python类to_int64()的实例源码

def get_classification_loss(logits, targets, softmax_loss_function=None):
  bucket_outputs = logits
  if softmax_loss_function is None:
    assert len(bucket_outputs) == len(targets) == 1
    # We need to make target an int64-tensor and set its shape.
    bucket_target = array_ops.reshape(math_ops.to_int64(targets[0]), [-1])
    crossent = nn_ops.sparse_softmax_cross_entropy_with_logits(logits=bucket_outputs[0],
                                                               labels=bucket_target)
  else:
    assert len(bucket_outputs) == len(targets) == 1
    crossent = softmax_loss_function(bucket_outputs[0], targets[0])

  batch_size = array_ops.shape(targets[0])[0]
  loss = tf.reduce_sum(crossent) / math_ops.cast(batch_size, dtypes.float32)

  return loss

def _get_eval_ops(self, features, targets, metrics):
    features, spec = data_ops.ParseDataTensorOrDict(features)
    labels = data_ops.ParseLabelTensorOrDict(targets)

    graph_builder = self.graph_builder_class(
        self.params, device_assigner=self.device_assigner, training=False,
        **self.construction_args)

    probabilities = graph_builder.inference_graph(features, data_spec=spec)

    # One-hot the labels.
    if not self.params.regression:
      labels = math_ops.to_int64(array_ops.one_hot(math_ops.to_int64(
          array_ops.squeeze(labels)), self.params.num_classes, 1, 0))

    if metrics is None:
      metrics = {self.accuracy_metric:
                 eval_metrics.get_metric(self.accuracy_metric)}

    result = {}
    for name, metric in six.iteritems(metrics):
      result[name] = metric(probabilities, labels)

    return result

def _lengths_to_masks(lengths, max_length):
  """Creates a binary matrix that can be used to mask away padding.

  Args:
    lengths: A vector of integers representing lengths.
    max_length: An integer indicating the maximum length. All values in
      lengths should be less than max_length.
  Returns:
    masks: Masks that can be used to get rid of padding.
  """
  tiled_ranges = array_ops.tile(
      array_ops.expand_dims(math_ops.range(max_length), 0),
      [array_ops.shape(lengths)[0], 1])
  lengths = array_ops.expand_dims(lengths, 1)
  masks = math_ops.to_float(
      math_ops.to_int64(tiled_ranges) < math_ops.to_int64(lengths))
  return masks

def assert_integer_form(
    x, data=None, summarize=None, message=None, name="assert_integer_form"):
  """Assert that x has integer components (or floats equal to integers).

  Args:
    x: Numeric `Tensor`
    data: The tensors to print out if the condition is `False`. Defaults to
      error message and first few entries of `x` and `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).

  Returns:
    Op raising `InvalidArgumentError` if round(x) != x.
  """

  message = message or "x has non-integer components"
  x = ops.convert_to_tensor(x, name="x")
  casted_x = math_ops.to_int64(x)
  return check_ops.assert_equal(
      x, math_ops.cast(math_ops.round(casted_x), x.dtype),
      data=data, summarize=summarize, message=message, name=name)

def _lengths_to_masks(lengths, max_length):
  """Creates a binary matrix that can be used to mask away padding.

  Args:
    lengths: A vector of integers representing lengths.
    max_length: An integer indicating the maximum length. All values in
      lengths should be less than max_length.
  Returns:
    masks: Masks that can be used to get rid of padding.
  """
  tiled_ranges = array_ops.tile(
      array_ops.expand_dims(math_ops.range(max_length), 0),
      [array_ops.shape(lengths)[0], 1])
  lengths = array_ops.expand_dims(lengths, 1)
  masks = math_ops.to_float(
      math_ops.to_int64(tiled_ranges) < math_ops.to_int64(lengths))
  return masks

def assert_integer_form(
    x, data=None, summarize=None, message=None, name="assert_integer_form"):
  """Assert that x has integer components (or floats equal to integers).

  Args:
    x: Numeric `Tensor`
    data: The tensors to print out if the condition is `False`. Defaults to
      error message and first few entries of `x` and `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).

