losses.py

# Copyright 2017 Antoine Miech All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Provides definitions for non-regularized training or test losses."""

import tensorflow as tf
from tensorflow import flags
import scipy.io as sio
import numpy as np

FLAGS = flags.FLAGS

flags.DEFINE_float(
      "alpha", "0.5",
      "Ponderation for XENT")


class BaseLoss(object):
  """Inherit from this class when implementing new losses."""

  def calculate_loss(self, unused_predictions, unused_labels, **unused_params):
    """Calculates the average loss of the examples in a mini-batch.

     Args:
      unused_predictions: a 2-d tensor storing the prediction scores, in which
        each row represents a sample in the mini-batch and each column
        represents a class.
      unused_labels: a 2-d tensor storing the labels, which has the same shape
        as the unused_predictions. The labels must be in the range of 0 and 1.
      unused_params: loss specific parameters.

    Returns:
      A scalar loss tensor.
    """
    raise NotImplementedError()


class CrossEntropyLoss(BaseLoss):
  """Calculate the cross entropy loss between the predictions and labels.
  """

  def calculate_loss(self, predictions, labels, **unused_params):
    with tf.name_scope("loss_xent"):
      epsilon = 10e-6
      alpha = FLAGS.alpha

      float_labels = tf.cast(labels, tf.float32)
      cross_entropy_loss = 2*(alpha*float_labels * tf.log(predictions + epsilon) + (1-alpha)*(
          1 - float_labels) * tf.log(1 - predictions + epsilon))
      cross_entropy_loss = tf.negative(cross_entropy_loss)
      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))