def preprocess_data(X,y,nb_classes=10, binarize=False, noise_prop=0.49):
X_new = X.reshape(-1, 784)
X_new = X_new.astype('float32')
print(X_new.shape[0], 'samples')
if binarize:
X_new = binaryze_dataset(X_new, threshold=0.5)
if noise_prop != None:
print("Adding salt and pepper ({})".format(noise_prop))
X_new = add_salt_and_pepper(X_new,proportion=noise_prop)
if nb_classes == 2:
Y = numpy.in1d(y,[0,2,4,6,8]).astype('float64')
else:
Y = y
print(Y.shape)
return X_new,Y
def preprocess_data(X,y,nb_classes=10, binarize=False, noise=False,
proportion=0.1):
X_new = X.reshape(-1, 784)
X_new = X_new.astype('float32')
X_new /= 255.0
print(X_new.shape[0], 'samples')
if binarize:
X_new = binaryze_dataset(X_new, threshold=0.5)
if noise:
X_new = add_salt_and_pepper(X_new,proportion=proportion)
if nb_classes == 2:
Y = np.in1d(y,[0,2,4,6,8]).astype('float64')
else:
Y = np_utils.to_categorical(y,nb_classes)
return X_new,Y