  Returns:
    Op raising `InvalidArgumentError` if round(x) != x.
  """

  message = message or "x has non-integer components"
  x = ops.convert_to_tensor(x, name="x")
  casted_x = math_ops.to_int64(x)
  return check_ops.assert_equal(
      x, math_ops.cast(math_ops.round(casted_x), x.dtype),
      data=data, summarize=summarize, message=message, name=name)

def _lengths_to_masks(lengths, max_length):
  """Creates a binary matrix that can be used to mask away padding.

  Args:
    lengths: A vector of integers representing lengths.
    max_length: An integer indicating the maximum length. All values in
      lengths should be less than max_length.
  Returns:
    masks: Masks that can be used to get rid of padding.
  """

  tiled_ranges = array_ops.tile(
      array_ops.expand_dims(math_ops.range(max_length), 0),
      [array_ops.shape(lengths)[0], 1])
  lengths = array_ops.expand_dims(lengths, 1)
  masks = math_ops.to_float(
      math_ops.to_int64(tiled_ranges) < math_ops.to_int64(lengths))

  return masks

def _lengths_to_masks(lengths, max_length):
  """Creates a binary matrix that can be used to mask away padding.

  Args:
    lengths: A vector of integers representing lengths.
    max_length: An integer indicating the maximum length. All values in
      lengths should be less than max_length.
  Returns:
    masks: Masks that can be used to get rid of padding.
  """
  tiled_ranges = array_ops.tile(
      array_ops.expand_dims(math_ops.range(max_length), 0),
      [array_ops.shape(lengths)[0], 1])
  lengths = array_ops.expand_dims(lengths, 1)
  masks = math_ops.to_float(
      math_ops.to_int64(tiled_ranges) < math_ops.to_int64(lengths))
  return masks

def assert_integer_form(
    x, data=None, summarize=None, message=None, name="assert_integer_form"):
  """Assert that x has integer components (or floats equal to integers).

  Args:
    x: Numeric `Tensor`
    data: The tensors to print out if the condition is `False`. Defaults to
      error message and first few entries of `x` and `y`.
    summarize: Print this many entries of each tensor.
    message: A string to prefix to the default message.
    name: A name for this operation (optional).

  Returns:
    Op raising `InvalidArgumentError` if round(x) != x.
  """

  message = message or "x has non-integer components"
  x = ops.convert_to_tensor(x, name="x")
  casted_x = math_ops.to_int64(x)
  return check_ops.assert_equal(
      x, math_ops.cast(math_ops.round(casted_x), x.dtype),
      data=data, summarize=summarize, message=message, name=name)

def _reverse_seq(input_seq, lengths):
  """Reverse a list of Tensors up to specified lengths.
  Args:
    input_seq: Sequence of seq_len tensors of dimension (batch_size, depth)
    lengths:   A tensor of dimension batch_size, containing lengths for each
               sequence in the batch. If "None" is specified, simply reverses
               the list.
  Returns:
    time-reversed sequence
  """
  if lengths is None:
    return list(reversed(input_seq))

  input_shape = tensor_shape.matrix(None, None)
  for input_ in input_seq:
    input_shape.merge_with(input_.get_shape())
    input_.set_shape(input_shape)

  # Join into (time, batch_size, depth)
  s_joined = array_ops.pack(input_seq)

  # TODO(schuster, ebrevdo): Remove cast when reverse_sequence takes int32
  if lengths is not None:
    lengths = math_ops.to_int64(lengths)

  # Reverse along dimension 0
  s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
  # Split again into list
  result = array_ops.unpack(s_reversed)
  for r in result:
    r.set_shape(input_shape)
  return result

def get_sequence_loss(logits, targets, weights, softmax_loss_function=None, per_example_loss=False):

  if per_example_loss:
    assert len(logits) == len(targets)
    # We need to make target and int64-tensor and set its shape.
    bucket_target = [array_ops.reshape(math_ops.to_int64(x), [-1]) for x in targets]
    crossent = sequence_loss_by_example(logits, bucket_target, weights,
                                              softmax_loss_function=softmax_loss_function)
  else:
    assert len(logits) == len(targets)
    bucket_target = [array_ops.reshape(math_ops.to_int64(x), [-1]) for x in targets]
    crossent = sequence_loss_by_batch(logits, bucket_target, weights,
                                      softmax_loss_function=softmax_loss_function)

  return crossent

def _reverse_seq(input_seq, lengths):
  """Reverse a list of Tensors up to specified lengths.

