ocr_model.py 文件源码

def loss(logits, labels):
  """Add L2Loss to all the trainable variables.

  Add summary for for "Loss" and "Loss/avg".

  Args:
    logits: Logits from inference().
    labels: Labels from distorted_inputs or inputs(). 1-D tensor
            of shape [batch_size]

  Returns:
    Loss tensor of type float.
  """
  # Reshape the labels into a dense Tensor of
  # shape [batch_size, NUM_CLASSES].
  sparse_labels = tf.reshape(labels, [FLAGS.batch_size, 1])
  indices = tf.reshape(tf.range(0, FLAGS.batch_size), [FLAGS.batch_size, 1])
  concated = tf.concat(axis=1, values=[indices, sparse_labels])
  dense_labels = tf.sparse_to_dense(concated,
                                    [FLAGS.batch_size, NUM_CLASSES],
                                    1.0, 0.0)

  # Calculate the average cross entropy loss across the batch.
  cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
    logits=logits, labels=dense_labels, name='cross_entropy_per_example')
  cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
  tf.add_to_collection('losses', cross_entropy_mean)

  # The total loss is defined as the cross entropy loss plus all of the weight
  # decay terms (L2 loss).
  return tf.add_n(tf.get_collection('losses'), name='total_loss')