Python tensorflow.python.ops.nn.softmax() Examples

def softmax(logits, scope=None):
  """Performs softmax on Nth dimension of N-dimensional logit tensor.

  For two-dimensional logits this reduces to tf.nn.softmax. The N-th dimension
  needs to have a specified number of elements (number of classes).

  Args:
    logits: N-dimensional `Tensor` with logits, where N > 1.
    scope: Optional scope for variable_scope.

  Returns:
    A `Tensor` with same shape and type as logits.
  """
  with variable_scope.variable_scope(scope, 'softmax', [logits]):
    num_logits = utils.last_dimension(logits.get_shape(), min_rank=2)
    logits_2d = array_ops.reshape(logits, [-1, num_logits])
    predictions = nn.softmax(logits_2d)
    predictions = array_ops.reshape(predictions, array_ops.shape(logits))
    if not tf.executing_eagerly():
      predictions.set_shape(logits.get_shape())
    return predictions 

def softmax(logits, scope=None):
  """Performs softmax on Nth dimension of N-dimensional logit tensor.

  For two-dimensional logits this reduces to tf.nn.softmax. The N-th dimension
  needs to have a specified number of elements (number of classes).

  Args:
    logits: N-dimensional `Tensor` with logits, where N > 1.
    scope: Optional scope for variable_scope.

  Returns:
    A `Tensor` with same shape and type as logits.
  """
  # TODO(jrru): Add axis argument which defaults to last dimension.
  with variable_scope.variable_scope(scope, 'softmax', [logits]):
    num_logits = utils.last_dimension(logits.get_shape(), min_rank=2)
    logits_2d = array_ops.reshape(logits, [-1, num_logits])
    predictions = nn.softmax(logits_2d)
    predictions = array_ops.reshape(predictions, array_ops.shape(logits))
    if not context.executing_eagerly():
      predictions.set_shape(logits.get_shape())
    return predictions 

def softmax(logits, scope=None):
  """Performs softmax on Nth dimension of N-dimensional logit tensor.

  For two-dimensional logits this reduces to tf.nn.softmax. The N-th dimension
  needs to have a specified number of elements (number of classes).

  Args:
    logits: N-dimensional `Tensor` with logits, where N > 1.
    scope: Optional scope for variable_scope.

  Returns:
    a `Tensor` with same shape and type as logits.
  """
  # TODO(jrru): Add axis argument which defaults to last dimension.
  with variable_scope.variable_scope(scope, 'softmax', [logits]):
    num_logits = utils.last_dimension(logits.get_shape(), min_rank=2)
    logits_2d = array_ops.reshape(logits, [-1, num_logits])
    predictions = nn.softmax(logits_2d)
    predictions = array_ops.reshape(predictions, array_ops.shape(logits))
    predictions.set_shape(logits.get_shape())
    return predictions 

def softmax(logits, scope=None):
  """Performs softmax on Nth dimension of N-dimensional logit tensor.

  For two-dimensional logits this reduces to tf.nn.softmax. The N-th dimension
  needs to have a specified number of elements (number of classes).

  Args:
    logits: N-dimensional `Tensor` with logits, where N > 1.
    scope: Optional scope for variable_scope.

  Returns:
    a `Tensor` with same shape and type as logits.
  """
  # TODO(jrru): Add axis argument which defaults to last dimension.
  with variable_scope.variable_scope(scope, 'softmax', [logits]):
    num_logits = utils.last_dimension(logits.get_shape(), min_rank=2)
    logits_2d = array_ops.reshape(logits, [-1, num_logits])
    predictions = nn.softmax(logits_2d)
    predictions = array_ops.reshape(predictions, array_ops.shape(logits))
    predictions.set_shape(logits.get_shape())
    return predictions 

def softmax(logits, scope=None):
  """Performs softmax on Nth dimension of N-dimensional logit tensor.

  For two-dimensional logits this reduces to tf.nn.softmax. The N-th dimension
  needs to have a specified number of elements (number of classes).

  Args:
    logits: N-dimensional `Tensor` with logits, where N > 1.
    scope: Optional scope for variable_scope.

  Returns:
    a `Tensor` with same shape and type as logits.
  """
  # TODO(jrru): Add axis argument which defaults to last dimension.
  with variable_scope.variable_scope(scope, 'softmax', [logits]):
    num_logits = utils.last_dimension(logits.get_shape(), min_rank=2)
    logits_2d = array_ops.reshape(logits, [-1, num_logits])
    predictions = nn.softmax(logits_2d)
    predictions = array_ops.reshape(predictions, array_ops.shape(logits))
    predictions.set_shape(logits.get_shape())
    return predictions 

def softmax(x):
  """Softmax of a tensor.

