Python math_ops.to_int64函数代码示例

  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 generate_sequence_output(num_encoder_symbols,
                       encoder_outputs, encoder_state, targets,sequence_length, num_decoder_symbols, weights,
                       buckets, softmax_loss_function=None,
                       per_example_loss=False, name=None, use_attention=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"):
    with variable_scope.variable_scope("decoder_sequence_output", reuse=None):
      logits, attention_weights = attention_RNN(encoder_outputs,
                                                encoder_state,
                                                num_decoder_symbols,
                                                sequence_length,
                                                use_attention=use_attention)
      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)

  return logits, crossent

  def to_weighted_sum(self,
                      input_tensor,
                      num_outputs=1,
                      weight_collections=None,
                      trainable=True):
    """Returns a Tensor as linear predictions and a list of created Variable."""
    dimension = self.source_column.dimension
    batch_size = array_ops.shape(input_tensor)[0]

    if dimension > 1:
      i1 = array_ops.reshape(array_ops.tile(array_ops.expand_dims(
          math_ops.range(0, batch_size), 1), [1, dimension]), [-1])
      i2 = array_ops.tile(math_ops.range(0, dimension), [batch_size])
      # 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
      # TODO(chapelle): move that logic to insert_transformed_feature to ensure
      #   unique buckets across dimensions after crossing.
      bucket_indices = array_ops.reshape(input_tensor, [-1]) + 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)
      bucket_indices = array_ops.reshape(input_tensor, [-1])

    indices = math_ops.to_int64(array_ops.transpose(array_ops.pack((i1, i2))))
    shape = math_ops.to_int64(array_ops.pack([batch_size, 1]))
    sparse_id_values = ops.SparseTensor(indices, bucket_indices, shape)
    vocab_size = self.length * self.source_column.dimension

    return _create_embedding_lookup(
        sparse_id_values, vocab_size, num_outputs,
        _add_variable_collection(weight_collections), 0., "sum",
        trainable, self.name + "_weights")

  def _call_cell(self,
                 inputs,
                 initial_cell_state=None,
                 initial_output=None,
                 dtype=None,
                 sequence_length=None):
    """Run this LSTM on inputs, starting from the given state.

    Args:
      inputs: `3-D` tensor with shape `[time_len, batch_size, input_size]`
      initial_cell_state: initial value for cell state, shape `[batch_size,
        self._num_units]`
      initial_output: initial value of cell output, shape `[batch_size,
        self._num_units]`
      dtype: The data type for the initial state and expected output.
      sequence_length: Specifies the length of each sequence in inputs. An
        `int32` or `int64` vector (tensor) size `[batch_size]`, values in `[0,
        time_len)` or None.

    Returns:
      A pair containing:

      - Cell state (cs): A `3-D` tensor of shape `[time_len, batch_size,
                         output_size]`
      - Output (h): A `3-D` tensor of shape `[time_len, batch_size,
                    output_size]`
    """

    inputs_shape = inputs.get_shape().with_rank(3)
    time_len = inputs_shape[0].value
    if time_len is None:
      time_len = array_ops.shape(inputs)[0]

    if self._use_peephole:
      wci = self._w_i_diag
      wco = self._w_o_diag
      wcf = self._w_f_diag
    else:
      wci = wcf = wco = array_ops.zeros([self._num_units], dtype=dtype)

    if sequence_length is None:
      max_seq_len = math_ops.to_int64(time_len)
    else:
      max_seq_len = math_ops.to_int64(math_ops.reduce_max(sequence_length))

    _, cs, _, _, _, _, h = gen_lstm_ops.block_lstm(
        seq_len_max=max_seq_len,
        x=inputs,
        cs_prev=initial_cell_state,
        h_prev=initial_output,
        w=self._kernel,
        wci=wci,
        wcf=wcf,
        wco=wco,
        b=self._bias,
        forget_bias=self._forget_bias,
        cell_clip=self._cell_clip,
        use_peephole=self._use_peephole)
    return cs, h

def sparse_feature_cross(inputs, hashed_output=False, num_buckets=0,
                         name=None):
  """Crosses a list of Tensor or SparseTensor objects.

