height_shift_range=5. / 32) generator.fit(trainX, seed=0) weights_file = 'DenseNet-40-12-CIFAR-10.h5' lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), cooldown=0, patience=10, min_lr=0.5e-6) early_stopper = EarlyStopping(monitor='val_acc', min_delta=1e-4, patience=20) model_checkpoint = ModelCheckpoint(weights_file, monitor='val_acc', save_best_only=True, save_weights_only=True, mode='auto') callbacks = [lr_reducer, early_stopper, model_checkpoint] model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size), steps_per_epoch=len(trainX) // batch_size, epochs=epochs, callbacks=callbacks, validation_data=(testX, Y_test), verbose=2) scores = model.evaluate(testX, Y_test, batch_size=batch_size) print('Test loss : ', scores[0]) print('Test accuracy : ', scores[1])
generator.fit(trainX, seed=0) weights_file = "DenseNet-40-12-CIFAR-10.h5" lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=np.sqrt(0.1), cooldown=0, patience=10, min_lr=0.5e-6) early_stopper = EarlyStopping(monitor='val_acc', min_delta=1e-4, patience=20) model_checkpoint = ModelCheckpoint(weights_file, monitor="val_acc", save_best_only=True, save_weights_only=True, mode='auto') callbacks = [lr_reducer, early_stopper, model_checkpoint] model.fit_generator(generator.flow(trainX, Y_train, batch_size=batch_size), samples_per_epoch=len(trainX), nb_epoch=nb_epoch, callbacks=callbacks, validation_data=(testX, Y_test), nb_val_samples=testX.shape[0], verbose=2) scores = model.evaluate(testX, Y_test, batch_size=batch_size) print("Test loss : ", scores[0]) print("Test accuracy : ", scores[1])