remove Keras dependency for loading MNIST when using TF
@@ -3,20 +3,33 @@ from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
-import keras
-from keras.datasets import mnist
-from keras.utils import np_utils
+import numpy as np
+import sys
import warnings
from . import utils
-def data_mnist():
+def data_mnist(datadir='/tmp/'):
"""
- Preprocess MNIST dataset
+ Load and preprocess MNIST dataset
:return:
"""
+ if 'tensorflow' in sys.modules:
+ from tensorflow.examples.tutorials.mnist import input_data
+ mnist = input_data.read_data_sets(datadir, one_hot=True, reshape=False)
+ X_train = np.vstack((mnist.train.images, mnist.validation.images))
+ Y_train = np.vstack((mnist.train.labels, mnist.validation.labels))
+ X_test = mnist.test.images
+ Y_test = mnist.test.labels
+ else:
+ warnings.warn("cleverhans support for Theano is deprecated and "
+ "will be dropped on 2017-11-08.")
+ import keras
+ from keras.datasets import mnist
+ from keras.utils import np_utils
+
# These values are specific to MNIST
img_rows = 28
img_cols = 28
@@ -28,20 +41,20 @@ def data_mnist():
if keras.backend.image_dim_ordering() == 'th':
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
- else:
- X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
- X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
+
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
- print('X_train shape:', X_train.shape)
- print(X_train.shape[0], 'train samples')
- print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
+
+ print('X_train shape:', X_train.shape)
+ print(X_train.shape[0], 'train samples')
+ print(X_test.shape[0], 'test samples')
+
return X_train, Y_train, X_test, Y_test
