o 7?eO/@s dZddlmZddlmZddlmmZddlm Z ddl m Z ddl m Z ddl mZdd l mZdd l mZdd lmZdd lmZed GdddejZedGdddejZedGdddejZedGdddejZedGdddejZdZeej_dS)z)Probabilistic metrics (based on Entropy).)Optional)UnionN)utils)binary_crossentropy)categorical_crossentropy)kullback_leibler_divergence)poisson)sparse_categorical_crossentropy) base_metric) keras_exportzkeras.metrics.Poissonc(eZdZdZejdfdd ZZS)Poissonu-Computes the Poisson score between `y_true` and `y_pred`. 🐟 🐟 🐟 It is defined as: `poisson_score = y_pred - y_true * log(y_pred)`. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Standalone usage: >>> m = tf.keras.metrics.Poisson() >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]]) >>> m.result().numpy() 0.49999997 >>> m.reset_state() >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]], ... sample_weight=[1, 0]) >>> m.result().numpy() 0.99999994 Usage with `compile()` API: ```python model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=[tf.keras.metrics.Poisson()]) ``` rNctjt||ddSN)dtype)super__init__rselfnamer __class__h/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/keras/src/metrics/probabilistic_metrics.pyrDzPoisson.__init__)rN__name__ __module__ __qualname____doc__ dtensor_utils inject_meshr __classcell__rrrrr "s r zkeras.metrics.KLDivergencecr ) KLDivergencea-Computes Kullback-Leibler divergence metric between `y_true` and `y_pred`. `metric = y_true * log(y_true / y_pred)` Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Standalone usage: >>> m = tf.keras.metrics.KLDivergence() >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]]) >>> m.result().numpy() 0.45814306 >>> m.reset_state() >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]], ... sample_weight=[1, 0]) >>> m.result().numpy() 0.9162892 Usage with `compile()` API: ```python model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=[tf.keras.metrics.KLDivergence()]) ``` rNcrr)rrrrrrrrjrzKLDivergence.__init__)rNrrrrrr#Isr#z keras.metrics.BinaryCrossentropycs0eZdZdZej    dfdd ZZS) BinaryCrossentropya Computes the crossentropy metric between the labels and predictions. This is the crossentropy metric class to be used when there are only two label classes (0 and 1). Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. from_logits: (Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution. label_smoothing: (Optional) Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. e.g. `label_smoothing=0.2` means that we will use a value of `0.1` for label `0` and `0.9` for label `1`". Standalone usage: >>> m = tf.keras.metrics.BinaryCrossentropy() >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]]) >>> m.result().numpy() 0.81492424 >>> m.reset_state() >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]], ... sample_weight=[1, 0]) >>> m.result().numpy() 0.9162905 Usage with `compile()` API: ```python model.compile( optimizer='sgd', loss='binary_crossentropy', metrics=[tf.keras.metrics.BinaryCrossentropy()]) ``` rNFrcstjt||||ddS)N)r from_logitslabel_smoothing)rrr)rrrr%r&rrrrs zBinaryCrossentropy.__init__)rNFrrrrrrr$os&r$z%keras.metrics.CategoricalCrossentropycs2eZdZdZej     d fdd ZZS) CategoricalCrossentropyaComputes the crossentropy metric between the labels and predictions. This is the crossentropy metric class to be used when there are multiple label classes (2 or more). Here we assume that labels are given as a `one_hot` representation. eg., When labels values are [2, 0, 1], `y_true` = [[0, 0, 1], [1, 0, 0], [0, 1, 0]]. