def filt(X_data, type_freq, lim_freq_1): if type_freq == "low": X_data_f = aug_freq_low(X_data, lim_freq_1, 3, "float") if type_freq == "high": X_data_f = aug_freq_high(X_data, lim_freq_1, 3, "float") if type_freq == "none": X_data_f = X_data return (X_data_f)
print("Accuracy on train set: " + str(model.evaluate(X_train, Y_train, verbose=0)[1])) print("Accuracy on test set: " + str(model.evaluate(X_test, Y_test, verbose=0)[1])) model.save("models/SVHN_low_freq_" + str(lim_freq) + ".h5") if (model_type == "high_freq"): lim_freq = int(sys.argv[3]) print("limit frequency: " + str(lim_freq)) from data_augmentation import aug_freq_high X_train = aug_freq_high(X_train, lim_freq, 3, type_r="float") X_test = aug_freq_high(X_test, lim_freq, 3, type_r="float") generator=ImageDataGenerator() model = svhn_model() filepath="svhn_weights_best_" + model_type + "_"+ str(lim_freq) +".hdf5" checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max') def lr_schedule(epoch): lr = 1e-2 if epoch > 15: lr = 0.001 print('Learning rate: ', lr) return lr lr_scheduler = LearningRateScheduler(lr_schedule) callbacks_list = [checkpoint, lr_scheduler]