google-research

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# coding=utf-8
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# Copyright 2024 The Google Research Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Contains commonly used loss functions."""
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import functools
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from flax.training import common_utils
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import jax
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import jax.numpy as jnp
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def binary_logistic_loss(logit, label, num_classes=2):
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  """Computes the logistic loss for one datapoint.
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  Args:
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    logit: logit predicted by the model
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    label: true class label: 0 or 1.
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    num_classes: not used
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  Returns:
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    loss: value of the loss
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  """
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  del num_classes
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  return (jax.nn.softplus(logit) - label * logit).sum()
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@functools.partial(jax.jit, static_argnums=(2,))
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def cross_entropy_loss(logprobs, label,
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                       num_classes):
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  """Computes the cross entropy loss for one datapoint.
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  Args:
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    logprobs: log probabilities predicted by the model
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    label: true class label
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    num_classes: number of classes in the task
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  Returns:
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    loss: value of the loss.
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  """
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  one_hot_labels = common_utils.onehot(label, num_classes=num_classes)
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  return -jnp.sum(one_hot_labels * logprobs)
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