~WhEDdZddlmcmZddlmZddlmZ ddl m Z ddl m Z ddlmZedGd d e jZed Gd d e jZedGdde jZedGdde jZdZeej_edGdde jZedGdde jZeej_dZedejjjd(dZedejjjd Zed!ejjjd"Zed#ejjjd)d%Z ed&ejjjd)d'Z!dS)*zAccuracy metrics.N)backend)utils) base_metric) metrics_utils) keras_exportzkeras.metrics.Accuracyc>eZdZdZejdfd 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()]) ``` accuracyNcZtt||dSNdtype)super__init__r selfnamer __class__s d/var/www/html/movieo_spanner_bot/venv/lib/python3.11/site-packages/keras/metrics/accuracy_metrics.pyrzAccuracy.__init__Bs( 4u55555)r N__name__ __module__ __qualname____doc__ dtensor_utils inject_meshr __classcell__rs@rr r sX""H6666666666rr zkeras.metrics.BinaryAccuracyc>eZdZdZejdfd 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()]) ``` binary_accuracyN?cfttj|||dS)N)r threshold)rrrbinary_matches)rrrr%rs rrzBinaryAccuracy.__init__os:   ($ey      r)r"Nr#rrs@rr!r!GsX$$L          rr!z!keras.metrics.CategoricalAccuracyc>eZdZdZejdfd ZxZS)CategoricalAccuracyaCalculates how often predictions match one-hot labels. You can provide logits of classes as `y_pred`, since argmax of logits and probabilities are same. This metric creates two local variables, `total` and `count` that are used to compute the frequency with which `y_pred` matches `y_true`. This frequency is ultimately returned as `categorical accuracy`: an idempotent operation that simply divides `total` by `count`. `y_pred` and `y_true` should be passed in as vectors of probabilities, rather than as labels. If necessary, use `tf.one_hot` to expand `y_true` as a vector. If `sample_weight` is `None`, weights default to 1. Use `sample_weight` of 0 to mask values. Args: name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Standalone usage: >>> m = tf.keras.metrics.CategoricalAccuracy() >>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8], ... [0.05, 0.95, 0]]) >>> m.result().numpy() 0.5 >>> m.reset_state() >>> m.update_state([[0, 0, 1], [0, 1, 0]], [[0.1, 0.9, 0.8], ... [0.05, 0.95, 0]], ... sample_weight=[0.7, 0.3]) >>> m.result().numpy() 0.3 Usage with `compile()` API: ```python model.compile( optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.CategoricalAccuracy()]) ``` categorical_accuracyNcRtd||dS)Ncjtjtj|d|SNaxisrsparse_categorical_matchestfmathargmaxy_truey_preds rz.CategoricalAccuracy.__init__..s*=#KvB//$$rr rrrs rrzCategoricalAccuracy.__init__s@          r)r)Nrrs@rr(r(vsX,,\          rr(z'keras.metrics.SparseCategoricalAccuracyc>eZdZdZejdfd 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()]) ``` sparse_categorical_accuracyNcdttj||dSr )rrrr1rs rrz"SparseCategoricalAccuracy.__init__s8   4d%      r)r<Nrrs@rr;r;sX**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.TopKCategoricalAccuracyc>eZdZdZejdfd 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()]) ``` top_k_categorical_accuracyNcTtd|||dS)Ncltjtj|d||Sr,r sparse_top_k_categorical_matchesr2r3r4)ytypks rr8z2TopKCategoricalAccuracy.__init__..'s,mLr++RrrrHr9rrHrrrs rrz TopKCategoricalAccuracy.__init__$sC          r)r@rANrrs@rr?r?sX@          rr?z+keras.metrics.SparseTopKCategoricalAccuracyc@eZdZdZej dfd ZxZS)SparseTopKCategoricalAccuracyaNComputes how often integer targets are in the top `K` predictions. Args: k: (Optional) Number of top elements to look at for computing accuracy. Defaults to 5. name: (Optional) string name of the metric instance. dtype: (Optional) data type of the metric result. Standalone usage: >>> m = tf.keras.metrics.SparseTopKCategoricalAccuracy(k=1) >>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]]) >>> m.result().numpy() 0.5 >>> m.reset_state() >>> m.update_state([2, 1], [[0.1, 0.9, 0.8], [0.05, 0.95, 0]], ... sample_weight=[0.7, 0.3]) >>> m.result().numpy() 0.3 Usage with `compile()` API: ```python model.compile( optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.SparseTopKCategoricalAccuracy()]) ``` r@!sparse_top_k_categorical_accuracyNcfttj|||dS)NrI)rrrrErJs rrz&SparseTopKCategoricalAccuracy.__init__Qs?   :       r)r@rMNrrs@rrLrL0sZ>CG          rrLc>tj||g\\}}}|j|j|j|jkrt j||j}t jt j||tj S)N) r,ragged_assert_compatible_and_get_flat_valuesshapeassert_is_compatible_withrr2castequalrfloatx)r6r7_s rr r bs  E     L**6<888 |v|##.. 728FF++W^-=-= > >>rzkeras.metrics.binary_accuracyr#cVtjtj|||dS)aCalculates how often predictions match binary labels. Standalone usage: >>> y_true = [[1], [1], [0], [0]] >>> y_pred = [[1], [1], [0], [0]] >>> m = tf.keras.metrics.binary_accuracy(y_true, y_pred) >>> assert m.shape == (4,) >>> m.numpy() array([1., 1., 1., 1.], dtype=float32) Args: y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`. y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`. threshold: (Optional) Float representing the threshold for deciding whether prediction values are 1 or 0. Returns: Binary accuracy values. shape = `[batch_size, d0, .. dN-1]` r-r.)r2 reduce_meanrr&)r6r7r%s rr"r"os16 >$VVY??b   rz"keras.metrics.categorical_accuracycjtjtj|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.r0r5s rr)r)s06  3 vB''  rz)keras.metrics.sparse_categorical_accuracyctj||}|jjdkr'|jddkrt 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-)rr1rQndimsr2squeeze)r6r7matchess rr<r<sQ86vvFFG}Q7=#4#9#9*Wrd++ Nrz(keras.metrics.top_k_categorical_accuracyr@cltjtj|d||S)aEComputes 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.rDr6r7rHs rrArAs24  9 vB''  rz/keras.metrics.sparse_top_k_categorical_accuracyc.tj|||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. )rrEr`s rrMrMs8  9&&! L LLr)r#)r@)"rtensorflow.compat.v2compatv2r2kerasr keras.dtensorrr keras.metricsr keras.utilsr tensorflow.python.util.tf_exportrMeanMetricWrapperr r!r(r;*_SPARSE_CATEGORICAL_UPDATE_STATE_DOCSTRING update_stater?rLr __internal__dispatchadd_dispatch_supportr"r)r<rArMrrrqs!!!!!!!!!000000%%%%%%%%%%%%:99999&'''6'6'6'6'6{,'6'6(''6T,--+ + + + + [2+ + .-+ \1227 7 7 7 7 +77 7 327 t7881 1 1 1 1 =1 1 981 h.*0/&. 566* * * * * k;* * 76* Z;<<) ) ) ) ) K$A) ) =<) Z/*2 ? ? ?-..././.<233./.43<9::.  /.;: F899./.:9:?@@.MMM/.A@MMMr