  Arguments:
      x: A tensor or variable.

  Returns:
      A tensor.
  """
  return nn.softmax(x) 

def softmax_classifier(tensor_in,
                       labels,
                       weights,
                       biases,
                       class_weight=None,
                       name=None):
  """Returns prediction and loss for softmax classifier.

  This function returns "probabilities" and a cross entropy loss. To obtain
  predictions, use `tf.argmax` on the returned probabilities.

  This function requires labels to be passed in one-hot encoding.

  Args:
    tensor_in: Input tensor, [batch_size, feature_size], features.
    labels: Tensor, [batch_size, n_classes], one-hot labels of the output
      classes.
    weights: Tensor, [batch_size, feature_size], linear transformation
      matrix.
    biases: Tensor, [batch_size], biases.
    class_weight: Tensor, optional, [n_classes], weight for each class.
      If not given, all classes are supposed to have weight one.
    name: Operation name.

  Returns:
    `tuple` of softmax predictions and loss `Tensor`s.
  """
  with ops.name_scope(name, "softmax_classifier", [tensor_in, labels]):
    logits = nn.xw_plus_b(tensor_in, weights, biases)
    if class_weight is not None:
      logits = math_ops.mul(logits, class_weight)
    return nn.softmax(logits), loss_ops.softmax_cross_entropy(logits, labels) 

def _predictions(logits, n_classes):
  """Returns predictions for the given logits and n_classes."""
  predictions = {}
  if n_classes == 2:
    predictions[_LOGISTIC] = math_ops.sigmoid(logits)
    logits = array_ops.concat(1, [array_ops.zeros_like(logits), logits])
  predictions[_PROBABILITIES] = nn.softmax(logits)
  predictions[_CLASSES] = array_ops.reshape(
      math_ops.argmax(logits, 1), shape=(-1, 1))
  return predictions 

def _convert_to_estimator_model_result(self, logits_fn_result):
    logits, loss, train_op = logits_fn_result
    return {
        Classifier.CLASS_OUTPUT:
            math_ops.argmax(logits, len(logits.get_shape()) - 1),
        Classifier.PROBABILITY_OUTPUT: nn.softmax(logits)
    }, loss, train_op 

def _logits_to_prediction(self, logits=None):
    # pylint: disable=missing-docstring
    predictions = {prediction_key.PredictionKey.LOGITS: logits}
    if self.logits_dimension == 1:
      predictions[prediction_key.PredictionKey.LOGISTIC] = math_ops.sigmoid(
          logits)
      logits = array_ops.concat(1, [array_ops.zeros_like(logits), logits])
    predictions[prediction_key.PredictionKey.PROBABILITIES] = nn.softmax(
        logits)
    predictions[prediction_key.PredictionKey.CLASSES] = math_ops.argmax(
        logits, 1)

    return predictions 

def multi_class_target(n_classes, label_name=None, weight_column_name=None):
  """Creates a _TargetColumn for multi class single label classification.

  The target column uses softmax cross entropy loss.

  Args:
    n_classes: Integer, number of classes, must be >= 2
    label_name: String, name of the key in label dict. Can be null if label
        is a tensor (single headed models).
    weight_column_name: A string defining feature column name representing
      weights. It is used to down weight or boost examples during training. It
      will be multiplied by the loss of the example.

  Returns:
    An instance of _MultiClassTargetColumn.

  Raises:
    ValueError: if n_classes is < 2
  """
  if n_classes < 2:
    raise ValueError("n_classes must be > 1 for classification.")
  if n_classes == 2:
    loss_fn = _log_loss_with_two_classes
  else:
    loss_fn = _softmax_cross_entropy_loss
  return _MultiClassTargetColumn(loss_fn=loss_fn,
                                 n_classes=n_classes,
                                 label_name=label_name,
                                 weight_column_name=weight_column_name) 

def logits_to_predictions(self, logits, proba=False):
    if self.num_label_columns == 1:
      logits = array_ops.concat(1, [array_ops.zeros_like(logits), logits])

    if proba:
      return nn.softmax(logits)
    else:
      return math_ops.argmax(logits, 1) 

def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
  """Returns predictions and loss for sequence of predictions.