  See sparse_cross_op.cc for more details.

  Args:
    inputs: List of `SparseTensor` or `Tensor` to be crossed.
    hashed_output: If true, returns the hash of the cross instead of the string.
      This will allow us avoiding string manipulations.
    num_buckets: It is used if hashed_output is true.
      output = hashed_value%num_buckets if num_buckets > 0 else hashed_value.
    name: A name prefix for the returned tensors (optional).

  Returns:
    A `SparseTensor` with the crossed features.
    Return type is string if hashed_output=False, int64 otherwise.

  Raises:
    TypeError: If the inputs aren't either SparseTensor or Tensor.
  """
  if not isinstance(inputs, list):
    raise TypeError("Inputs must be a list")
  if not all(isinstance(i, ops.SparseTensor) or
             isinstance(i, ops.Tensor) for i in inputs):
    raise TypeError("All inputs must be SparseTensors")

  sparse_inputs = [i for i in inputs if isinstance(i, ops.SparseTensor)]
  dense_inputs = [i for i in inputs if not isinstance(i, ops.SparseTensor)]

  indices = [sp_input.indices for sp_input in sparse_inputs]
  values = [sp_input.values for sp_input in sparse_inputs]
  shapes = [sp_input.shape for sp_input in sparse_inputs]
  out_type = dtypes.int64 if hashed_output else dtypes.string

  internal_type = dtypes.string
  for i in range(len(values)):
    if values[i].dtype != dtypes.string:
      values[i] = math_ops.to_int64(values[i])
      internal_type = dtypes.int64
  for i in range(len(dense_inputs)):
    if dense_inputs[i].dtype != dtypes.string:
      dense_inputs[i] = math_ops.to_int64(dense_inputs[i])
      internal_type = dtypes.int64

  indices_out, values_out, shape_out = (
      _sparse_feature_cross_op.sparse_feature_cross(indices,
                                                    values,
                                                    shapes,
                                                    dense_inputs,
                                                    hashed_output,
                                                    num_buckets,
                                                    out_type=out_type,
                                                    internal_type=internal_type,
                                                    name=name))
  return ops.SparseTensor(indices_out, values_out, shape_out)

  def testDistribution(self, initial_known):
    classes = np.random.randint(5, size=(20000,))  # Uniformly sampled
    target_dist = [0.9, 0.05, 0.05, 0.0, 0.0]
    initial_dist = [0.2] * 5 if initial_known else None
    classes = math_ops.to_int64(classes)  # needed for Windows build.
    dataset = dataset_ops.Dataset.from_tensor_slices(classes).shuffle(
        200, seed=21).map(lambda c: (c, string_ops.as_string(c))).repeat()

    get_next = dataset.apply(
        resampling.rejection_resample(
            target_dist=target_dist,
            initial_dist=initial_dist,
            class_func=lambda c, _: c,
            seed=27)).make_one_shot_iterator().get_next()

    with self.cached_session() as sess:
      returned = []
      while len(returned) < 4000:
        returned.append(sess.run(get_next))

    returned_classes, returned_classes_and_data = zip(*returned)
    _, returned_data = zip(*returned_classes_and_data)
    self.assertAllEqual([compat.as_bytes(str(c))
                         for c in returned_classes], returned_data)
    total_returned = len(returned_classes)
    class_counts = np.array([
        len([True for v in returned_classes if v == c])
        for c in range(5)])
    returned_dist = class_counts / total_returned
    self.assertAllClose(target_dist, returned_dist, atol=1e-2)

  def make_splits(self, stamp_token, next_stamp_token, class_id):
    """Create the best split using the accumulated stats and flush the state."""
    # Get the aggregated gradients and hessians per <partition_id, feature_id>
    # pair.
    num_minibatches, partition_ids, feature_ids, gradients, hessians = (
        self._stats_accumulator.flush(stamp_token, next_stamp_token))
    # For sum_reduction, we don't need to divide by number of minibatches.

