diff options
| author |  Derek Murray <mrry@google.com> | 2018-01-15 15:43:28 -0800 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-01-15 15:47:21 -0800 |
| commit | a976d3c0843932e6226c7571e94f542b1b7d2dc6 (patch) | |
| tree | 2edb22391d7d1bb219e49ee9249b2882d410f2e5 /tensorflow/contrib/kfac | |
| parent | bdb1a5ee6ca8d343a7a03614fb8fbfb653e848cc (diff) | |
Migrate `tf.contrib.data` users to the stable `tf.data` API.
PiperOrigin-RevId: 181993953
Diffstat (limited to 'tensorflow/contrib/kfac')
| -rw-r--r-- | tensorflow/contrib/kfac/examples/mnist.py | 2 | ||||
| -rw-r--r-- | tensorflow/contrib/kfac/examples/tests/convnet_test.py | 2 |
2 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/contrib/kfac/examples/mnist.py b/tensorflow/contrib/kfac/examples/mnist.py index cf92c909f4..547c4ab25d 100644 --- a/tensorflow/contrib/kfac/examples/mnist.py +++ b/tensorflow/contrib/kfac/examples/mnist.py @@ -63,7 +63,7 @@ def load_mnist(data_dir, images = mnist_data.train.images labels = mnist_data.train.labels - dataset = tf.contrib.data.Dataset.from_tensor_slices((np.asarray( + dataset = tf.data.Dataset.from_tensor_slices((np.asarray( images, dtype=np.float32), np.asarray(labels, dtype=np.int64))) return (dataset.repeat(num_epochs).shuffle(num_examples).batch(batch_size) .make_one_shot_iterator().get_next()) diff --git a/tensorflow/contrib/kfac/examples/tests/convnet_test.py b/tensorflow/contrib/kfac/examples/tests/convnet_test.py index 3c98c54ef6..8d86c2bb51 100644 --- a/tensorflow/contrib/kfac/examples/tests/convnet_test.py +++ b/tensorflow/contrib/kfac/examples/tests/convnet_test.py @@ -96,7 +96,7 @@ class ConvNetTest(tf.test.TestCase): """ x = np.asarray([[1.], [2.]]).astype(np.float32) y = np.asarray([1., 2.]).astype(np.float32) - x, y = (tf.contrib.data.Dataset.from_tensor_slices((x, y)) + x, y = (tf.data.Dataset.from_tensor_slices((x, y)) .repeat(100).batch(2).make_one_shot_iterator().get_next()) w = tf.get_variable("w", shape=[1, 1], initializer=tf.zeros_initializer()) y_hat = tf.matmul(x, w) |