  Args:
    input_seq: Sequence of seq_len tensors of dimension (batch_size, depth)
    lengths:   A tensor of dimension batch_size, containing lengths for each
               sequence in the batch. If "None" is specified, simply reverses
               the list.

  Returns:
    time-reversed sequence
  """
  if lengths is None:
    return list(reversed(input_seq))

  input_shape = tensor_shape.matrix(None, None)
  for input_ in input_seq:
    input_shape.merge_with(input_.get_shape())
    input_.set_shape(input_shape)

  # Join into (time, batch_size, depth)
  s_joined = array_ops.pack(input_seq)

  # TODO(schuster, ebrevdo): Remove cast when reverse_sequence takes int32
  if lengths is not None:
    lengths = math_ops.to_int64(lengths)

  # Reverse along dimension 0
  s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
  # Split again into list
  result = array_ops.unpack(s_reversed)
  for r in result:
    r.set_shape(input_shape)
  return result

def _get_eval_ops(self, features, targets, metrics):
    features, _, spec = data_ops.ParseDataTensorOrDict(features)
    labels = data_ops.ParseLabelTensorOrDict(targets)
    _assert_float32(features)
    _assert_float32(labels)

    graph_builder = self.graph_builder_class(
        self.params, device_assigner=self.device_assigner, training=False,
        **self.construction_args)

    probabilities = graph_builder.inference_graph(features, data_spec=spec)

    # One-hot the labels.
    if not self.params.regression:
      labels = math_ops.to_int64(array_ops.one_hot(math_ops.to_int64(
          array_ops.squeeze(labels)), self.params.num_classes, 1, 0))

    if metrics is None:
      metrics = {self.accuracy_metric:
                 eval_metrics.get_metric(self.accuracy_metric)}

    result = {}
    for name, metric in six.iteritems(metrics):
      result[name] = metric(probabilities, labels)

    return result

def to_dnn_input_layer(self,
                         input_tensor,
                         weight_collections=None,
                         trainable=True):
    return array_ops.reshape(
        array_ops.one_hot(
            math_ops.to_int64(input_tensor),
            self.length,
            1.,
            0.,
            name="one_hot"), [-1, self.length * self.source_column.dimension],
        name="reshape")

def _select_class_id(ids, selected_id):
  """Filter all but `selected_id` out of `ids`.

  Args:
    ids: `int64` `Tensor` or `SparseTensor` of IDs.
    selected_id: Int id to select.

  Returns:
    `SparseTensor` of same dimensions as `ids`, except for the last dimension,
    which might be smaller. This contains only the entries equal to
    `selected_id`.
  """
  if isinstance(ids, (ops.SparseTensor, ops.SparseTensorValue)):
    return sparse_ops.sparse_retain(
        ids, math_ops.equal(ids.values, selected_id))

  # TODO(ptucker): Make this more efficient, maybe add a sparse version of
  # tf.equal and tf.reduce_any?

  # Shape of filled IDs is the same as `ids` with the last dim collapsed to 1.
  ids_shape = array_ops.shape(ids)
  ids_last_dim = array_ops.size(ids_shape) - 1
  filled_selected_id_shape = math_ops.reduced_shape(
      ids_shape, array_ops.reshape(ids_last_dim, [1]))

  # Intersect `ids` with the selected ID.
  filled_selected_id = array_ops.fill(
      filled_selected_id_shape, math_ops.to_int64(selected_id))
  return set_ops.set_intersection(filled_selected_id, ids)

def _logits_to_predictions(self, logits):
    """See `_MultiClassHead`."""
    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] = math_ops.sigmoid(
        logits)
    predictions[prediction_key.PredictionKey.CLASSES] = math_ops.to_int64(
        math_ops.greater(logits, 0))
    return predictions

def _to_dnn_input_layer(self,
                          input_tensor,
                          weight_collections=None,
                          trainable=True,
                          output_rank=2):
    if output_rank != 2:
      raise ValueError("BucketizedColumn currently only supports output_rank=2")
    return array_ops.reshape(
        array_ops.one_hot(
            math_ops.to_int64(input_tensor),
            self.length,
            1.,
            0.,
            name="one_hot"), [-1, self.length * self.source_column.dimension],
        name="reshape")

def _select_class_id(ids, selected_id):
  """Filter all but `selected_id` out of `ids`.

