0hjDdZddlmcmZddlmZddlmZ ddl m Z ddl m Z ddlmZedGd d e j Zed Gd d e j ZedGdde j ZedGdde j ZdZeej,_edGdde j ZedGdde j Zeej,_dZedej4j6j8d&dZedej4j6j8dZed ej4j6j8d!Zed"ej4j6j8d'd#Z ed$ej4j6j8d'd%Z!y)(zAccuracy metrics.N)backend)utils) base_metric) metrics_utils) keras_exportzkeras.metrics.AccuracycBeZdZdZej dfd ZxZS)Accuracya9Calculates how often predictions equal labels. This metric creates two local variables, `total` and `count` that are used to compute the frequency with which `y_pred` matches `y_true`. This frequency is ultimately returned as `binary accuracy`: an idempotent operation that simply divides `total` by `count`. If `sample_weight` is `None`, weights default to 1. Use `sample_weight` of 0 to mask values. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Standalone usage: >>> m = tf.keras.metrics.Accuracy() >>> m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]]) >>> m.result().numpy() 0.75 >>> m.reset_state() >>> m.update_state([[1], [2], [3], [4]], [[0], [2], [3], [4]], ... sample_weight=[1, 1, 0, 0]) >>> m.result().numpy() 0.5 Usage with `compile()` API: ```python model.compile(optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.Accuracy()]) ``` c2t|t||yNdtype)super__init__accuracyselfnamer __class__s _/var/www/html/engine/venv/lib/python3.12/site-packages/tf_keras/src/metrics/accuracy_metrics.pyrzAccuracy.__init__Bs 4u5)rN__name__ __module__ __qualname____doc__ dtensor_utils inject_meshr __classcell__rs@rr r s#"H66rr zkeras.metrics.BinaryAccuracycBeZdZdZej dfd ZxZS)BinaryAccuracyaCalculates how often predictions match binary labels. This metric creates two local variables, `total` and `count` that are used to compute the frequency with which `y_pred` matches `y_true`. This frequency is ultimately returned as `binary accuracy`: an idempotent operation that simply divides `total` by `count`. If `sample_weight` is `None`, weights default to 1. Use `sample_weight` of 0 to mask values. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. threshold: (Optional) Float representing the threshold for deciding whether prediction values are 1 or 0. Standalone usage: >>> m = tf.keras.metrics.BinaryAccuracy() >>> m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]]) >>> m.result().numpy() 0.75 >>> m.reset_state() >>> m.update_state([[1], [1], [0], [0]], [[0.98], [1], [0], [0.6]], ... sample_weight=[1, 0, 0, 1]) >>> m.result().numpy() 0.5 Usage with `compile()` API: ```python model.compile(optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.BinaryAccuracy()]) ``` cHt|tj|||y)N)r threshold)rrrbinary_matches)rrr r#rs rrzBinaryAccuracy.__init__os$   ( ($ey  r)binary_accuracyN?rrs@rr!r!Gs#$L  rr!z!keras.metrics.CategoricalAccuracycBeZdZdZej dfd ZxZS)CategoricalAccuracyaCalculates how often predictions match one-hot labels. You can provide logits of classes as `y_pred`, since argmax of logits and probabilities are same. This metric creates two local variables, `total` and `count` that are used to compute the frequency with which `y_pred` matches `y_true`. This frequency is ultimately returned as `categorical accuracy`: an idempotent operation that simply divides `total` by `count`. `y_pred` and `y_true` should be passed in as vectors of probabilities, rather than as labels. If necessary, use `tf.one_hot` to expand `y_true` as a vector. If `sample_weight` is `None`, weights default to 1. Use `sample_weight` of 0 to mask values. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Standalone usage: >>> m = tf.keras.metrics.CategoricalAccuracy() >>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8], ... [0.05, 0.95, 0]]) >>> m.result().numpy() 0.5 >>> m.reset_state() >>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8], ... [0.05, 0.95, 0]], ... sample_weight=[0.7, 0.3]) >>> m.result().numpy() 0.3 Usage with `compile()` API: ```python model.compile( optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.CategoricalAccuracy()]) ``` c,t|d||y)Ncltjtjj |d|SNaxisrsparse_categorical_matchestfmathargmaxy_truey_preds rz.CategoricalAccuracy.