new parameter init_model to initialize the network parameters from an existing model.

(use cases: continue training with more epochs, or save memory by using different subsets of data for each epoch)

2 files changed, 16 insertions, 3 deletions

diff --git a/src/param.h b/src/param.h
index b303514..8e42853 100644
--- a/src/param.h
+++ b/src/param.h
@@ -18,6 +18,7 @@ struct param
     std::string input_words_file;
     std::string output_words_file;
     std::string model_prefix;
+    std::string init_model;
 
     int ngram_size;
     int vocab_size;
diff --git a/src/trainNeuralNetwork.cpp b/src/trainNeuralNetwork.cpp
index fa27627..a088e16 100644
--- a/src/trainNeuralNetwork.cpp
+++ b/src/trainNeuralNetwork.cpp
@@ -94,6 +94,7 @@ int main(int argc, char** argv)
       ValueArg<string> output_words_file("", "output_words_file", "Vocabulary." , false, "", "string", cmd);
       ValueArg<string> validation_file("", "validation_file", "Validation data (one numberized example per line)." , false, "", "string", cmd);
       ValueArg<string> train_file("", "train_file", "Training data (one numberized example per line)." , true, "", "string", cmd);
+      ValueArg<string> init_model("", "init_model", "Initialize parameters from existing model (to continue interrupted training)", false, "", "string", cmd);
 
       cmd.parse(argc, argv);
 
@@ -139,6 +140,7 @@ int main(int argc, char** argv)
       myParam.L2_reg = L2_reg.getValue();
       myParam.init_normal= init_normal.getValue();
       myParam.init_range = init_range.getValue();
+      myParam.init_model = init_model.getValue();
       myParam.normalization_init = normalization_init.getValue();
 
       cerr << "Command line: " << endl;
@@ -187,8 +189,13 @@ int main(int argc, char** argv)
       }
       cerr << loss_function.getDescription() << sep << loss_function.getValue() << endl;
 
-      cerr << init_normal.getDescription() << sep << init_normal.getValue() << endl;
-      cerr << init_range.getDescription() << sep << init_range.getValue() << endl;
+      if (init_model.getValue() != "") {
+        cerr << init_model.getDescription() << sep << init_model.getValue() << endl;
+      }
+      else {
+          cerr << init_normal.getDescription() << sep << init_normal.getValue() << endl;
+          cerr << init_range.getDescription() << sep << init_range.getValue() << endl;
+      }
 
       cerr << num_epochs.getDescription() << sep << num_epochs.getValue() << endl;
       cerr << minibatch_size.getDescription() << sep << minibatch_size.getValue() << endl;
@@ -311,7 +318,12 @@ int main(int argc, char** argv)
         myParam.output_embedding_dimension,
         myParam.share_embeddings);
 
-    nn.initialize(rng, myParam.init_normal, myParam.init_range, -log(myParam.output_vocab_size));
+    if (myParam.init_model != "") {
+      nn.read(myParam.init_model);
+    }
+    else {
+      nn.initialize(rng, myParam.init_normal, myParam.init_range, -log(myParam.output_vocab_size));
+    }
     nn.set_activation_function(string_to_activation_function(myParam.activation_function));
     loss_function_type loss_function = string_to_loss_function(myParam.loss_function);