  Args:
    ids: `int64` `Tensor` or `SparseTensor` of IDs.
    selected_id: Int id to select.

  Returns:
    `SparseTensor` of same dimensions as `ids`. This contains only the entries
    equal to `selected_id`.
  """
  if isinstance(
      ids, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
    return sparse_ops.sparse_retain(
        ids, math_ops.equal(ids.values, selected_id))

  # TODO(ptucker): Make this more efficient, maybe add a sparse version of
  # tf.equal and tf.reduce_any?

  # Shape of filled IDs is the same as `ids` with the last dim collapsed to 1.
  ids_shape = array_ops.shape(ids, out_type=dtypes.int64)
  ids_last_dim = array_ops.size(ids_shape) - 1
  filled_selected_id_shape = math_ops.reduced_shape(
      ids_shape, array_ops.reshape(ids_last_dim, [1]))

  # Intersect `ids` with the selected ID.
  filled_selected_id = array_ops.fill(
      filled_selected_id_shape, math_ops.to_int64(selected_id))
  result = set_ops.set_intersection(filled_selected_id, ids)
  return sparse_tensor.SparseTensor(
      indices=result.indices, values=result.values, shape=ids_shape)

def _reverse_seq(input_seq, lengths):
  """Reverse a list of Tensors up to specified lengths.

  Args:
    input_seq: Sequence of seq_len tensors of dimension (batch_size, depth)
    lengths:   A tensor of dimension batch_size, containing lengths for each
               sequence in the batch. If "None" is specified, simply reverses
               the list.

  Returns:
    time-reversed sequence
  """
  if lengths is None:
    return list(reversed(input_seq))

  input_shape = tensor_shape.matrix(None, None)
  for input_ in input_seq:
    input_shape.merge_with(input_.get_shape())
    input_.set_shape(input_shape)

  # Join into (time, batch_size, depth)
  s_joined = array_ops.pack(input_seq)

  # TODO(schuster, ebrevdo): Remove cast when reverse_sequence takes int32
  if lengths is not None:
    lengths = math_ops.to_int64(lengths)

  # Reverse along dimension 0
  s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
  # Split again into list
  result = array_ops.unpack(s_reversed)
  for r in result:
    r.set_shape(input_shape)
  return result

def _reverse_seq(input_seq, lengths):
  """Reverse a list of Tensors up to specified lengths.

  Args:
    input_seq: Sequence of seq_len tensors of dimension (batch_size, depth)
    lengths:   A tensor of dimension batch_size, containing lengths for each
               sequence in the batch. If "None" is specified, simply reverses
               the list.

  Returns:
    time-reversed sequence
  """
  if lengths is None:
    return list(reversed(input_seq))

  input_shape = tensor_shape.matrix(None, None)
  for input_ in input_seq:
    input_shape.merge_with(input_.get_shape())
    input_.set_shape(input_shape)

  # Join into (time, batch_size, depth)
  s_joined = array_ops.pack(input_seq)

  # TODO(schuster, ebrevdo): Remove cast when reverse_sequence takes int32
  if lengths is not None:
    lengths = math_ops.to_int64(lengths)

  # Reverse along dimension 0
  s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
  # Split again into list
  result = array_ops.unpack(s_reversed)
  for r in result:
    r.set_shape(input_shape)
  return result

def _reverse_seq(input_seq, lengths):
  """Reverse a list of Tensors up to specified lengths.

  Args:
    input_seq: Sequence of seq_len tensors of dimension (batch_size, n_features)
               or nested tuples of tensors.
    lengths:   A `Tensor` of dimension batch_size, containing lengths for each
               sequence in the batch. If "None" is specified, simply reverses
               the list.

