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E.g. for use with categorical_crossentropy. |
Arguments |
y: class vector to be converted into a matrix (integers from 0 to num_classes). |
num_classes: total number of classes. If None, this would be inferred as the (largest number in y) + 1. |
dtype: The data type expected by the input. Default: 'float32'. |
Returns |
A binary matrix representation of the input. The classes axis is placed last. |
Example |
>>> a = tf.keras.utils.to_categorical([0, 1, 2, 3], num_classes=4) |
>>> a = tf.constant(a, shape=[4, 4]) |
>>> print(a) |
tf.Tensor( |
[[1. 0. 0. 0.] |
[0. 1. 0. 0.] |
[0. 0. 1. 0.] |
[0. 0. 0. 1.]], shape=(4, 4), dtype=float32) |
>>> b = tf.constant([.9, .04, .03, .03, |
... .3, .45, .15, .13, |
... .04, .01, .94, .05, |
... .12, .21, .5, .17], |
... shape=[4, 4]) |
>>> loss = tf.keras.backend.categorical_crossentropy(a, b) |
>>> print(np.around(loss, 5)) |
[0.10536 0.82807 0.1011 1.77196] |
>>> loss = tf.keras.backend.categorical_crossentropy(a, a) |
>>> print(np.around(loss, 5)) |
[0. 0. 0. 0.] |
Raises |
Value Error: If input contains string value |
normalize function |
tf.keras.utils.normalize(x, axis=-1, order=2) |
Normalizes a Numpy array. |
Arguments |
x: Numpy array to normalize. |
axis: axis along which to normalize. |
order: Normalization order (e.g. order=2 for L2 norm). |
Returns |
A normalized copy of the array. |
get_file function |
tf.keras.utils.get_file( |
fname, |
origin, |
untar=False, |
md5_hash=None, |
file_hash=None, |
cache_subdir="datasets", |
hash_algorithm="auto", |
extract=False, |
archive_format="auto", |
cache_dir=None, |
) |
Downloads a file from a URL if it not already in the cache. |
By default the file at the url origin is downloaded to the cache_dir ~/.keras, placed in the cache_subdir datasets, and given the filename fname. The final location of a file example.txt would therefore be ~/.keras/datasets/example.txt. |
Files in tar, tar.gz, tar.bz, and zip formats can also be extracted. Passing a hash will verify the file after download. The command line programs shasum and sha256sum can compute the hash. |
Example |
path_to_downloaded_file = tf.keras.utils.get_file( |
"flower_photos", |
"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz", |
untar=True) |
Arguments |
fname: Name of the file. If an absolute path /path/to/file.txt is specified the file will be saved at that location. |
origin: Original URL of the file. |
untar: Deprecated in favor of extract argument. boolean, whether the file should be decompressed |
md5_hash: Deprecated in favor of file_hash argument. md5 hash of the file for verification |
file_hash: The expected hash string of the file after download. The sha256 and md5 hash algorithms are both supported. |
cache_subdir: Subdirectory under the Keras cache dir where the file is saved. If an absolute path /path/to/folder is specified the file will be saved at that location. |
hash_algorithm: Select the hash algorithm to verify the file. options are 'md5', 'sha256', and 'auto'. The default 'auto' detects the hash algorithm in use. |
extract: True tries extracting the file as an Archive, like tar or zip. |
archive_format: Archive format to try for extracting the file. Options are 'auto', 'tar', 'zip', and None. 'tar' includes tar, tar.gz, and tar.bz files. The default 'auto' corresponds to ['tar', 'zip']. None or an empty list will return no matches found. |
cache_dir: Location to store cached files, when None it defaults to the default directory ~/.keras/. |
Returns |
Path to the downloaded file |
Progbar class |
tf.keras.utils.Progbar( |
target, width=30, verbose=1, interval=0.05, stateful_metrics=None, unit_name="step" |
) |
Displays a progress bar. |