  Args:
    decoding: List of Tensors with predictions.
    labels: List of Tensors with labels.
    sampling_decoding: Optional, List of Tensor with predictions to be used
      in sampling. E.g. they shouldn't have dependncy on outputs.
      If not provided, decoding is used.
    name: Operation name.

  Returns:
    Predictions and losses tensors.
  """
  with ops.name_scope(name, "sequence_classifier", [decoding, labels]):
    predictions, xent_list = [], []
    for i, pred in enumerate(decoding):
      xent_list.append(nn.softmax_cross_entropy_with_logits(
          pred, labels[i],
          name="sequence_loss/xent_raw{0}".format(i)))
      if sampling_decoding:
        predictions.append(nn.softmax(sampling_decoding[i]))
      else:
        predictions.append(nn.softmax(pred))
    xent = math_ops.add_n(xent_list, name="sequence_loss/xent")
    loss = math_ops.reduce_sum(xent, name="sequence_loss")
    return array_ops_.pack(predictions, axis=1), loss 

def categorical_crossentropy(target, output, from_logits=False):
  """Categorical crossentropy between an output tensor and a target tensor.

  Arguments:
      target: A tensor of the same shape as `output`.
      output: A tensor resulting from a softmax
          (unless `from_logits` is True, in which
          case `output` is expected to be the logits).
      from_logits: Boolean, whether `output` is the
          result of a softmax, or is a tensor of logits.

  Returns:
      Output tensor.
  """
  # Note: nn.softmax_cross_entropy_with_logits
  # expects logits, Keras expects probabilities.
  if not from_logits:
    # scale preds so that the class probas of each sample sum to 1
    output /= math_ops.reduce_sum(
        output, axis=len(output.get_shape()) - 1, keep_dims=True)
    # manual computation of crossentropy
    epsilon_ = _to_tensor(epsilon(), output.dtype.base_dtype)
    output = clip_ops.clip_by_value(output, epsilon_, 1. - epsilon_)
    return -math_ops.reduce_sum(
        target * math_ops.log(output),
        axis=len(output.get_shape()) - 1)
  else:
    return nn.softmax_cross_entropy_with_logits(labels=target, logits=output) 

def sparse_categorical_crossentropy(target, output, from_logits=False):
  """Categorical crossentropy with integer targets.

  Arguments:
      target: An integer tensor.
      output: A tensor resulting from a softmax
          (unless `from_logits` is True, in which
          case `output` is expected to be the logits).
      from_logits: Boolean, whether `output` is the
          result of a softmax, or is a tensor of logits.

  Returns:
      Output tensor.
  """
  # Note: nn.sparse_softmax_cross_entropy_with_logits
  # expects logits, Keras expects probabilities.
  if not from_logits:
    epsilon_ = _to_tensor(epsilon(), output.dtype.base_dtype)
    output = clip_ops.clip_by_value(output, epsilon_, 1 - epsilon_)
    output = math_ops.log(output)

  output_shape = output.get_shape()
  targets = cast(flatten(target), 'int64')
  logits = array_ops.reshape(output, [-1, int(output_shape[-1])])
  res = nn.sparse_softmax_cross_entropy_with_logits(
      labels=targets, logits=logits)
  if len(output_shape) == 3:
    # if our output includes timesteps we need to reshape
    return array_ops.reshape(res, array_ops.shape(output)[:-1])
  else:
    return res 

def ctc_batch_cost(y_true, y_pred, input_length, label_length):
  """Runs CTC loss algorithm on each batch element.

  Arguments:
      y_true: tensor `(samples, max_string_length)`
          containing the truth labels.
      y_pred: tensor `(samples, time_steps, num_categories)`
          containing the prediction, or output of the softmax.
      input_length: tensor `(samples, 1)` containing the sequence length for
          each batch item in `y_pred`.
      label_length: tensor `(samples, 1)` containing the sequence length for
          each batch item in `y_true`.

  Returns:
      Tensor with shape (samples,1) containing the
          CTC loss of each element.
  """
  label_length = math_ops.to_int32(array_ops.squeeze(label_length))
  input_length = math_ops.to_int32(array_ops.squeeze(input_length))
  sparse_labels = math_ops.to_int32(
      ctc_label_dense_to_sparse(y_true, label_length))

  y_pred = math_ops.log(array_ops.transpose(y_pred, perm=[1, 0, 2]) + 1e-8)

  return array_ops.expand_dims(
      ctc.ctc_loss(
          inputs=y_pred, labels=sparse_labels, sequence_length=input_length), 1) 

def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
  """Returns predictions and loss for sequence of predictions.