  Returns:
    time-reversed sequence
  """
  if lengths is None:
    return list(reversed(input_seq))

  flat_input_seq = tuple(nest.flatten(input_) for input_ in input_seq)

  flat_results = [[] for _ in range(len(input_seq))]
  for sequence in zip(*flat_input_seq):
    input_shape = tensor_shape.unknown_shape(
        ndims=sequence[0].get_shape().ndims)
    for input_ in sequence:
      input_shape.merge_with(input_.get_shape())
      input_.set_shape(input_shape)

    # Join into (time, batch_size, depth)
    s_joined = array_ops.pack(sequence)

    # TODO(schuster, ebrevdo): Remove cast when reverse_sequence takes int32
    if lengths is not None:
      lengths = math_ops.to_int64(lengths)

    # Reverse along dimension 0
    s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
    # Split again into list
    result = array_ops.unpack(s_reversed)
    for r, flat_result in zip(result, flat_results):
      r.set_shape(input_shape)
      flat_result.append(r)

  results = [nest.pack_sequence_as(structure=input_, flat_sequence=flat_result)
             for input_, flat_result in zip(input_seq, flat_results)]
  return results

def get_classification_loss(logits, targets, softmax_loss_function=None):
  bucket_outputs = logits
  if softmax_loss_function is None:
    assert len(bucket_outputs) == len(targets) == 1
    # We need to make target an int64-tensor and set its shape.
    bucket_target = array_ops.reshape(math_ops.to_int64(targets[0]), [-1])
    crossent = nn_ops.sparse_softmax_cross_entropy_with_logits(bucket_outputs[0], bucket_target)
  else:
    assert len(bucket_outputs) == len(targets) == 1
    crossent = softmax_loss_function(bucket_outputs[0], targets[0])

  batch_size = array_ops.shape(targets[0])[0]
  loss = tf.reduce_sum(crossent) / math_ops.cast(batch_size, dtypes.float32)

  return loss

def _logits_to_predictions(self, logits):
    """See `_MultiClassHead`."""
    with ops.name_scope(None, "predictions", (logits,)):
      return {
          prediction_key.PredictionKey.LOGITS:
              logits,
          prediction_key.PredictionKey.PROBABILITIES:
              math_ops.sigmoid(
                  logits, name=prediction_key.PredictionKey.PROBABILITIES),
          prediction_key.PredictionKey.CLASSES:
              math_ops.to_int64(
                  math_ops.greater(logits, 0),
                  name=prediction_key.PredictionKey.CLASSES)
      }

def _build_estimator_for_export_tests(tmpdir):

  def _input_fn():
    iris = base.load_iris()
    return {
        'feature': constant_op.constant(
            iris.data, dtype=dtypes.float32)
    }, constant_op.constant(
        iris.target, shape=[150], dtype=dtypes.int32)

  feature_columns = [
      feature_column_lib.real_valued_column(
          'feature', dimension=4)
  ]

  est = linear.LinearRegressor(feature_columns)
  est.fit(input_fn=_input_fn, steps=20)

  feature_spec = feature_column_lib.create_feature_spec_for_parsing(
      feature_columns)
  serving_input_fn = input_fn_utils.build_parsing_serving_input_fn(feature_spec)

  # hack in an op that uses an asset, in order to test asset export.
  # this is not actually valid, of course.
  def serving_input_fn_with_asset():
    features, labels, inputs = serving_input_fn()

    vocab_file_name = os.path.join(tmpdir, 'my_vocab_file')
    vocab_file = gfile.GFile(vocab_file_name, mode='w')
    vocab_file.write(VOCAB_FILE_CONTENT)
    vocab_file.close()
    hashtable = lookup.HashTable(
        lookup.TextFileStringTableInitializer(vocab_file_name), 'x')
    features['bogus_lookup'] = hashtable.lookup(
        math_ops.to_int64(features['feature']))

    return input_fn_utils.InputFnOps(features, labels, inputs)

  return est, serving_input_fn_with_asset

def _to_dnn_input_layer(self,
                          input_tensor,
                          weight_collections=None,
                          trainable=True,
                          output_rank=2):
    if output_rank != 2:
      raise ValueError("BucketizedColumn currently only supports output_rank=2")
    return array_ops.reshape(
        array_ops.one_hot(
            math_ops.to_int64(input_tensor),
            self.length,
            1.,
            0.,
            name="one_hot"), [-1, self.length * self.source_column.dimension],
        name="reshape")

def to_sparse_tensor(self, input_tensor):
    """Creates a SparseTensor from the bucketized Tensor."""
    dimension = self.source_column.dimension
    batch_size = array_ops.shape(input_tensor, name="shape")[0]

