return recurrent_group(name='%s_recurrent_group' % name, step=__gru_step__, reverse=reverse, input=input) @wrap_name_default('simple_gru') def simple_gru(input, size, name=None, reverse=False, mixed_param_attr=None, mixed_bias_param_attr=None, mixed_layer_attr=None, gru_bias_attr=None, act=None, gate_act=None, gru_layer_attr=None ): with mixed_layer(name='%s_transform' % name, size=size * 3, bias_attr=mixed_bias_param_attr, layer_attr=mixed_layer_attr) as m: m += full_matrix_projection(input=input, param_attr=mixed_param_attr) return gru_group(name=name, size=size, input=m, reverse=reverse, gru_bias_attr=gru_bias_attr, act=act, gate_act=gate_act, gru_layer_attr=gru_layer_attr) @wrap_name_default("bidirectional_lstm") def bidirectional_lstm(input, size, name=None, return_seq=False, fwd_mat_param_attr=None, fwd_bias_param_attr=None, fwd_inner_param_attr=None, fwd_act=None, fwd_gate_act=None, fwd_state_act=None, fwd_mixed_layer_attr=None, fwd_lstm_cell_attr=None, bwd_mat_param_attr=None, bwd_bias_param_attr=None, bwd_inner_param_attr=None, bwd_act=None, bwd_gate_act=None, bwd_state_act=None, bwd_mixed_layer_attr=None, bwd_lstm_cell_attr=None, last_seq_attr=None, first_seq_attr=None, concat_attr=None, concat_act=None): """ TODO(yuyang18): Complete docs :param name: bidirectional lstm layer name. :type name: basestring :param input: input layer. :type input: LayerOutput :param size: lstm layer size. :type size: int :param return_seq: If False, concat word in last time step and return. If True, concat sequnce in all time step and return. :type return_seq: bool :return: lstm layer name. :rtype: LayerOutput """ args = locals() fw = simple_lstm(name='%s_fw' % name, input=input, size=size, **dict((k[len('fwd_'):], v) for k, v in args.iteritems() if k.startswith('fwd_'))) bw = simple_lstm(name="%s_bw" % name, input=input, size=size, reverse=True, **dict((k[len('bwd_'):], v) for k, v in args.iteritems() if k.startswith('bwd_'))) if return_seq: return concat_layer(name=name, input=[fw, bw], layer_attr=concat_attr, act=concat_act) else: fw_seq = last_seq(name="%s_fw_last" % name, input=fw, layer_attr=last_seq_attr) bw_seq = first_seq(name="%s_bw_last" % name, input=bw, layer_attr=first_seq_attr) return concat_layer(name=name, input=[fw_seq, bw_seq], layer_attr=concat_attr, act=concat_act) @wrap_name_default() @wrap_act_default(param_names=['weight_act'], act=TanhActivation()) def simple_attention(encoded_sequence, encoded_proj, decoder_state, transform_param_attr=None, softmax_param_attr=None, weight_act=None, name=None): """ Calculate and then return a context vector by attention machanism. Size of the context vector equals to size of encoded_sequence. .. math:: a(s_{i-1},h_{j}) = v_{a}f(W_{a}s_{t-1} + U_{a}h_{j}) .. math:: e_{i,j} = a(s_{i-1}, h_{j}) .. math:: a_{i,j} = \\frac{exp(e_{i,i})}{\\sum_{k=1}^{T_{x}{exp(e_{i,k})}}} .. math:: c_{i} = \\sum_{j=1}^{T_{x}}a_{i,j}h_{j} where :math:`h_{j}` is the jth element of encoded_sequence, :math:`U_{a}h_{j}` is the jth element of encoded_proj :math:`s_{i-1}` is decoder_state :math:`f` is weight_act, and is set to tanh by default. Please refer to **Neural Machine Translation by Jointly Learning to Align and Translate** for more details. The link is as follows: https://arxiv.org/abs/1409.0473. The example usage is: .. code-block:: python context = simple_attention(encoded_sequence=enc_seq, encoded_proj=enc_proj, decoder_state=decoder_prev,) :param name: name of the attention model. :type name: basestring :param softmax_param_attr: parameter attribute of sequence softmax that is used to produce attention weight :type softmax_param_attr: ParameterAttribute :param weight_act: activation of the attention model :type weight_act: Activation :param encoded_sequence: output of the encoder :type encoded_sequence: LayerOutput :param encoded_proj: attention weight is computed by a feed forward neural