    num_minibatches = control_flow_ops.cond(
        ops.convert_to_tensor(self._loss_uses_sum_reduction),
        lambda: math_ops.to_int64(1), lambda: num_minibatches)
    partition_ids, gains, split_infos = (
        split_handler_ops.build_categorical_equality_splits(
            num_minibatches=num_minibatches,
            partition_ids=partition_ids,
            feature_ids=feature_ids,
            gradients=gradients,
            hessians=hessians,
            class_id=class_id,
            feature_column_group_id=self._feature_column_group_id,
            l1_regularization=self._l1_regularization,
            l2_regularization=self._l2_regularization,
            tree_complexity_regularization=self._tree_complexity_regularization,
            min_node_weight=self._min_node_weight,
            bias_feature_id=_BIAS_FEATURE_ID,
            multiclass_strategy=self._multiclass_strategy,
            weak_learner_type=self._weak_learner_type))
    # There are no warm-up rounds needed in the equality column handler. So we
    # always return ready.
    are_splits_ready = constant_op.constant(True)
    return (are_splits_ready, partition_ids, gains, split_infos)

 def _process_labels(self, labels):
   if labels is None:
     raise ValueError(
         'You must provide a labels Tensor. Given: None. '
         'Suggested troubleshooting steps: Check that your data contain '
         'your label feature. Check that your input_fn properly parses and '
         'returns labels.')
   if isinstance(labels, sparse_tensor.SparseTensor):
     if labels.dtype == dtypes.string:
       label_ids_values = lookup_ops.index_table_from_tensor(
           vocabulary_list=tuple(self._label_vocabulary),
           name='class_id_lookup').lookup(labels.values)
       label_ids = sparse_tensor.SparseTensor(
           indices=labels.indices,
           values=label_ids_values,
           dense_shape=labels.dense_shape)
     else:
       label_ids = labels
     return math_ops.to_int64(
         sparse_ops.sparse_to_indicator(label_ids, self._n_classes))
   msg = ('labels shape must be [batch_size, {}]. '
          'Given: ').format(self._n_classes)
   labels_shape = array_ops.shape(labels)
   check_rank_op = control_flow_ops.Assert(
       math_ops.equal(array_ops.rank(labels), 2),
       data=[msg, labels_shape])
   check_label_dim = control_flow_ops.Assert(
       math_ops.equal(labels_shape[-1], self._n_classes),
       data=[msg, labels_shape])
   with ops.control_dependencies([check_rank_op, check_label_dim]):
     return array_ops.identity(labels)

    def Forward(*args):
      """Forward pass of the recurrent net."""
      theta, state0, inputs, max_input_length, extras = _Pack(args, forward_sig)

      slen_dim = _SeqLenDim(inputs)

      # Creates accumulators for state0 and extras.
      acc_state = _EmptyAcc(slen_dim, state0)
      acc_extras = _EmptyAcc(slen_dim, extras)

      t = slen_dim - max_input_length if self._aligned_end else 0
      dev_t = math_ops.to_int32(t) if use_tpu else math_ops.to_int64(t)
      run = functional_ops.For(
          start=t,
          limit=slen_dim if self._aligned_end else max_input_length,
          delta=1,
          inputs=[dev_t] + _Flatten(
              [theta, state0, inputs, acc_state, acc_extras]),
          body=ForwardLoopBody,
          rewrite_with_while=compiled)
      _, state1, _, acc_state, acc_extras = _Pack(
          run[1:],
          [self._theta, self._state, self._inputs, self._state, self._extras])

      return _Flatten([acc_state, state1, acc_extras])

