write_dataset.py

"""Loads the MNIST dataset.

This is a dataset of 60,000 28x28 grayscale images of the 10 digits,
along with a test set of 10,000 images.
More info can be found at the
[MNIST homepage](http://yann.lecun.com/exdb/mnist/).

Args:
  path: path where to cache the dataset locally
    (relative to `~/.keras/datasets`).

Returns:
  Tuple of NumPy arrays: `(x_train, y_train), (x_test, y_test)`.

**x_train**: uint8 NumPy array of grayscale image data with shapes
  `(60000, 28, 28)`, containing the training data. Pixel values range
  from 0 to 255.

**y_train**: uint8 NumPy array of digit labels (integers in range 0-9)
  with shape `(60000,)` for the training data.

**x_test**: uint8 NumPy array of grayscale image data with shapes
  (10000, 28, 28), containing the test data. Pixel values range
  from 0 to 255.

**y_test**: uint8 NumPy array of digit labels (integers in range 0-9)
  with shape `(10000,)` for the test data.

Example:

```python
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
assert x_train.shape == (60000, 28, 28)
assert x_test.shape == (10000, 28, 28)
assert y_train.shape == (60000,)
assert y_test.shape == (10000,)
```

License:
  Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset,
  which is a derivative work from original NIST datasets.
  MNIST dataset is made available under the terms of the
  [Creative Commons Attribution-Share Alike 3.0 license.](
  https://creativecommons.org/licenses/by-sa/3.0/)
"""
import os


def folder_browse(top):
        for root, dirs, files in os.walk(top):
            print(root, "consumes", end=" ")
            print(sum(getsize(join(root, name)) for name in files), end=" ")
            print("bytes in", len(files), "non-directory files")
            if 'settings.xml' in dirs:
                exec "mvn ../IdeaProjects/features/ "