k = np.load(yfile)
return (j, k)
# Load data and normalize
X, y = load_data()
# Change type and Normalize
X = X.astype('float32')
X /= 255
# 1-hot encoding
y = np_utils.to_categorical(y, num_classes)
skf = StratifiedKFold(n_splits=4,random_state=34, shuffle=True)
print('using K-cross validation with %s folds' % skf.get_n_splits(X, y))
# Model
model = Sequential()
def define_model():
print('\n defining model . . .')
gaussian = RandomNormal(mean=0., stddev=0.1)
cons = Constant(value=2.)
model.add(Conv2D(filters=32, kernel_size=(7, 7), padding='same', kernel_initializer=gaussian,
use_bias=True, bias_initializer=cons, bias_constraint=maxnorm(5.),input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True))
model.add(MaxPooling2D(pool_size=(3,3)))
