BVhr  dZddlZddZddZy)zNumpy-related utilities.Nctj|d}|j}|r$|ddk(rt|dkDrt |dd}|j }|stj |dz}|jd}tj||f|}d|tj||f<||fz}tj||}|S)aConverts a class vector (integers) to binary class matrix. E.g. for use with categorical_crossentropy. Args: y: class vector to be converted into a matrix (integers from 0 to num_classes). num_classes: total number of classes. If `None`, this would be inferred as the (largest number in `y`) + 1. dtype: The data type expected by the input. Default: `'float32'`. Returns: A binary matrix representation of the input. The classes axis is placed last. Example: >>> a = tf.keras.utils.to_categorical([0, 1, 2, 3], num_classes=4) >>> a = tf.constant(a, shape=[4, 4]) >>> print(a) tf.Tensor( [[1. 0. 0. 0.] [0. 1. 0. 0.] [0. 0. 1. 0.] [0. 0. 0. 1.]], shape=(4, 4), dtype=float32) >>> b = tf.constant([.9, .04, .03, .03, ... .3, .45, .15, .13, ... .04, .01, .94, .05, ... .12, .21, .5, .17], ... shape=[4, 4]) >>> loss = tf.keras.backend.categorical_crossentropy(a, b) >>> print(np.around(loss, 5)) [0.10536 0.82807 0.1011 1.77196] >>> loss = tf.keras.backend.categorical_crossentropy(a, a) >>> print(np.around(loss, 5)) [0. 0. 0. 0.] Raises: Value Error: If input contains string value int)dtypeNr) nparrayshapelentupleravelmaxzerosarangereshape)y num_classesr input_shapen categorical output_shapes V/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/keras/utils/np_utils.pyto_categoricalrsX hhq!+[_)c+.>.B CR()Kggi! &&)a-Kggaj!![)7+!"+biilAo ~-, ; 5+ ctjtjj|||}d||dk(<|tj||z S)zNormalizes a Numpy array. Args: x: Numpy array to normalize. axis: axis along which to normalize. order: Normalization order (e.g. `order=2` for L2 norm). Returns: A normalized copy of the array. rr)r atleast_1dlinalgnorm expand_dims)xaxisorderl2s r normalizer$OsF }}RYY^^Aud34""R1W+ R^^B % %%r)Nfloat32)r)__doc__numpyrrr$rrr*s8v &r