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. from_logits: (Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution. label_smoothing: (Optional) Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. e.g. `label_smoothing=0.2` means that we will use a value of `0.1` for label `0` and `0.9` for label `1`" axis: (Optional) -1 is the dimension along which entropy is computed. Defaults to `-1`. Standalone usage: >>> # EPSILON = 1e-7, y = y_true, y` = y_pred >>> # y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON) >>> # y` = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]] >>> # xent = -sum(y * log(y'), axis = -1) >>> # = -((log 0.95), (log 0.1)) >>> # = [0.051, 2.302] >>> # Reduced xent = (0.051 + 2.302) / 2 >>> m = tf.keras.metrics.CategoricalCrossentropy() >>> m.update_state([[0, 1, 0], [0, 0, 1]], ... [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]) >>> m.result().numpy() 1.1769392 >>> m.reset_state() >>> m.update_state([[0, 1, 0], [0, 0, 1]], ... [[0.05, 0.95, 0], [0.1, 0.8, 0.1]], ... sample_weight=tf.constant([0.3, 0.7])) >>> m.result().numpy() 1.6271976 Usage with `compile()` API: ```python model.compile( optimizer='sgd', loss='categorical_crossentropy', metrics=[tf.keras.metrics.CategoricalCrossentropy()]) ``` rNFrctjt|||||ddS)N)rr%r&axis)rrr)rrrr%r&r*rrrr  z CategoricalCrossentropy.__init__)rNFrr(rrrrrr's3r'z+keras.metrics.SparseCategoricalCrossentropyc s\eZdZdZej     d dedeeee j j fde d ee d e f fd d ZZS)SparseCategoricalCrossentropya Computes the crossentropy metric between the labels and predictions. Use this crossentropy metric when there are two or more label classes. We expect labels to be provided as integers. If you want to provide labels using `one-hot` representation, please use `CategoricalCrossentropy` metric. There should be `# classes` floating point values per feature for `y_pred` and a single floating point value per feature for `y_true`. In the snippet below, there is a single floating point value per example for `y_true` and `# classes` floating pointing values per example for `y_pred`. The shape of `y_true` is `[batch_size]` and the shape of `y_pred` is `[batch_size, num_classes]`. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. from_logits: (Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution. ignore_class: Optional integer. The ID of a class to be ignored during metric computation. This is useful, for example, in segmentation problems featuring a "void" class (commonly -1 or 255) in segmentation maps. By default (`ignore_class=None`), all classes are considered. axis: (Optional) The dimension along which entropy is computed. Defaults to `-1`. Standalone usage: >>> # y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]] >>> # logits = log(y_pred) >>> # softmax = exp(logits) / sum(exp(logits), axis=-1) >>> # softmax = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]] >>> # xent = -sum(y * log(softmax), 1) >>> # log(softmax) = [[-2.9957, -0.0513, -16.1181], >>> # [-2.3026, -0.2231, -2.3026]] >>> # y_true * log(softmax) = [[0, -0.0513, 0], [0, 0, -2.3026]] >>> # xent = [0.0513, 2.3026] >>> # Reduced xent = (0.0513 + 2.3026) / 2 >>> m = tf.keras.metrics.SparseCategoricalCrossentropy() >>> m.update_state([1, 2], ... [[0.05, 0.95, 0], [0.1, 0.8, 0.1]]) >>> m.result().numpy() 1.1769392 >>> m.reset_state() >>> m.update_state([1, 2], ... [[0.05, 0.95, 0], [0.1, 0.8, 0.1]], ... sample_weight=tf.constant([0.3, 0.7])) >>> m.result().numpy() 1.6271976 Usage with `compile()` API: ```python model.compile( optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=[tf.keras.metrics.SparseCategoricalCrossentropy()]) ``` r NFr(rrr% ignore_classr*cr))N)rr%r-r*)rrr )rrrr%r-r*rrrr.r+z&SparseCategoricalCrossentropy.__init__)r NFNr()rrrrr r!strrrtfZdtypesZDTypeboolintrr"rrrrr,s&<r,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. )rtypingrrZtensorflow.compat.v2compatv2r/Zkeras.src.dtensorrr Zkeras.src.lossesrrrrr Zkeras.src.metricsr Z tensorflow.python.util.tf_exportr ZMeanMetricWrapperr r#r$r'r,Z*_SPARSE_CATEGORICAL_UPDATE_STATE_DOCSTRINGZ update_staterrrrs2          &%8GP