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Calculates how often predictions matches integer labels.

tf.keras.metrics.SparseCategoricalAccuracy( name='sparse_categorical_accuracy', dtype=None )

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. |

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_states() 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:

model.compile( optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])

`reset_states`

reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

`result`

result()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

`update_state`

update_state( y_true, y_pred, sample_weight=None )

Accumulates metric statistics.

`y_true`

and `y_pred`

should have the same shape.

Args | |
---|---|

`y_true` | Ground truth values. shape = `[batch_size, d0, .. dN]` . |

`y_pred` | The predicted 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. |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/metrics/SparseCategoricalAccuracy