ecfb7febc19941314261a644ec37787ffbf6d4ad,snntoolbox/models/cifar_cnn_larger_pool.py,,,#,17

Before Change

model.compile(loss="categorical_crossentropy", optimizer=sgd,
              metrics=["accuracy"])

if not data_augmentation:
    print("Not using data augmentation or normalization")
//    early_stopping = EarlyStopping(monitor="val_loss", patience=3)
    history = model.fit(X_train, Y_train, batch_size=batch_size,
                        validation_data=(X_test, Y_test), nb_epoch=nb_epoch)
    plot_history(history)

else:
    print("Using real time data augmentation")

    // this will do preprocessing and realtime data augmentation
    datagen = ImageDataGenerator(
        featurewise_center=False,  // set input mean to 0 over the dataset
        samplewise_center=False,  // set each sample mean to 0
        // divide inputs by std of the dataset
        featurewise_std_normalization=False,
        samplewise_std_normalization=False,  // divide each input by its std
        zca_whitening=False,  // apply ZCA whitening
        // randomly rotate images in the range (degrees, 0 to 180)
        rotation_range=0,
        // randomly shift images horizontally (fraction of total width)
        width_shift_range=0.1,
        // randomly shift images vertically (fraction of total height)
        height_shift_range=0.1,
        horizontal_flip=True,  // randomly flip images
        vertical_flip=False)  // randomly flip images

    // compute quantities required for featurewise normalization
    // (std, mean, and principal components if ZCA whitening is applied)
    datagen.fit(X_train)
    log = np.empty([nb_epoch, 4])

    // fit the model on the batches generated by datagen.flow()
    model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size),
                        samples_per_epoch=X_train.shape[0], nb_epoch=nb_epoch,
                        validation_data=(X_test, Y_test))

score = model.evaluate(X_test, Y_test, batch_size=batch_size)
print("Test score:", score[0])
print("Test accuracy:", score[1])

After Change

model.compile(loss="categorical_crossentropy", optimizer="adadelta",
              metrics=["accuracy"])
early_stopping = EarlyStopping(monitor="val_loss", patience=3)
history = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
                    verbose=1, validation_data=(X_test, Y_test))
//                    callbacks=[early_stopping])
score = model.evaluate(X_test, Y_test, verbose=0)

Instances