Module `brevettiai.model.losses`

Expand source code

import numpy as np
import tensorflow as tf
from tensorflow.python.keras.losses import LossFunctionWrapper
from tensorflow.python.keras.utils import losses_utils


def weighted_loss(y_true, y_pred, baseloss, sample_weights, sample_weights_bias, output_weights, **kwargs):
    baseloss_fn = tf.keras.losses.get(baseloss)
    loss = baseloss_fn(y_true[..., None], y_pred[..., None], **kwargs)

    if sample_weights is not None:
        ww = tf.tensordot(y_true, tf.cast(sample_weights, y_true.dtype), axes=[3, 0]) + tf.cast(sample_weights_bias,
                                                                                                y_true.dtype)
        ww = tf.clip_by_value(ww, 0, np.inf)
        loss = loss * ww

    if output_weights is not None:
        loss = tf.tensordot(loss, output_weights, axes=1)

    return loss


class WeightedLossV2(LossFunctionWrapper):
    def __init__(self,
                 baseloss="binary_crossentropy",
                 sample_weights=None,
                 sample_weights_bias=None,
                 output_weights=None,
                 from_logits=False,
                 label_smoothing=0.0,
                 reduction=losses_utils.ReductionV2.AUTO,
                 name='weighted_loss'):

        if sample_weights is not None:
            sample_weights = tf.constant(sample_weights, tf.float32)

        if sample_weights_bias is not None:
            sample_weights_bias = tf.constant(sample_weights_bias, tf.float32)

        if output_weights is not None:
            output_weights = tf.constant(output_weights, tf.float32)

        super().__init__(
            weighted_loss,
            baseloss=baseloss,
            name=name,
            reduction=reduction,
            from_logits=from_logits,
            label_smoothing=label_smoothing,
            sample_weights=sample_weights,
            sample_weights_bias=sample_weights_bias,
            output_weights=output_weights,
        )

Functions

def weighted_loss(y_true, y_pred, baseloss, sample_weights, sample_weights_bias, output_weights, **kwargs)

Expand source code

def weighted_loss(y_true, y_pred, baseloss, sample_weights, sample_weights_bias, output_weights, **kwargs):
    baseloss_fn = tf.keras.losses.get(baseloss)
    loss = baseloss_fn(y_true[..., None], y_pred[..., None], **kwargs)

    if sample_weights is not None:
        ww = tf.tensordot(y_true, tf.cast(sample_weights, y_true.dtype), axes=[3, 0]) + tf.cast(sample_weights_bias,
                                                                                                y_true.dtype)
        ww = tf.clip_by_value(ww, 0, np.inf)
        loss = loss * ww

    if output_weights is not None:
        loss = tf.tensordot(loss, output_weights, axes=1)

    return loss

Classes

class WeightedLossV2 (baseloss='binary_crossentropy', sample_weights=None, sample_weights_bias=None, output_weights=None, from_logits=False, label_smoothing=0.0, reduction='auto', name='weighted_loss')

Wraps a loss function in the Loss class.

Initializes LossFunctionWrapper class.

Args

fn: The loss function to wrap, with signature fn(y_true, y_pred, **kwargs).
reduction: Type of tf.keras.losses.Reduction to apply to loss. Default value is AUTO. AUTO indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE. When used with tf.distribute.Strategy, outside of built-in training loops such as tf.keras compile and fit, using AUTO or SUM_OVER_BATCH_SIZE will raise an error. Please see this custom training tutorial for more details.
name: Optional name for the instance.
**kwargs: The keyword arguments that are passed on to fn.

Expand source code

class WeightedLossV2(LossFunctionWrapper):
    def __init__(self,
                 baseloss="binary_crossentropy",
                 sample_weights=None,
                 sample_weights_bias=None,
                 output_weights=None,
                 from_logits=False,
                 label_smoothing=0.0,
                 reduction=losses_utils.ReductionV2.AUTO,
                 name='weighted_loss'):

        if sample_weights is not None:
            sample_weights = tf.constant(sample_weights, tf.float32)

        if sample_weights_bias is not None:
            sample_weights_bias = tf.constant(sample_weights_bias, tf.float32)

        if output_weights is not None:
            output_weights = tf.constant(output_weights, tf.float32)

        super().__init__(
            weighted_loss,
            baseloss=baseloss,
            name=name,
            reduction=reduction,
            from_logits=from_logits,
            label_smoothing=label_smoothing,
            sample_weights=sample_weights,
            sample_weights_bias=sample_weights_bias,
            output_weights=output_weights,
        )

Ancestors

tensorflow.python.keras.losses.LossFunctionWrapper
tensorflow.python.keras.losses.Loss