Pass verbose classifier as a flag

1 files changed, 2 insertions, 2 deletions

diff --git a/lenet.py b/lenet.py
index 71b1c9e..4950fe9 100644
--- a/lenet.py
+++ b/lenet.py
@@ -118,7 +118,7 @@ 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, keep_training = False):
+def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100, metrics=[categorical_accuracy], optimizer = None, keep_training = False, verbose=1):
   shape = (32, 32, 1)
 
   # Pad data to 32x32 (MNIST is 28x28)
@@ -133,7 +133,7 @@ def train_classifier(x_train, y_train, x_val, y_val, batch_size=128, epochs=100,
   model.compile(loss='categorical_crossentropy', metrics=metrics, optimizer=optimizer)
   if keep_training:
     model.load_weights('./weights.h5')
-  history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data = (x_val, y_val))
+  history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=verbose, validation_data = (x_val, y_val))
   model.save_weights('./model_gan.h5')
   plot_history(history, 'categorical_accuracy')
   plot_history(history)