diff options
Diffstat (limited to 'src/propagator.h')
| -rw-r--r-- | src/propagator.h | 51 |
1 files changed, 41 insertions, 10 deletions
diff --git a/src/propagator.h b/src/propagator.h index df8a7c2..9f214de 100644 --- a/src/propagator.h +++ b/src/propagator.h @@ -23,6 +23,7 @@ public: Node<Linear_layer> second_hidden_linear_node; Node<Activation_function> second_hidden_activation_node; Node<Output_word_embeddings> output_layer_node; + bool skip_hidden; public: propagator () : minibatch_size(0), pnn(0) { } @@ -38,6 +39,7 @@ public: output_layer_node(&nn.output_layer, minibatch_size), minibatch_size(minibatch_size) { + skip_hidden = (nn.num_hidden == 0); } // This must be called if the underlying model is resized. @@ -83,12 +85,14 @@ public: stop_timer(1); + if (!skip_hidden) { start_timer(2); second_hidden_linear_node.param->fProp(first_hidden_activation_node.fProp_matrix, second_hidden_linear_node.fProp_matrix); second_hidden_activation_node.param->fProp(second_hidden_linear_node.fProp_matrix, second_hidden_activation_node.fProp_matrix); stop_timer(2); + } // The propagation stops here because the last layer is very expensive. } @@ -112,19 +116,20 @@ public: stop_timer(7); start_timer(8); + Node<Activation_function> & final_hidden_activation_node = skip_hidden ? first_hidden_activation_node : second_hidden_activation_node; if (parameter_update == "SGD") { - output_layer_node.param->computeGradient(second_hidden_activation_node.fProp_matrix, + output_layer_node.param->computeGradient(final_hidden_activation_node.fProp_matrix, output, learning_rate, momentum); } else if (parameter_update == "ADA") { - output_layer_node.param->computeGradientAdagrad(second_hidden_activation_node.fProp_matrix, + output_layer_node.param->computeGradientAdagrad(final_hidden_activation_node.fProp_matrix, output, learning_rate); } else if (parameter_update == "ADAD") { //std::cerr<<"Adadelta gradient"<<endl; - int current_minibatch_size = second_hidden_activation_node.fProp_matrix.cols(); - output_layer_node.param->computeGradientAdadelta(second_hidden_activation_node.fProp_matrix, + int current_minibatch_size = final_hidden_activation_node.fProp_matrix.cols(); + output_layer_node.param->computeGradientAdadelta(final_hidden_activation_node.fProp_matrix, output, 1.0/current_minibatch_size, conditioning_constant, @@ -166,21 +171,22 @@ public: start_timer(8); + Node<Activation_function> & final_hidden_activation_node = skip_hidden ? first_hidden_activation_node : second_hidden_activation_node; if (parameter_update == "SGD") { - output_layer_node.param->computeGradient(second_hidden_activation_node.fProp_matrix, + output_layer_node.param->computeGradient(final_hidden_activation_node.fProp_matrix, samples, weights, learning_rate, momentum); } else if (parameter_update == "ADA") { - output_layer_node.param->computeGradientAdagrad(second_hidden_activation_node.fProp_matrix, + output_layer_node.param->computeGradientAdagrad(final_hidden_activation_node.fProp_matrix, samples, weights, learning_rate); } else if (parameter_update == "ADAD") { - int current_minibatch_size = second_hidden_activation_node.fProp_matrix.cols(); + int current_minibatch_size = final_hidden_activation_node.fProp_matrix.cols(); //std::cerr<<"Adadelta gradient"<<endl; - output_layer_node.param->computeGradientAdadelta(second_hidden_activation_node.fProp_matrix, + output_layer_node.param->computeGradientAdadelta(final_hidden_activation_node.fProp_matrix, samples, weights, 1.0/current_minibatch_size, @@ -217,7 +223,22 @@ private: // functions are together ////////BACKPROP//////////// start_timer(9); - second_hidden_activation_node.param->bProp(output_layer_node.bProp_matrix, + if (skip_hidden) + { + start_timer(9); + first_hidden_activation_node.param->bProp(output_layer_node.bProp_matrix, + first_hidden_activation_node.bProp_matrix, + first_hidden_linear_node.fProp_matrix, + first_hidden_activation_node.fProp_matrix); + + first_hidden_linear_node.param->bProp(first_hidden_activation_node.bProp_matrix, + first_hidden_linear_node.bProp_matrix); + stop_timer(9); + + } + else + { + second_hidden_activation_node.param->bProp(output_layer_node.bProp_matrix, second_hidden_activation_node.bProp_matrix, second_hidden_linear_node.fProp_matrix, second_hidden_activation_node.fProp_matrix); @@ -233,13 +254,16 @@ private: first_hidden_linear_node.fProp_matrix, first_hidden_activation_node.fProp_matrix); - first_hidden_linear_node.param->bProp(first_hidden_activation_node.bProp_matrix, + first_hidden_linear_node.param->bProp(first_hidden_activation_node.bProp_matrix, first_hidden_linear_node.bProp_matrix); stop_timer(11); + } //std::cerr<<"First hidden layer node backprop matrix is"<<first_hidden_linear_node.bProp_matrix<<std::endl; //std::getchar(); ////COMPUTE GRADIENT///////// if (parameter_update == "SGD") { + if (!skip_hidden) + { start_timer(10); second_hidden_linear_node.param->computeGradient(second_hidden_activation_node.bProp_matrix, first_hidden_activation_node.fProp_matrix, @@ -247,6 +271,7 @@ private: momentum, L2_reg); stop_timer(10); + } // First hidden layer @@ -265,12 +290,15 @@ private: learning_rate, momentum, L2_reg); stop_timer(13); } else if (parameter_update == "ADA") { + if (!skip_hidden) + { start_timer(10); second_hidden_linear_node.param->computeGradientAdagrad(second_hidden_activation_node.bProp_matrix, first_hidden_activation_node.fProp_matrix, learning_rate, L2_reg); stop_timer(10); + } // First hidden layer @@ -293,6 +321,8 @@ private: } else if (parameter_update == "ADAD") { int current_minibatch_size = first_hidden_activation_node.fProp_matrix.cols(); //std::cerr<<"Adadelta gradient"<<endl; + if (!skip_hidden) + { start_timer(10); second_hidden_linear_node.param->computeGradientAdadelta(second_hidden_activation_node.bProp_matrix, first_hidden_activation_node.fProp_matrix, @@ -301,6 +331,7 @@ private: conditioning_constant, decay); stop_timer(10); + } //std::cerr<<"Finished gradient for second hidden linear layer"<<std::endl; // First hidden layer |