 def split(self, value, lengths, name=None):
   """See TensorArray."""
   with ops.name_scope(name, "TensorArraySplit", [self._flow, value, lengths]):
     value = ops.convert_to_tensor(value, name="value")
     lengths_64 = math_ops.to_int64(lengths)
     if self._infer_shape and not context.executing_eagerly():
       clengths = tensor_util.constant_value(lengths_64)
       if value.shape.dims is not None:
         if clengths is not None and clengths.max() == clengths.min():
           self._merge_element_shape(
               tensor_shape.TensorShape([clengths[0]]).concatenate(
                   value.shape[1:]))
     flow_out = list_ops.tensor_list_split(
         tensor=value,
         lengths=lengths_64,
         element_shape=self._element_shape[0] if self._element_shape else None,
         name=name)
     ta = TensorArray(
         dtype=self._dtype,
         handle=self.handle,
         flow=flow_out,
         colocate_with_first_write_call=self._colocate_with_first_write_call)
     ta._infer_shape = self._infer_shape
     ta._element_shape = self._element_shape
     ta._colocate_with = self._colocate_with
     return ta

  def _call_cell(self, inputs, initial_cell_state, initial_output, dtype,
                 sequence_length):
    """Run this LSTM on inputs, starting from the given state.

    Args:
      inputs: `3-D` tensor with shape `[time_len x batch_size x input_size]`
      initial_cell_state: initial value for cell state, shape `[batch_size,
        self._num_units]`
      initial_output: initial value of cell output, shape `[batch_size,
        self._num_units]`
      dtype: The data type for the initial state and expected output.
      sequence_length: Specifies the length of each sequence in inputs. An int32
        or int64 vector (tensor) size [batch_size], values in [0, time_len) or
          None.

    Returns:
      A pair containing:
      - Cell state (cs): A `3-D` tensor of shape `[time_len x batch_size x
                         output_size]`
      - Output (h): A `3-D` tensor of shape `[time_len x batch_size x
                    output_size]`
    """

    inputs_shape = inputs.get_shape().with_rank(3)
    time_len = inputs_shape[0].value
    if time_len is None:
      time_len = array_ops.shape(inputs)[0]
    input_size = inputs_shape[2].value
    w = vs.get_variable(
        "W_0", [input_size + self._num_units, self._num_units * 4], dtype=dtype)
    b = vs.get_variable(
        "B", [w.get_shape().with_rank(2)[1]],
        initializer=init_ops.constant_initializer(0.0),
        dtype=dtype)
    if self._use_peephole:
      wci = vs.get_variable("W_I_diag", [self._num_units], dtype=dtype)
      wco = vs.get_variable("W_O_diag", [self._num_units], dtype=dtype)
      wcf = vs.get_variable("W_F_diag", [self._num_units], dtype=dtype)
    else:
      wci = wco = wcf = array_ops.zeros([self._num_units], dtype=dtype)

    if sequence_length is None:
      max_seq_len = time_len
    else:
      max_seq_len = math_ops.to_int64(math_ops.reduce_max(sequence_length))

    _, cs, _, _, _, _, h = _lstm_ops_so.block_lstm(
        seq_len_max=max_seq_len,
        x=inputs,
        cs_prev=initial_cell_state,
        h_prev=initial_output,
        w=w,
        wci=wci,
        wco=wco,
        wcf=wcf,
        b=b,
        forget_bias=self._forget_bias,
        cell_clip=self._cell_clip,
        use_peephole=self._use_peephole)
    return cs, h

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 _select_last_activations(activations, sequence_lengths):
  """Selects the nth set of activations for each n in `sequence_length`.

  Returns a `Tensor` of shape `[batch_size, k]`. If `sequence_length` is not
  `None`, then `output[i, :] = activations[i, sequence_length[i] - 1, :]`. If
  `sequence_length` is `None`, then `output[i, :] = activations[i, -1, :]`.

