B ӻd @s@dZddlZddlmZedd ddZeddd d ZdS)zNumpy-related utilities.N) keras_exportzkeras.utils.to_categoricalfloat32cCstj|dd}|j}|r@|ddkr@t|dkr@t|dd}|}|sZt|d}|jd}tj||f|d}d|t||f<||f}t ||}|S)aConverts a class vector (integers) to binary class matrix. E.g. for use with categorical_crossentropy. Args: 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 int)dtypeNr) nparrayshapelentupleZravelmaxZzerosZarangeZreshape)yZ num_classesrZ input_shapenZ categoricalZ output_shaperX/var/www/html/venv/lib/python3.7/site-packages/tensorflow/python/keras/utils/np_utils.pyto_categoricals-   rzkeras.utils.normalizercCs2ttj|||}d||dk<|t||S)zNormalizes a Numpy array. Args: 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. rr)rZ atleast_1dZlinalgZnormZ expand_dims)xZaxisorderl2rrr normalizeQs  r)Nr)rr)__doc__numpyrZ tensorflow.python.util.tf_exportrrrrrrrs   ;