~WhdZddlZddlmZedd dZedd dZed dd ZdS)zNumpy-related utilities.N) keras_exportzkeras.utils.to_categoricalfloat32ctj|d}|j}|r6|ddkr*t|dkrt |dd}|d}|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: 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 Z/var/www/html/movieo_spanner_bot/venv/lib/python3.11/site-packages/keras/utils/np_utils.pyto_categoricalrsN %   A'K.{2!++K0@0@10D0DK,--  " A $fQii!m   A(A{+5999K#$K ! a +/L*[,77K zkeras.utils.to_ordinalcRtj|d}|j}|r6|ddkr*t|dkrt |dd}|d}|stj|dz}|jd}tj|dz }tjtj |d|dg}tj ||dz f|}d||tj |dk<||dz fz}tj||}|S)awConverts 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.]] rrr r Nr) r r r rrrrrtile expand_dimsr)rrrrr range_valuesordinalrs r to_ordinalr"Ps%8 %   A'K.{2!++K0@0@10D0DK,--  " A $fQii!m   A9[1_--L72>,::QFCCLh;?+5999G45GL2>!R00 01+/!33Lj,//G Nrzkeras.utils.normalizer ctjtj|||}d||dk<|tj||z S)zNormalizes a Numpy array. Args: x: Numpy array to normalize. axis: axis along which to normalize. order: Normalization order (e.g. `order=2` for L2 norm). Returns: A normalized copy of the array. r r)r atleast_1dlinalgnormr)xaxisorderl2s r normalizer,sI ry~~a55 6 6BBrQwK r~b$'' ''r)Nr)r r#)__doc__numpyr tensorflow.python.util.tf_exportrrr"r,rrr1s:99999*++555,+5p&'',,,(',^%&& ( ( ('& ( ( (r