From 36629817fbdcc9e696e27f371ca2905ba6cb99aa Mon Sep 17 00:00:00 2001 From: nunzip Date: Thu, 7 Mar 2019 01:44:07 +0000 Subject: Pass verbose classifier as a flag --- lenet.py | 4 ++-- 1 file 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) -- cgit v1.2.3