  Args:
    decoding: List of Tensors with predictions.
    labels: List of Tensors with labels.
    sampling_decoding: Optional, List of Tensor with predictions to be used
      in sampling. E.g. they shouldn't have dependncy on outputs.
      If not provided, decoding is used.
    name: Operation name.

  Returns:
    Predictions and losses tensors.
  """
  with ops.name_scope(name, "sequence_classifier", [decoding, labels]):
    predictions, xent_list = [], []
    for i, pred in enumerate(decoding):
      xent_list.append(nn.softmax_cross_entropy_with_logits(
          labels=labels[i], logits=pred,
          name="sequence_loss/xent_raw{0}".format(i)))
      if sampling_decoding:
        predictions.append(nn.softmax(sampling_decoding[i]))
      else:
        predictions.append(nn.softmax(pred))
    xent = math_ops.add_n(xent_list, name="sequence_loss/xent")
    loss = math_ops.reduce_sum(xent, name="sequence_loss")
    return array_ops.stack(predictions, axis=1), loss 

def softmax_classifier(tensor_in,
                       labels,
                       weights,
                       biases,
                       class_weight=None,
                       name=None):
  """Returns prediction and loss for softmax classifier.

  This function returns "probabilities" and a cross entropy loss. To obtain
  predictions, use `tf.argmax` on the returned probabilities.

  This function requires labels to be passed in one-hot encoding.

  Args:
    tensor_in: Input tensor, [batch_size, feature_size], features.
    labels: Tensor, [batch_size, n_classes], one-hot labels of the output
      classes.
    weights: Tensor, [batch_size, feature_size], linear transformation
      matrix.
    biases: Tensor, [batch_size], biases.
    class_weight: Tensor, optional, [n_classes], weight for each class.
      If not given, all classes are supposed to have weight one.
    name: Operation name.

  Returns:
    `tuple` of softmax predictions and loss `Tensor`s.
  """
  with ops.name_scope(name, 'softmax_classifier', [tensor_in, labels]):
    logits = nn.xw_plus_b(tensor_in, weights, biases)
    if class_weight is not None:
      logits = math_ops.multiply(logits, class_weight)
    return nn.softmax(logits), loss_ops.softmax_cross_entropy(logits, labels) 

def _logits_to_predictions(self, logits):
    """Returns a dict of predictions.

    Args:
      logits: logits `Output` after applying possible centered bias.

    Returns:
      Dict of prediction `Output` keyed by `PredictionKey`.
    """
    with ops.name_scope(None, "predictions", (logits,)):
      two_class_logits = _one_class_to_two_class_logits(logits)
      return {
          prediction_key.PredictionKey.LOGITS:
              logits,
          prediction_key.PredictionKey.LOGISTIC:
              math_ops.sigmoid(
                  logits, name=prediction_key.PredictionKey.LOGISTIC),
          prediction_key.PredictionKey.PROBABILITIES:
              nn.softmax(
                  two_class_logits,
                  name=prediction_key.PredictionKey.PROBABILITIES),
          prediction_key.PredictionKey.CLASSES:
              math_ops.argmax(
                  two_class_logits,
                  1,
                  name=prediction_key.PredictionKey.CLASSES)
      } 

def multi_class_target(n_classes, label_name=None, weight_column_name=None):
  """Creates a _TargetColumn for multi class single label classification.

  The target column uses softmax cross entropy loss.

  Args:
    n_classes: Integer, number of classes, must be >= 2
    label_name: String, name of the key in label dict. Can be null if label
        is a tensor (single headed models).
    weight_column_name: A string defining feature column name representing
      weights. It is used to down weight or boost examples during training. It
      will be multiplied by the loss of the example.

  Returns:
    An instance of _MultiClassTargetColumn.

  Raises:
    ValueError: if n_classes is < 2
  """
  if n_classes < 2:
    raise ValueError("n_classes must be > 1 for classification.")
  if n_classes == 2:
    loss_fn = _log_loss_with_two_classes
  else:
    loss_fn = _softmax_cross_entropy_loss
  return _MultiClassTargetColumn(
      loss_fn=loss_fn,
      n_classes=n_classes,
      label_name=label_name,
      weight_column_name=weight_column_name) 

def logits_to_predictions(self, logits, proba=False):
    if self.num_label_columns == 1:
      logits = array_ops.concat([array_ops.zeros_like(logits), logits], 1)

    if proba:
      return nn.softmax(logits)
    else:
      return math_ops.argmax(logits, 1) 

def logits_to_predictions(self, logits, proba=False):
    if self.num_label_columns == 1:
      logits = array_ops.concat([array_ops.zeros_like(logits), logits], 1)

    if proba:
      return nn.softmax(logits)
    else:
      return math_ops.argmax(logits, 1) 

def ctc_batch_cost(y_true, y_pred, input_length, label_length):
  """Runs CTC loss algorithm on each batch element.

