The learning continues from where it stopped if a state is found.

diff --git a/cnn-svrt.py b/cnn-svrt.py
index 1511e82..fb1cad9 100755 (executable)
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -279,7 +279,7 @@ def nb_errors(model, data_set):
 
 ######################################################################
 
-def train_model(model, train_set, validation_set):
+def train_model(model, model_filename, train_set, validation_set, nb_epochs_done = 0):
     batch_size = args.batch_size
     criterion = nn.CrossEntropyLoss()
 
@@ -290,7 +290,7 @@ def train_model(model, train_set, validation_set):
 
     start_t = time.time()
 
-    for e in range(0, args.nb_epochs):
+    for e in range(nb_epochs_done, args.nb_epochs):
         acc_loss = 0.0
         for b in range(0, train_set.nb_batches):
             input, target = train_set.get_batch(b)
@@ -305,6 +305,8 @@ def train_model(model, train_set, validation_set):
         log_string('train_loss {:d} {:f}'.format(e + 1, acc_loss),
                    ' [ETA ' + time.ctime(time.time() + dt * (args.nb_epochs - e)) + ']')
 
+        torch.save([ model.state_dict(), e + 1 ], model_filename)
+
         if validation_set is not None:
             nb_validation_errors = nb_errors(model, validation_set)
 
@@ -404,7 +406,7 @@ for problem_number in map(int, args.problems.split(',')):
 
     model_filename = model.name + '_pb:' + \
                      str(problem_number) + '_ns:' + \
-                     int_to_suffix(args.nb_train_samples) + '.param'
+                     int_to_suffix(args.nb_train_samples) + '.state'
 
     nb_parameters = 0
     for p in model.parameters(): nb_parameters += p.numel()
@@ -415,15 +417,17 @@ for problem_number in map(int, args.problems.split(',')):
 
     need_to_train = False
     try:
-        model.load_state_dict(torch.load(model_filename))
+        model_state_dict, nb_epochs_done = torch.load(model_filename)
+        model.load_state_dict(model_state_dict)
         log_string('loaded_model ' + model_filename)
     except:
-        need_to_train = True
+        nb_epochs_done = 0
+
 
     ##################################################
     # Train if necessary
 
-    if need_to_train:
+    if nb_epochs_done < args.nb_epochs:
 
         log_string('training_model ' + model_filename)
 
@@ -450,8 +454,7 @@ for problem_number in map(int, args.problems.split(',')):
         else:
             validation_set = None
 
-        train_model(model, train_set, validation_set)
-        torch.save(model.state_dict(), model_filename)
+        train_model(model, model_filename, train_set, validation_set, nb_epochs_done = nb_epochs_done)
         log_string('saved_model ' + model_filename)
 
         nb_train_errors = nb_errors(model, train_set)