Insert option to keep training the network with different splits

1 files changed, 5 insertions, 1 deletions

diff --git a/lenet.py b/lenet.py
index c1c27b5..663c137 100644
--- a/lenet.py
+++ b/lenet.py
@@ -101,7 +101,7 @@ def plot_history(history, metric = None):
     plt.ylabel('Loss')
     plt.xlabel('Epoch')
 
-def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100, metrics=[categorical_accuracy], optimizer = None):
+def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100, metrics=[categorical_accuracy], optimizer = None, keep_training = False):
   shape = (32, 32, 1)
 
   # Pad data to 32x32 (MNIST is 28x28)
@@ -114,8 +114,12 @@ def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100,
       optimizer = optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
 
   model.compile(loss='categorical_crossentropy', metrics=metrics, optimizer=optimizer)
+
+  if keep_training:
+      model.load_weights('./model_gan.h5', by_name=False)
   
   history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data = (x_val, y_val))
+  model.save_weights('./model_gan.h5')
   plot_history(history, 'categorical_accuracy')
   plot_history(history)
   return model