    if dimension > 1:
      i1 = array_ops.reshape(
          array_ops.tile(
              array_ops.expand_dims(
                  math_ops.range(0, batch_size), 1, name="expand_dims"),
              [1, dimension],
              name="tile"), [-1],
          name="rehsape")
      i2 = array_ops.tile(
          math_ops.range(0, dimension), [batch_size], name="tile")
      # Flatten the bucket indices and unique them across dimensions
      # E.g. 2nd dimension indices will range from k to 2*k-1 with k buckets
      bucket_indices = array_ops.reshape(
          input_tensor, [-1], name="reshape") + self.length * i2
    else:
      # Simpler indices when dimension=1
      i1 = math_ops.range(0, batch_size)
      i2 = array_ops.zeros([batch_size], dtype=dtypes.int32, name="zeros")
      bucket_indices = array_ops.reshape(input_tensor, [-1], name="reshape")

    indices = math_ops.to_int64(array_ops.transpose(array_ops.stack((i1, i2))))
    shape = math_ops.to_int64(array_ops.stack([batch_size, dimension]))
    sparse_id_values = sparse_tensor_py.SparseTensor(
        indices, bucket_indices, shape)

    return sparse_id_values

def _define_distance_to_clusters(self, data):
    """Defines the Mahalanobis distance to the assigned Gaussian."""
    # TODO(xavigonzalvo): reuse (input - mean) * cov^-1 * (input -
    # mean) from log probability function.
    self._all_scores = []
    for shard in data:
      all_scores = []
      shard = array_ops.expand_dims(shard, 0)
      for c in xrange(self._num_classes):
        if self._covariance_type == FULL_COVARIANCE:
          cov = self._covs[c, :, :]
        elif self._covariance_type == DIAG_COVARIANCE:
          cov = array_ops.diag(self._covs[c, :])
        inverse = linalg_ops.matrix_inverse(cov + self._min_var)
        inv_cov = array_ops.tile(
            array_ops.expand_dims(inverse, 0),
            array_ops.stack([self._num_examples, 1, 1]))
        diff = array_ops.transpose(shard - self._means[c, :, :], perm=[1, 0, 2])
        m_left = math_ops.matmul(diff, inv_cov)
        all_scores.append(
            math_ops.sqrt(
                math_ops.matmul(
                    m_left, array_ops.transpose(
                        diff, perm=[0, 2, 1]))))
      self._all_scores.append(
          array_ops.reshape(
              array_ops.concat(all_scores, 1),
              array_ops.stack([self._num_examples, self._num_classes])))

    # Distance to the associated class.
    self._all_scores = array_ops.concat(self._all_scores, 0)
    assignments = array_ops.concat(self.assignments(), 0)
    rows = math_ops.to_int64(math_ops.range(0, self._num_examples))
    indices = array_ops.concat(
        [array_ops.expand_dims(rows, 1), array_ops.expand_dims(assignments, 1)],
        1)
    self._scores = array_ops.gather_nd(self._all_scores, indices)

def ndlstm_base_dynamic(inputs, noutput, scope=None, reverse=False):
  """Run an LSTM, either forward or backward.

  This is a 1D LSTM implementation using dynamic_rnn and
  the TensorFlow LSTM op.

  Args:
    inputs: input sequence (length, batch_size, ninput)
    noutput: depth of output
    scope: optional scope name
    reverse: run LSTM in reverse

  Returns:
    Output sequence (length, batch_size, noutput)
  """
  with variable_scope.variable_scope(scope, "SeqLstm", [inputs]):
    # TODO(tmb) make batch size, sequence_length dynamic
    # example: sequence_length = tf.shape(inputs)[0]
    _, batch_size, _ = _shape(inputs)
    lstm_cell = core_rnn_cell_impl.BasicLSTMCell(noutput, state_is_tuple=False)
    state = array_ops.zeros([batch_size, lstm_cell.state_size])
    sequence_length = int(inputs.get_shape()[0])
    sequence_lengths = math_ops.to_int64(
        array_ops.fill([batch_size], sequence_length))
    if reverse:
      inputs = array_ops.reverse_v2(inputs, [0])
    outputs, _ = rnn.dynamic_rnn(
        lstm_cell, inputs, sequence_lengths, state, time_major=True)
    if reverse:
      outputs = array_ops.reverse_v2(outputs, [0])
    return outputs

def sequence_loss_per_sample(logits,
                             targets,
                             weights):
  """TODO(nh2tran): docstring.
  Weighted cross-entropy loss for a sequence of logits (per example).