__init__..s'=#K#KvB/$rr rrrs rrzCategoricalAccuracy.__init__s"     r)categorical_accuracyNrrs@rr(r(vs#,\  rr(z'keras.metrics.SparseCategoricalAccuracycBeZdZdZej dfd ZxZS)SparseCategoricalAccuracya7Calculates how often predictions match integer labels. ```python acc = np.dot(sample_weight, np.equal(y_true, np.argmax(y_pred, axis=1)) ``` You can provide logits of classes as `y_pred`, since argmax of logits and probabilities are same. This metric creates two local variables, `total` and `count` that are used to compute the frequency with which `y_pred` matches `y_true`. This frequency is ultimately returned as `sparse categorical accuracy`: an idempotent operation that simply divides `total` by `count`. If `sample_weight` is `None`, weights default to 1. Use `sample_weight` of 0 to mask values. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Standalone usage: >>> m = tf.keras.metrics.SparseCategoricalAccuracy() >>> m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]]) >>> m.result().numpy() 0.5 >>> m.reset_state() >>> m.update_state([[2], [1]], [[0.1, 0.6, 0.3], [0.05, 0.95, 0]], ... sample_weight=[0.7, 0.3]) >>> m.result().numpy() 0.3 Usage with `compile()` API: ```python model.compile( optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.SparseCategoricalAccuracy()]) ``` cFt|tj||yr )rrrr0rs rrz"SparseCategoricalAccuracy.__init__s"   4 4d%  r)sparse_categorical_accuracyNrrs@rr;r;s#*X  rr;aAccumulates metric statistics. For sparse categorical metrics, the shapes of `y_true` and `y_pred` are different. Args: y_true: Ground truth label values. shape = `[batch_size, d0, .. dN-1]` or shape = `[batch_size, d0, .. dN-1, 1]`. y_pred: The predicted probability values. shape = `[batch_size, d0, .. dN]`. sample_weight: Optional `sample_weight` acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the metric for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be broadcasted to this shape), then each metric element of `y_pred` is scaled by the corresponding value of `sample_weight`. (Note on `dN-1`: all metric functions reduce by 1 dimension, usually the last axis (-1)). Returns: Update op. z%keras.metrics.TopKCategoricalAccuracycBeZdZdZej dfd ZxZS)TopKCategoricalAccuracyaComputes how often targets are in the top `K` predictions. Args: k: (Optional) Number of top elements to look at for computing accuracy. Defaults to `5`. name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Standalone usage: >>> m = tf.keras.metrics.TopKCategoricalAccuracy(k=1) >>> m.update_state([[0, 0, 1], [0, 1, 0]], ... [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]) >>> m.result().numpy() 0.5 >>> m.reset_state() >>> m.update_state([[0, 0, 1], [0, 1, 0]], ... [[0.1, 0.9, 0.8], [0.05, 0.95, 0]], ... sample_weight=[0.7, 0.3]) >>> m.result().numpy() 0.3 Usage with `compile()` API: ```python model.compile(optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.TopKCategoricalAccuracy()]) ``` c.t|d|||y)Ncntjtjj |d||Sr+r sparse_top_k_categorical_matchesr1r2r3)ytypks rr7z2TopKCategoricalAccuracy.__init__..'s)mLLr+Rrr rFr8rrFrr rs rrz TopKCategoricalAccuracy.__init__$s%     r)top_k_categorical_accuracyNrrs@rr?r?s#@  rr?z+keras.metrics.SparseTopKCategoricalAccuracycDeZdZdZej dfd ZxZS)SparseTopKCategoricalAccuracyaPComputes how often integer targets are in the top `K` predictions. Args: k: (Optional) Number of top elements to look at for computing accuracy. Defaults to `5`. name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Standalone usage: >>> m = tf.keras.metrics.SparseTopKCategoricalAccuracy(k=1) >>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]) >>> m.result().numpy() 0.5 >>> m.reset_state() >>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]], ... sample_weight=[0.7, 0.3]) >>> m.result().numpy() 0.3 Usage with `compile()` API: ```python model.compile( optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.SparseTopKCategoricalAccuracy()]) ``` cHt|tj|||y)NrG)rrrrCrHs rrz&SparseTopKCategoricalAccuracy.