6bi|SrSSKrSSKJr \"S5S Sj5r\"S5S Sj5r\"S5S S j5rg) zNumpy-related utilities.N) keras_exportzkeras.utils.to_categoricalc[R"USS9nURnU(a&USS:Xa[U5S:a[ USS5nUR S5nU(d[R "U5S-nURSn[R"XA4US9nSU[R"U5U4'X14-n[R "XV5nU$)a;Converts a class vector (integers) to binary class matrix. E.g. for use with `categorical_crossentropy`. Args: y: Array-like with class values to be converted into a matrix (integers from 0 to `num_classes - 1`). num_classes: Total number of classes. If `None`, this would be inferred as `max(y) + 1`. dtype: The data type expected by the input. Default: `'float32'`. Returns: A binary matrix representation of the input as a NumPy array. The class axis is placed last. Example: >>> a = tf.keras.utils.to_categorical([0, 1, 2, 3], num_classes=4) >>> print(a) [[1. 0. 0. 0.] [0. 1. 0. 0.] [0. 0. 1. 0.] [0. 0. 0. 1.]] >>> b = tf.constant([.9, .04, .03, .03, ... .3, .45, .15, .13, ... .04, .01, .94, .05, ... .12, .21, .5, .17], ... shape=[4, 4]) >>> loss = tf.keras.backend.categorical_crossentropy(a, b) >>> print(np.around(loss, 5)) [0.10536 0.82807 0.1011 1.77196] >>> loss = tf.keras.backend.categorical_crossentropy(a, a) >>> print(np.around(loss, 5)) [0. 0. 0. 0.] intdtypeNr) nparrayshapelentuplereshapemaxzerosarange)y num_classesr input_shapen categorical output_shapes U/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/utils/np_utils.pyto_categoricalrsN % A''K{2!+K0@10DK,-  " A ffQi!m   A((A+59K#$K ! a /L**[7K zkeras.utils.to_ordinalcV[R"USS9nURnU(a&USS:Xa[U5S:a[ USS5nUR S5nU(d[R "U5S-nURSn[R"US- 5n[R"[R"US5US/5n[R"XAS- 4US9nSXe[R"US5:'X1S- 4-n[R "Xg5nU$)a'Converts a class vector (integers) to an ordinal regression matrix. This utility encodes class vector to ordinal regression/classification matrix where each sample is indicated by a row and rank of that sample is indicated by number of ones in that row. 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: An ordinal regression matrix representation of the input as a NumPy array. The class axis is placed last. Example: >>> a = tf.keras.utils.to_ordinal([0, 1, 2, 3], num_classes=4) >>> print(a) [[0. 0. 0.] [1. 0. 0.] [1. 1. 0.] [1. 1. 1.]] rrrr Nr) r r r r rrrrtile expand_dimsr)rrrrr range_valuesordinalrs r to_ordinalr!Ps8 % A''K{2!+K0@10DK,-  " A ffQi!m   A99[1_-L772>>,:QFCLhh?+59G45G2>>!R0 01/!33Ljj/G Nrzkeras.utils.normalizec[R"[RRXU55nSX3S:H'U[R"X15- $)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. r r)r atleast_1dlinalgnormr)xaxisorderl2s r normalizer*s@ ryy~~a5 6BBQwK r~~b' ''r)Nfloat32)r)__doc__numpyr tensorflow.python.util.tf_exportrrr!r*rrr1sb:*+5,5p&',(,^%& (' (r