  Args:
    activations: A `Tensor` with shape `[batch_size, padded_length, k]`.
    sequence_lengths: A `Tensor` with shape `[batch_size]` or `None`.
  Returns:
    A `Tensor` of shape `[batch_size, k]`.
  """
  with ops.name_scope(
      'select_last_activations', values=[activations, sequence_lengths]):
    activations_shape = array_ops.shape(activations)
    batch_size = activations_shape[0]
    padded_length = activations_shape[1]
    output_units = activations_shape[2]
    if sequence_lengths is None:
      sequence_lengths = padded_length
    start_indices = math_ops.to_int64(
        math_ops.range(batch_size) * padded_length)
    last_indices = start_indices + sequence_lengths - 1
    reshaped_activations = array_ops.reshape(
        activations, [batch_size * padded_length, output_units])

    last_activations = array_ops.gather(reshaped_activations, last_indices)
    last_activations.set_shape([activations.shape[0], activations.shape[2]])
    return last_activations

def generate_single_output(encoder_state, attention_states, sequence_length, targets, num_classes, buckets,
                       use_mean_attention=False,
                       softmax_loss_function=None, per_example_loss=False, name=None, use_attention=False):
  all_inputs = targets
  with ops.op_scope(all_inputs, name, "model_with_buckets"):
    with variable_scope.variable_scope(variable_scope.get_variable_scope(),
                                       reuse=None):
      bucket_attention_states, bucket_attn_weights, bucket_attns, bucket_outputs = attention_single_output_decoder(
                                                                                        encoder_state, attention_states, output_size=num_classes,
                                                                                        num_heads=1,
                                                                                        sequence_length=sequence_length,
                                                                                        initial_state_attention=True,
                                                                                        use_attention=use_attention)

      if softmax_loss_function is None:
        assert len(bucket_outputs) == len(targets) == 1
        # We need to make target and 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 bucket_outputs, loss

def index_to_string_table_from_tensor(vocabulary_list,
                                      default_value="UNK",
                                      name=None):
  """Returns a lookup table that maps a `Tensor` of indices into strings.

  This operation constructs a lookup table to map int64 indices into string
  values. The mapping is initialized from a string `mapping` 1-D `Tensor` where
  each element is a value and the corresponding index within the tensor is the
  key.

  Any input which does not have a corresponding index in 'mapping'
  (an out-of-vocabulary entry) is assigned the `default_value`

  The underlying table must be initialized by calling
  `tf.tables_initializer.run()` or `table.init.run()` once.

  Elements in `mapping` cannot have duplicates, otherwise when executing the
  table initializer op, it will throw a `FailedPreconditionError`.

  Sample Usages:

  ```python
  vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
  indices = tf.constant([1, 5], tf.int64)
  table = tf.contrib.lookup.index_to_string_table_from_tensor(
      vocabulary_list, default_value="UNKNOWN")
  values = table.lookup(indices)
  ...
  tf.tables_initializer().run()

  values.eval() ==> ["lake", "UNKNOWN"]
  ```

  Args:
    vocabulary_list: A 1-D string `Tensor` that specifies the strings to map
      from indices.
    default_value: The value to use for out-of-vocabulary indices.
    name: A name for this op (optional).

  Returns:
    The lookup table to map a string values associated to a given index `int64`
    `Tensors`.

  Raises:
    ValueError: when `vocabulary_list` is not set.
  """

  if vocabulary_list is None:
    raise ValueError("vocabulary_list must be specified.")

  with ops.name_scope(name, "index_to_string") as scope:
    vocabulary_list = ops.convert_to_tensor(vocabulary_list, dtypes.string)
    num_elements = array_ops.size(vocabulary_list)
    keys = math_ops.to_int64(math_ops.range(num_elements))

    shared_name = ""
    init = KeyValueTensorInitializer(
        keys, vocabulary_list, dtypes.int64, dtypes.string, name="table_init")
    # TODO(yleon): Use a more effienct structure.
    return HashTable(init, default_value, shared_name=shared_name, name=scope)