__init__Qs)   : :   r)rI!sparse_top_k_categorical_accuracyNrrs@rrLrL0s%>CG  rrLcptj||g\\}}}|jj|j|j|jk7r t j ||j}t j t j||tjS)N) r,ragged_assert_compatible_and_get_flat_valuesshapeassert_is_compatible_withr r1castequalrfloatx)r5r6_s rrrbs  E E    LL**6<<8 ||v||#. 77288FF+W^^-= >>rzkeras.metrics.binary_accuracycZtjtj|||dS)aCalculates how often predictions match binary labels. Standalone usage: >>> y_true = [[1], [1], [0], [0]] >>> y_pred = [[1], [1], [0], [0]] >>> m = tf.keras.metrics.binary_accuracy(y_true, y_pred) >>> assert m.shape == (4,) >>> m.numpy() array([1., 1., 1., 1.], dtype=float32) Args: y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`. y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`. threshold: (Optional) Float representing the threshold for deciding whether prediction values are 1 or 0. Returns: Binary accuracy values. shape = `[batch_size, d0, .. dN-1]` r,r-)r1 reduce_meanrr$)r5r6r#s rr%r%os)6 >>$$VVY?b rz"keras.metrics.categorical_accuracycltjtjj |d|S)a;Calculates how often predictions match one-hot labels. Standalone usage: >>> y_true = [[0, 0, 1], [0, 1, 0]] >>> y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]] >>> m = tf.keras.metrics.categorical_accuracy(y_true, y_pred) >>> assert m.shape == (2,) >>> m.numpy() array([0., 1.], dtype=float32) You can provide logits of classes as `y_pred`, since argmax of logits and probabilities are same. Args: y_true: One-hot ground truth values. y_pred: The prediction values. Returns: Categorical accuracy values. r,r-r/r4s rr9r9s-6  3 3 vB' rz)keras.metrics.sparse_categorical_accuracyctj||}|jjdkDr)|jddk(rt j |dg}|S)a9Calculates how often predictions match integer labels. Standalone usage: >>> y_true = [2, 1] >>> y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]] >>> m = tf.keras.metrics.sparse_categorical_accuracy(y_true, y_pred) >>> assert m.shape == (2,) >>> m.numpy() array([0., 1.], dtype=float32) You can provide logits of classes as `y_pred`, since argmax of logits and probabilities are same. Args: y_true: Integer ground truth values. y_pred: The prediction values. Returns: Sparse categorical accuracy values. r,)rr0rQndimsr1squeeze)r5r6matchess rr=r=sQ866vvFG}}Q7==#4#9**Wrd+ Nrz(keras.metrics.top_k_categorical_accuracycntjtjj |d||S)aGComputes how often targets are in the top `K` predictions. Standalone usage: >>> y_true = [[0, 0, 1], [0, 1, 0]] >>> y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]] >>> m = tf.keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=3) >>> assert m.shape == (2,) >>> m.numpy() array([1., 1.], dtype=float32) Args: y_true: The ground truth values. y_pred: The prediction values. k: (Optional) Number of top elements to look at for computing accuracy. Defaults to `5`. Returns: Top K categorical accuracy value. r,r-rBr5r6rFs rrJrJs/4  9 9 vB' rz/keras.metrics.sparse_top_k_categorical_accuracyc0tj|||S)a_Computes how often integer targets are in the top `K` predictions. Standalone usage: >>> y_true = [2, 1] >>> y_pred = [[0.1, 0.9, 0.8], [0.05, 0.95, 0]] >>> m = tf.keras.metrics.sparse_top_k_categorical_accuracy( ... y_true, y_pred, k=3) >>> assert m.shape == (2,) >>> m.numpy() array([1., 1.], dtype=float32) Args: y_true: tensor of true targets. y_pred: tensor of predicted targets. k: (Optional) Number of top elements to look at for computing accuracy. Defaults to `5`. Returns: Sparse top K categorical accuracy value. )rrCr`s rrNrNs8  9 9&&! LLr)r&)rI)"rtensorflow.compat.v2compatv2r1 tf_keras.srcrtf_keras.src.dtensorrrtf_keras.src.metricsrtf_keras.src.utilsr tensorflow.python.util.tf_exportrMeanMetricWrapperr r!r(r;*_SPARSE_CATEGORICAL_UPDATE_STATE_DOCSTRING update_stater?rLr __internal__dispatchadd_dispatch_supportr%r9r=rJrNrrrqsB!! 7,,:&''6{,,'6('6T,-+ [22+ .+ \127 +777 37 t781 = =1 91 h.*0/&&. 56* k;;* 7* Z;<) K$A$A) =) Z/**2 ?-...//<23../4<9:.. /; F89../::?@..M/AMr