  Arguments:
      y_true: tensor `(samples, max_string_length)`
          containing the truth labels.
      y_pred: tensor `(samples, time_steps, num_categories)`
          containing the prediction, or output of the softmax.
      input_length: tensor `(samples, 1)` containing the sequence length for
          each batch item in `y_pred`.
      label_length: tensor `(samples, 1)` containing the sequence length for
          each batch item in `y_true`.

  Returns:
      Tensor with shape (samples,1) containing the
          CTC loss of each element.
  """
  label_length = math_ops.to_int32(array_ops.squeeze(label_length))
  input_length = math_ops.to_int32(array_ops.squeeze(input_length))
  sparse_labels = math_ops.to_int32(
      ctc_label_dense_to_sparse(y_true, label_length))

  y_pred = math_ops.log(array_ops.transpose(y_pred, perm=[1, 0, 2]) + 1e-8)

  return array_ops.expand_dims(
      ctc.ctc_loss(
          inputs=y_pred, labels=sparse_labels, sequence_length=input_length), 1) 

def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
  """Returns predictions and loss for sequence of predictions.

  Args:
    decoding: List of Tensors with predictions.
    labels: List of Tensors with labels.
    sampling_decoding: Optional, List of Tensor with predictions to be used
      in sampling. E.g. they shouldn't have dependncy on outputs.
      If not provided, decoding is used.
    name: Operation name.

  Returns:
    Predictions and losses tensors.
  """
  with ops.name_scope(name, "sequence_classifier", [decoding, labels]):
    predictions, xent_list = [], []
    for i, pred in enumerate(decoding):
      xent_list.append(nn.softmax_cross_entropy_with_logits(
          labels=labels[i], logits=pred,
          name="sequence_loss/xent_raw{0}".format(i)))
      if sampling_decoding:
        predictions.append(nn.softmax(sampling_decoding[i]))
      else:
        predictions.append(nn.softmax(pred))
    xent = math_ops.add_n(xent_list, name="sequence_loss/xent")
    loss = math_ops.reduce_sum(xent, name="sequence_loss")
    return array_ops.stack(predictions, axis=1), loss 

def softmax_classifier(tensor_in,
                       labels,
                       weights,
                       biases,
                       class_weight=None,
                       name=None):
  """Returns prediction and loss for softmax classifier.

  This function returns "probabilities" and a cross entropy loss. To obtain
  predictions, use `tf.argmax` on the returned probabilities.

  This function requires labels to be passed in one-hot encoding.

  Args:
    tensor_in: Input tensor, [batch_size, feature_size], features.
    labels: Tensor, [batch_size, n_classes], one-hot labels of the output
      classes.
    weights: Tensor, [batch_size, feature_size], linear transformation
      matrix.
    biases: Tensor, [batch_size], biases.
    class_weight: Tensor, optional, [n_classes], weight for each class.
      If not given, all classes are supposed to have weight one.
    name: Operation name.

  Returns:
    `tuple` of softmax predictions and loss `Tensor`s.
  """
  with ops.name_scope(name, 'softmax_classifier', [tensor_in, labels]):
    logits = nn.xw_plus_b(tensor_in, weights, biases)
    if class_weight is not None:
      logits = math_ops.multiply(logits, class_weight)
    return nn.softmax(logits), loss_ops.softmax_cross_entropy(logits, labels) 

def _logits_to_predictions(self, logits):
    """Returns a dict of predictions.

    Args:
      logits: logits `Output` after applying possible centered bias.

    Returns:
      Dict of prediction `Output` keyed by `PredictionKey`.
    """
    with ops.name_scope(None, "predictions", (logits,)):
      two_class_logits = _one_class_to_two_class_logits(logits)
      return {
          prediction_key.PredictionKey.LOGITS:
              logits,
          prediction_key.PredictionKey.LOGISTIC:
              math_ops.sigmoid(
                  logits, name=prediction_key.PredictionKey.LOGISTIC),
          prediction_key.PredictionKey.PROBABILITIES:
              nn.softmax(
                  two_class_logits,
                  name=prediction_key.PredictionKey.PROBABILITIES),
          prediction_key.PredictionKey.CLASSES:
              math_ops.argmax(
                  two_class_logits,
                  1,
                  name=prediction_key.PredictionKey.CLASSES)
      } 

def sparse_categorical_crossentropy(output, target, from_logits=False):
  """Categorical crossentropy with integer targets.

