Save parameters after epochs.

yann/modules/resultor.py

@@ -104,7 +104,8 @@ def __init__( self, resultor_init_args, verbose = 1):
     def process_results(    self,
                             cost,
                             lr,
-                            mom,                        
+                            mom,    
+                            params = None,                    
                             verbose = 2 ):
         """
         This method will print results and also write them down in the appropriate files.

yann/network.py

@@ -15,7 +15,7 @@
 if nx_installed is True:
     import networkx as nx
 
-import time
+import time, os
 from collections import OrderedDict
 
 import numpy
@@ -2831,6 +2831,7 @@ def train(self, verbose = 2, **kwargs):
         Args:
             epochs: ``(num_epochs for each learning rate... )`` to train Default is ``(20, 20)``
             validate_after_epochs: 1, after how many epochs do you want to validate ?
+            save_after_epochs: 1, Save network after that many epochs of training.            
             show_progress: default is ``True``, will display a clean progressbar.
                              If ``verbose`` is ``3`` or more - False
             early_terminate: ``True`` will allow early termination.
@@ -2861,6 +2862,11 @@ def train(self, verbose = 2, **kwargs):
         else:
             self.visualize_after_epochs = kwargs['visualize_after_epochs']
 
+        if not 'save_after_epochs' in kwargs.keys():
+            self.save_after_epochs = self.validate_after_epochs
+        else:
+            self.save_after_epochs = kwargs['save_after_epochs']
+
         if not 'show_progress' in kwargs.keys():
             show_progress = True
         else:
@@ -2995,6 +3001,7 @@ def train(self, verbose = 2, **kwargs):
                                         verbose = verbose )
                 self.visualize ( epoch = epoch_counter , verbose = verbose )
                 self.print_status ( epoch = epoch_counter, verbose=verbose )
+                self.save_params ( epoch = epoch_counter, verbose = verbose )                
 
                 if best is True:
                     copy_params(source = self.active_params, destination= nan_insurance ,
@@ -3127,5 +3134,20 @@ def get_params (self, verbose = 2):
                 params[lyr] = params_list
         return params
 
+    def save_params (self, epoch = 0, verbose = 2):
+        """
+        This method will save down a list of network parameters
+        
+        Args:
+            verbose: As usual
+            epoch: epoch.
+        """
+
+        from yann.utils.pickle import pickle
+        if not os.path.exists (self.cooked_resultor.root + '/params'):
+            os.makedirs (self.cooked_resultor.root + '/params')    
+
+        filename = self.cooked_resultor.root + '/params/epoch_' + str(epoch) + '.pkl'        
+        pickle(net = self, filename = filename, verbose=verbose)    
 if __name__ == '__main__':
     pass

yann/special/gan.py

@@ -530,6 +530,7 @@ def train ( self, verbose, **kwargs):
             k : how many discriminator updates for every generator update.
             learning_rates: (annealing_rate, learning_rates ... ) length must be one more than
                             ``epochs`` Default is ``(0.05, 0.01, 0.001)``
+            save_after_epochs: 1, Save network after that many epochs of training.
             pre_train_discriminator: If you want to pre-train the discriminator to make it stay
                                     ahead of the generator for making predictions. This will only
                                     train the softmax layer loss and not the fake or real loss.
@@ -564,6 +565,11 @@ def train ( self, verbose, **kwargs):
         else:
             self.visualize_after_epochs = kwargs['visualize_after_epochs']
 
+        if not 'save_after_epochs' in kwargs.keys():
+            self.save_after_epochs = self.validate_after_epochs
+        else:
+            self.save_after_epochs = kwargs['save_after_epochs']
+
         if not 'show_progress' in kwargs.keys():
             show_progress = True
         else:
@@ -606,7 +612,7 @@ def train ( self, verbose, **kwargs):
 
         if self.softmax_head is True:
             self.softmax_learning_rate.set_value(learning_rates[1])
-
+            
         patience_increase = 2
         improvement_threshold = 0.995
         best_iteration = 0
@@ -849,6 +855,7 @@ def train ( self, verbose, **kwargs):
                                         training_accuracy = training_accuracy,
                                         show_progress = show_progress,
                                         verbose = verbose )
+                self.save_params ( epoch = epoch_counter, verbose = verbose )
                 self.visualize ( epoch = epoch_counter , verbose = verbose)
 
                 if best is True:

yann/utils/pickle.py

@@ -2,13 +2,13 @@
 import theano
 from collections import OrderedDict
 
-def pickle(net, name, verbose = 2):
+def pickle(net, filename, verbose = 2):
     """
     This method saves the weights of all the layers.
 
     Args:
         net: A yann network object
-        name: What is the name of the file to pickle the network as.
+        filename: What is the name of the file to pickle the network as.
         verbose: Blah..
     """
     if verbose >= 3:
@@ -18,7 +18,7 @@ def pickle(net, name, verbose = 2):
     if verbose >= 3:
         print "... Dumping netowrk parameters"
 
-    f = open(name, 'wb')                   
+    f = open(filename, 'wb')                   
     cPickle.dump(params, f, protocol = cPickle.HIGHEST_PROTOCOL)
     f.close()