57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
import tensorflow as tf
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from keras.utils import tf_utils
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from keras.utils import losses_utils
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from keras import backend
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def inv_kl_divergence(y_true, y_pred):
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y_pred = tf.convert_to_tensor(y_pred)
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y_true = tf.cast(y_true, y_pred.dtype)
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y_true = backend.clip(y_true, backend.epsilon(), 1)
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y_pred = backend.clip(y_pred, backend.epsilon(), 1)
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return tf.reduce_sum(y_pred * tf.math.log(y_pred / y_true), axis=-1)
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def masked_bce(y_true, y_pred):
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y_true = tf.cast(y_true, dtype=tf.float32)
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mask = y_true != -1
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return tf.keras.metrics.binary_crossentropy(tf.boolean_mask(y_true, mask),
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tf.boolean_mask(y_pred, mask))
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class LossFunctionWrapper(tf.keras.losses.Loss):
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def __init__(self,
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fn,
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reduction=losses_utils.ReductionV2.AUTO,
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name=None,
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**kwargs):
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super().__init__(reduction=reduction, name=name)
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self.fn = fn
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self._fn_kwargs = kwargs
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def call(self, y_true, y_pred):
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if tf.is_tensor(y_pred) and tf.is_tensor(y_true):
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y_pred, y_true = losses_utils.squeeze_or_expand_dimensions(y_pred, y_true)
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ag_fn = tf.__internal__.autograph.tf_convert(self.fn, tf.__internal__.autograph.control_status_ctx())
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return ag_fn(y_true, y_pred, **self._fn_kwargs)
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def get_config(self):
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config = {}
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for k, v in self._fn_kwargs.items():
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config[k] = backend.eval(v) if tf_utils.is_tensor_or_variable(v) else v
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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class InvKLD(LossFunctionWrapper):
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def __init__(self,
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reduction=losses_utils.ReductionV2.AUTO,
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name='inv_kl_divergence'):
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super().__init__(inv_kl_divergence, name=name, reduction=reduction)
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class MaskedBCE(LossFunctionWrapper):
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def __init__(self,
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reduction=losses_utils.ReductionV2.AUTO,
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name='masked_bce'):
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super().__init__(masked_bce, name=name, reduction=reduction)
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