  Arguments:
      output: A tensor resulting from a softmax
          (unless `from_logits` is True, in which
          case `output` is expected to be the logits).
      target: An integer tensor.
      from_logits: Boolean, whether `output` is the
          result of a softmax, or is a tensor of logits.

  Returns:
      Output tensor.
  """
  # Note: nn.softmax_cross_entropy_with_logits
  # expects logits, Keras expects probabilities.
  if not from_logits:
    epsilon = _to_tensor(_EPSILON, output.dtype.base_dtype)
    output = clip_ops.clip_by_value(output, epsilon, 1 - epsilon)
    output = math_ops.log(output)

  output_shape = output.get_shape()
  targets = cast(flatten(target), 'int64')
  logits = array_ops.reshape(output, [-1, int(output_shape[-1])])
  res = nn.sparse_softmax_cross_entropy_with_logits(
      labels=targets, logits=logits)
  if len(output_shape) == 3:
    # if our output includes timesteps we need to reshape
    return array_ops.reshape(res, array_ops.shape(output)[:-1])
  else:
    return res 

def _logits_to_predictions(self, logits):
    """Returns a dict of predictions.

    Args:
      logits: logits `Tensor` after applying possible centered bias.

    Returns:
      Dict of prediction `Tensor` keyed by `PredictionKey`.
    """
    with ops.name_scope(None, "predictions", (logits,)):
      class_ids = math_ops.argmax(
          logits, 1, name=prediction_key.PredictionKey.CLASSES)
      if self._label_keys:
        table = lookup_lib.index_to_string_table_from_tensor(
            mapping=self._label_keys,
            name="class_string_lookup")
        classes = table.lookup(class_ids)
      else:
        classes = class_ids
      return {
          prediction_key.PredictionKey.LOGITS: logits,
          prediction_key.PredictionKey.PROBABILITIES:
              nn.softmax(
                  logits, name=prediction_key.PredictionKey.PROBABILITIES),
          prediction_key.PredictionKey.CLASSES: classes
      } 

def multi_class_target(n_classes, label_name=None, weight_column_name=None):
  """Creates a _TargetColumn for multi class single label classification.

  The target column uses softmax cross entropy loss.

  Args:
    n_classes: Integer, number of classes, must be >= 2
    label_name: String, name of the key in label dict. Can be null if label
        is a tensor (single headed models).
    weight_column_name: A string defining feature column name representing
      weights. It is used to down weight or boost examples during training. It
      will be multiplied by the loss of the example.

  Returns:
    An instance of _MultiClassTargetColumn.

  Raises:
    ValueError: if n_classes is < 2
  """
  if n_classes < 2:
    raise ValueError("n_classes must be > 1 for classification.")
  if n_classes == 2:
    loss_fn = _log_loss_with_two_classes
  else:
    loss_fn = _softmax_cross_entropy_loss
  return _MultiClassTargetColumn(
      loss_fn=loss_fn,
      n_classes=n_classes,
      label_name=label_name,
      weight_column_name=weight_column_name) 

def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
  """Returns predictions and loss for sequence of predictions.

  Args:
    decoding: List of Tensors with predictions.
    labels: List of Tensors with labels.
    sampling_decoding: Optional, List of Tensor with predictions to be used
      in sampling. E.g. they shouldn't have dependncy on outputs.
      If not provided, decoding is used.
    name: Operation name.

  Returns:
    Predictions and losses tensors.
  """
  with ops.name_scope(name, "sequence_classifier", [decoding, labels]):
    predictions, xent_list = [], []
    for i, pred in enumerate(decoding):
      xent_list.append(nn.softmax_cross_entropy_with_logits(
          labels=labels[i], logits=pred,
          name="sequence_loss/xent_raw{0}".format(i)))
      if sampling_decoding:
        predictions.append(nn.softmax(sampling_decoding[i]))
      else:
        predictions.append(nn.softmax(pred))
    xent = math_ops.add_n(xent_list, name="sequence_loss/xent")
    loss = math_ops.reduce_sum(xent, name="sequence_loss")
    return array_ops.stack(predictions, axis=1), loss