  Args:
    logits: List of 2D Tensors of shape [batch_size x num_decoder_symbols].
    targets: List of 1D batch-sized int32 Tensors of the same length as logits.
    weights: List of 1D batch-sized float-Tensors of the same length as logits.
    average_across_timesteps: If set, divide the returned cost by the total
      label weight.
    softmax_loss_function: Function (inputs-batch, labels-batch) -> loss-batch
      to be used instead of the standard softmax (the default if this is None).
    name: Optional name for this operation, default: "sequence_loss_by_example".

  Returns:
    1D batch-sized float Tensor: The log-perplexity for each sequence.

  Raises:
    ValueError: If len(logits) is different from len(targets) or len(weights).
  """

  #~ with tf.name_scope(name="sequence_loss_by_example",
                     #~ values=logits + targets + weights):
  with ops.op_scope(logits + targets + weights,
                    None,
                    "sequence_loss_by_example"):

    log_perp_list = []
    for logit, target, weight in zip(logits, targets, weights):
      target = array_ops.reshape(math_ops.to_int64(target), [-1])
      crossent = nn_ops.sparse_softmax_cross_entropy_with_logits(logits=logit,
                                                                 labels=target)
      log_perp_list.append(crossent * weight)
    log_perps = math_ops.add_n(log_perp_list)

    # average_across_timesteps:
    total_size = math_ops.add_n(weights)
    total_size += 1e-12  # Just to avoid division by 0 for all-0 weights.
    log_perps /= total_size

  return log_perps

def generate_task_output(encoder_outputs, additional_inputs, encoder_state, targets,sequence_length, num_decoder_symbols, weights,
                       buckets, softmax_loss_function=None,
                       per_example_loss=False, name=None, use_attention=False, scope=None, DNN_at_output=False, 
                       intent_results=None, 
                       tagging_results=None, 
                       train_with_true_label=True,
                       use_local_context=False,
                       forward_only=False):
  if len(targets) < buckets[-1][1]:
    raise ValueError("Length of targets (%d) must be at least that of last"
                     "bucket (%d)." % (len(targets), buckets[-1][1]))

  all_inputs = encoder_outputs + targets + weights
  with ops.op_scope(all_inputs, name, "model_with_buckets"):
    if scope == 'intent':
        logits, regularizers, sampled_intents = intent_results
        sampled_tags = list()
    elif scope == 'tagging':
        logits, regularizers, sampled_tags = tagging_results
        sampled_intents = list()
    elif scope == 'lm':
      with variable_scope.variable_scope(scope + "_generate_sequence_output", reuse=None):
        task_inputs = []
        if use_local_context:
          print ('lm task: use sampled_tag_intent_emb as local context')
          task_inputs = [array_ops.concat(1, [additional_input, encoder_output]) for additional_input, encoder_output in zip(additional_inputs, encoder_outputs)]
        else:
          task_inputs = encoder_outputs        

        logits, _, regularizers = generate_sequence_output(task_inputs, 
                                                            encoder_state,
                                                            num_decoder_symbols,
                                                            sequence_length,
                                                            use_attention=use_attention,
                                                            DNN_at_output=DNN_at_output,
                                                            forward_only=forward_only)

        sampled_tags = list()
        sampled_intents = list()             

    if per_example_loss is None:
      assert len(logits) == len(targets)
      # We need to make target and int64-tensor and set its shape.
      bucket_target = [array_ops.reshape(math_ops.to_int64(x), [-1]) for x in targets]
      crossent = sequence_loss_by_example(
            logits, bucket_target, weights,
            softmax_loss_function=softmax_loss_function)
    else:
      assert len(logits) == len(targets)
      bucket_target = [array_ops.reshape(math_ops.to_int64(x), [-1]) for x in targets]
      crossent = sequence_loss(
            logits, bucket_target, weights,
            softmax_loss_function=softmax_loss_function)
      crossent_with_regularizers = crossent + 1e-4 * regularizers

  return logits, sampled_tags, sampled_intents, crossent_with_regularizers, crossent