network which has two inputs : decoder's hidden state of previous time step and encoder's output. encoded_proj is output of the feed-forward network for encoder's output. Here we pre-compute it outside simple_attention for speed consideration. :type encoded_proj: LayerOutput :param decoder_state: hidden state of decoder in previous time step :type decoder_state: LayerOutput :param transform_param_attr: parameter attribute of the feed-forward network that takes decoder_state as inputs to compute attention weight. :type transform_param_attr: ParameterAttribute :return: a context vector """ assert encoded_proj.size == decoder_state.size proj_size = encoded_proj.size with mixed_layer(size=proj_size, name="%s_transform" % name) as m: m += full_matrix_projection(decoder_state, param_attr=transform_param_attr) expanded = expand_layer(input=m, expand_as=encoded_sequence, name='%s_expand' % name) with mixed_layer(size=proj_size, act=weight_act, name="%s_combine" % name) as m: m += identity_projection(expanded) m += identity_projection(encoded_proj) # sequence softmax is used to normalize similarities between decoder state # and encoder outputs into a distribution attention_weight = fc_layer(input=m, size=1, act=SequenceSoftmaxActivation(), param_attr=softmax_param_attr, name="%s_softmax" % name, bias_attr=False) scaled = scaling_layer(weight=attention_weight, input=encoded_sequence, name='%s_scaling' % name) return pooling_layer(input=scaled, pooling_type=SumPooling(), name="%s_pooling" % name) ############################################################################ # Miscs # ############################################################################ @wrap_name_default("dropout") def dropout_layer(input, dropout_rate, name=None): """ @TODO(yuyang18): Add comments. :param name: :param input: :param dropout_rate: :return: """ return addto_layer(name=name, input=input, act=LinearActivation(), bias_attr=False, layer_attr=ExtraAttr(drop_rate=dropout_rate)) def outputs(layers): """ Declare the end of network. Currently it will only calculate the input/output order of network. It will calculate the predict network or train network's output automatically. :param layers: :type layers: list|tuple|LayerOutput :return: """ def __dfs_travel__(layer, predicate=lambda x: x.layer_type == LayerType.DATA): """ DFS LRV Travel for output layer. The return order is define order for data_layer in this leaf node. :param layer: :type layer: LayerOutput :return: """ assert isinstance(layer, LayerOutput), "layer is %s" % (layer) retv = [] if layer.parents is not None: for p in layer.parents: retv.extend(__dfs_travel__(p, predicate)) if predicate(layer): retv.append(layer) return retv if isinstance(layers, LayerOutput): layers = [layers] assert len(layers) > 0 if len(layers) != 1: logger.warning("EndOfNetwork routine try to calculate network's" " inputs and outputs order. It might not work well." "Please see follow log carefully.") inputs = [] outputs_ = [] for each_layer in layers: assert isinstance(each_layer, LayerOutput) inputs.extend(__dfs_travel__(each_layer)) outputs_.extend(__dfs_travel__( each_layer, lambda x: x.layer_type == LayerType.COST)) # Currently, we got each leaf node's inputs order, output order. # We merge them together. final_inputs = [] final_outputs = [] for each_input in inputs: assert isinstance(each_input, LayerOutput) if each_input.name not in final_inputs: final_inputs.append(each_input.name) for each_output in outputs_: assert isinstance(each_output, LayerOutput) if each_output.name not in final_outputs: final_outputs.append(each_output.name) logger.info( "".join(["The input order is [", ", ".join(final_inputs), "]"]) ) logger.info( "".join(["The output order is [", ", ".join(final_outputs), "]" ])) Inputs(*final_inputs) if len(final_outputs) != 0: Outputs(*final_outputs) else: Outputs(*map(lambda x: x.name, layers))