a SicF @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: Array-like with class values to be converted into a matrix (integers from 0 to `num_classes - 1`). num_classes: Total number of classes. If `None`, this would be inferred as `max(y) + 1`. dtype: The data type expected by the input. Default: `'float32'`. Returns: A binary matrix representation of the input. The class 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.] int)dtypeNr) nparrayshapelentupleravelmaxzerosarangereshape)y num_classesr input_shapen categorical output_shaperP/var/www/html/django/DPS/env/lib/python3.9/site-packages/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)r atleast_1dlinalgnorm expand_dims)xaxisorderl2rrr normalizeOs  r$)Nr)rr)__doc__numpyr tensorflow.python.util.tf_exportrrr$rrrrs   7