View source on GitHub |

Computes the crossentropy metric between the labels and predictions.

Inherits From: `Mean`

, `Metric`

, `Layer`

, `Module`

tf.keras.metrics.BinaryCrossentropy( name='binary_crossentropy', dtype=None, from_logits=False, label_smoothing=0 )

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` ". |

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_states() 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:

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

`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/r2.4/api_docs/python/tf/keras/metrics/BinaryCrossentropy