LSTM层注释，东西全在帖子里

#include <string> #include <vector> #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/filler.hpp" #include "caffe/layer.hpp" #include "caffe/layers/sequence_layers.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { template <typename Dtype> void ALSTMLayer<Dtype>::RecurrentInputBlobNames(vector<string>* names) const { names->resize(2); (*names)[0] = "h_0"; (*names)[1] = "c_0"; } template <typename Dtype> void ALSTMLayer<Dtype>::RecurrentOutputBlobNames(vector<string>* names) const { names->resize(2); (*names)[0] = "h_" + format_int(this->T_); (*names)[1] = "c_T"; } template <typename Dtype> void ALSTMLayer<Dtype>::RecurrentInputShapes(vector<BlobShape>* shapes) const { const int num_output = this->layer_param_.recurrent_param().num_output(); const int num_blobs = 2; shapes->resize(num_blobs); for (int i = 0; i < num_blobs; ++i) { (*shapes)[i].Clear(); (*shapes)[i].add_dim(1); // a single timestep (*shapes)[i].add_dim(this->N_); (*shapes)[i].add_dim(num_output); } } template <typename Dtype> void ALSTMLayer<Dtype>::OutputBlobNames(vector<string>* names) const { names->resize(2); (*names)[0] = "h"; (*names)[1] = "mask"; } template <typename Dtype> void ALSTMLayer<Dtype>::FillUnrolledNet(NetParameter* net_param) const { const int num_output = this->layer_param_.recurrent_param().num_output(); CHECK_GT(num_output, 0) << "num_output must be positive"; const FillerParameter& weight_filler = this->layer_param_.recurrent_param().weight_filler(); const FillerParameter& bias_filler = this->layer_param_.recurrent_param().bias_filler(); // Add generic LayerParameter's (without bottoms/tops) of layer types we'll // use to save redundant code. LayerParameter hidden_param; hidden_param.set_type("InnerProduct"); hidden_param.mutable_inner_product_param()->set_num_output(num_output * 4); hidden_param.mutable_inner_product_param()->set_bias_term(false); hidden_param.mutable_inner_product_param()->set_axis(1); hidden_param.mutable_inner_product_param()-> mutable_weight_filler()->CopyFrom(weight_filler); LayerParameter biased_hidden_param(hidden_param); biased_hidden_param.mutable_inner_product_param()->set_bias_term(true); biased_hidden_param.mutable_inner_product_param()-> mutable_bias_filler()->CopyFrom(bias_filler); LayerParameter attention_param; attention_param.set_type("InnerProduct"); attention_param.mutable_inner_product_param()->set_num_output(256); attention_param.mutable_inner_product_param()->set_bias_term(false); attention_param.mutable_inner_product_param()->set_axis(2); attention_param.mutable_inner_product_param()-> mutable_weight_filler()->CopyFrom(weight_filler); LayerParameter biased_attention_param(attention_param); biased_attention_param.mutable_inner_product_param()->set_bias_term(true); biased_attention_param.mutable_inner_product_param()-> mutable_bias_filler()->CopyFrom(bias_filler); // weight + bias LayerParameter sum_param; sum_param.set_type("Eltwise"); sum_param.mutable_eltwise_param()->set_operation( EltwiseParameter_EltwiseOp_SUM); LayerParameter slice_param; slice_param.set_type("Slice"); slice_param.mutable_slice_param()->set_axis(0); LayerParameter softmax_param; softmax_param.set_type("Softmax"); softmax_param.mutable_softmax_param()->set_axis(-1); LayerParameter split_param; split_param.set_type("Split"); LayerParameter scale_param; scale_param.set_type("Scale"); LayerParameter permute_param; permute_param.set_type("Permute"); LayerParameter reshape_param; reshape_param.set_type("Reshape"); LayerParameter bias_layer_param; bias_layer_param.set_type("Bias"); LayerParameter pool_param; pool_param.set_type("Pooling"); LayerParameter reshape_layer_param; reshape_layer_param.set_type("Reshape"); BlobShape input_shape; input_shape.add_dim(1); // c_0 and h_0 are a single timestep input_shape.add_dim(this->N_); input_shape.add_dim(num_output); net_param->add_input("c_0"); net_param->add_input_shape()->CopyFrom(input_shape); net_param->add_input("h_0"); net_param->add_input_shape()->CopyFrom(input_shape); LayerParameter* cont_slice_param = net_param->add_layer(); cont_slice_param->CopyFrom(slice_param); cont_slice_param->set_name("cont_slice"); cont_slice_param->add_bottom("cont"); cont_slice_param->mutable_slice_param()->set_axis(1); LayerParameter* x_slice_param = net_param->add_layer(); x_slice_param->CopyFrom(slice_param); x_slice_param->set_name("x_slice"); x_slice_param->add_bottom("x"); // Add layer to transform all timesteps of x to the hidden state dimension. // W_xc_x = W_xc * x + b_c /* { LayerParameter* x_transform_param = net_param->add_layer(); x_transform_param->CopyFrom(biased_hidden_param); x_transform_param->set_name("x_transform"); x_transform_param->add_param()->set_name("W_xc"); x_transform_param->add_param()->set_name("b_c"); x_transform_param->add_bottom("x"); x_transform_param->add_top("W_xc_x"); } if (this->static_input_) { // Add layer to transform x_static to the gate dimension. // W_xc_x_static = W_xc_static * x_static LayerParameter* x_static_transform_param = net_param->add_layer(); x_static_transform_param->CopyFrom(hidden_param); x_static_transform_param->mutable_inner_product_param()->set_axis(1); x_static_transform_param->set_name("W_xc_x_static"); x_static_transform_param->add_param()->set_name("W_xc_static"); x_static_transform_param->add_bottom("x_static"); x_static_transform_param->add_top("W_xc_x_static"); LayerParameter* reshape_param = net_param->add_layer(); reshape_param->set_type("Reshape"); BlobShape* new_shape = reshape_param->mutable_reshape_param()->mutable_shape(); new_shape->add_dim(1); // One timestep. new_shape->add_dim(this->N_); new_shape->add_dim( x_static_transform_param->inner_product_param().num_output()); reshape_param->add_bottom("W_xc_x_static"); reshape_param->add_top("W_xc_x_static"); } LayerParameter* x_slice_param = net_param->add_layer(); x_slice_param->CopyFrom(slice_param); x_slice_param->add_bottom("W_xc_x"); x_slice_param->set_name("W_xc_x_slice"); */ LayerParameter output_concat_layer; output_concat_layer.set_name("h_concat"); output_concat_layer.set_type("Concat"); output_concat_layer.add_top("h"); output_concat_layer.mutable_concat_param()->set_axis(0); LayerParameter output_m_layer; output_m_layer.set_name("m_concat"); output_m_layer.set_type("Concat"); output_m_layer.add_top("mask"); output_m_layer.mutable_concat_param()->set_axis(0); // out put 2 for (int t = 1; t <= this->T_; ++t) { string tm1s = format_int(t - 1); string ts = format_int(t); cont_slice_param->add_top("cont_" + ts); x_slice_param->add_top("x_" + ts); // Add a layer to permute x { LayerParameter* permute_x_param = net_param->add_layer(); permute_x_param->CopyFrom(permute_param); permute_x_param->set_name("permute_x_" + ts); permute_x_param->mutable_permute_param()->add_order(2); permute_x_param->mutable_permute_param()->add_order(0); permute_x_param->mutable_permute_param()->add_order(1); permute_x_param->mutable_permute_param()->add_order(3);