python类less()的实例源码

def _ndtr(x):
  """Implements ndtr core logic."""
  half_sqrt_2 = constant_op.constant(
      0.5 * math.sqrt(2.), dtype=x.dtype, name="half_sqrt_2")
  w = x * half_sqrt_2
  z = math_ops.abs(w)
  y = array_ops.where(math_ops.less(z, half_sqrt_2),
                      1. + math_ops.erf(w),
                      array_ops.where(math_ops.greater(w, 0.),
                                      2. - math_ops.erfc(z),
                                      math_ops.erfc(z)))
  return 0.5 * y

def testGetLogitsAndProbsProbabilityValidateArgs(self):
    p = [0.01, 0.2, 0.5, 0.7, .99]
    # Component less than 0.
    p2 = [-1, 0.2, 0.5, 0.3, .2]
    # Component greater than 1.
    p3 = [2, 0.2, 0.5, 0.3, .2]

    with self.test_session():
      _, prob = distribution_util.get_logits_and_probs(
          probs=p, validate_args=True)
      prob.eval()

      with self.assertRaisesOpError("Condition x >= 0"):
        _, prob = distribution_util.get_logits_and_probs(
            probs=p2, validate_args=True)
        prob.eval()

      _, prob = distribution_util.get_logits_and_probs(
          probs=p2, validate_args=False)
      prob.eval()

      with self.assertRaisesOpError("probs has components greater than 1"):
        _, prob = distribution_util.get_logits_and_probs(
            probs=p3, validate_args=True)
        prob.eval()

      _, prob = distribution_util.get_logits_and_probs(
          probs=p3, validate_args=False)
      prob.eval()

def testGetLogitsAndProbsProbabilityValidateArgsMultidimensional(self):
    p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32)
    # Component less than 0. Still sums to 1.
    p2 = np.array([[-.3, 0.4, 0.9], [0.1, 0.5, 0.4]], dtype=np.float32)
    # Component greater than 1. Does not sum to 1.
    p3 = np.array([[1.3, 0.0, 0.0], [0.1, 0.5, 0.4]], dtype=np.float32)
    # Does not sum to 1.
    p4 = np.array([[1.1, 0.3, 0.4], [0.1, 0.5, 0.4]], dtype=np.float32)

    with self.test_session():
      _, prob = distribution_util.get_logits_and_probs(
          probs=p, multidimensional=True)
      prob.eval()

      with self.assertRaisesOpError("Condition x >= 0"):
        _, prob = distribution_util.get_logits_and_probs(
            probs=p2, multidimensional=True, validate_args=True)
        prob.eval()

      _, prob = distribution_util.get_logits_and_probs(
          probs=p2, multidimensional=True, validate_args=False)
      prob.eval()

      with self.assertRaisesOpError(
          "(probs has components greater than 1|probs does not sum to 1)"):
        _, prob = distribution_util.get_logits_and_probs(
            probs=p3, multidimensional=True, validate_args=True)
        prob.eval()

      _, prob = distribution_util.get_logits_and_probs(
          probs=p3, multidimensional=True, validate_args=False)
      prob.eval()

      with self.assertRaisesOpError("probs does not sum to 1"):
        _, prob = distribution_util.get_logits_and_probs(
            probs=p4, multidimensional=True, validate_args=True)
        prob.eval()

      _, prob = distribution_util.get_logits_and_probs(
          probs=p4, multidimensional=True, validate_args=False)
      prob.eval()

def testLoops(self):
    """Tests that loops work on XLA devices."""

    with session_lib.Session() as session:
      x = array_ops.placeholder(dtypes.float32)
      with ops.device("device:XLA_CPU:0"):
        c = lambda i, _: math_ops.less(i, 5)
        b = lambda i, x: (i + 1, x * 2.0 + 1.0)
        _, y = control_flow_ops.while_loop(c, b, (constant_op.constant(0), x))

      result = session.run(y, {x: np.float32(2)})
      self.assertAllClose(result, np.float32(95), rtol=1e-3)

def MetadataHasXlaLaunch(run_metadata):
  """Returns true if there is a _XlaLaunch kernel in run_metadata's timeline."""

  # TODO(phawkins): find a less hacky way to test whether a kernel ran.
  return InLabels(RunMetadataLabels(run_metadata), "_XlaLaunch")

def testLoopDeadlock(self):
    """Regression test for bug that caused deadlocks in graphs with loops."""

    with self.test_session() as session:
      x = array_ops.placeholder(dtypes.float32)
      with jit_scope():
        y = x + 1.0
        c = lambda i, _x, _y: math_ops.less(i, 5)
        b = lambda i, x, _y: (i + 1, x * 2.0 + 1.0, x - 3.0)
        _, _, w = control_flow_ops.while_loop(c, b,
                                              (constant_op.constant(0), y, x))
        u = w + y
      result = session.run(u, {x: np.float32(2)})
      self.assertAllClose(result, np.float32(63), rtol=1e-1)

def training_graph(self, input_data, input_labels, data_spec=None,
                     epoch=None, **tree_kwargs):
    """Constructs a TF graph for training a random forest.

    Args:
      input_data: A tensor or SparseTensor or placeholder for input data.
      input_labels: A tensor or placeholder for labels associated with
        input_data.
      data_spec: A list of tf.dtype values specifying the original types of
        each column.
      epoch: A tensor or placeholder for the epoch the training data comes from.
      **tree_kwargs: Keyword arguments passed to each tree's training_graph.

    Returns:
      The last op in the random forest training graph.
    """
    data_spec = [constants.DATA_FLOAT] if data_spec is None else data_spec
    tree_graphs = []
    for i in range(self.params.num_trees):
      with ops.device(self.device_assigner.get_device(i)):
        seed = self.params.base_random_seed
        if seed != 0:
          seed += i
        # If using bagging, randomly select some of the input.
        tree_data = input_data
        tree_labels = input_labels
        if self.params.bagging_fraction < 1.0:
          # TODO(thomaswc): This does sampling without replacment.  Consider
          # also allowing sampling with replacement as an option.
          batch_size = array_ops.slice(array_ops.shape(input_data), [0], [1])
          r = random_ops.random_uniform(batch_size, seed=seed)
          mask = math_ops.less(
              r, array_ops.ones_like(r) * self.params.bagging_fraction)
          gather_indices = array_ops.squeeze(
              array_ops.where(mask), squeeze_dims=[1])
          # TODO(thomaswc): Calculate out-of-bag data and labels, and store
          # them for use in calculating statistics later.
          tree_data = array_ops.gather(input_data, gather_indices)
          tree_labels = array_ops.gather(input_labels, gather_indices)
        if self.params.bagged_features:
          tree_data = self._bag_features(i, tree_data)

        initialization = self.trees[i].tree_initialization()

        with ops.control_dependencies([initialization]):
          tree_graphs.append(
              self.trees[i].training_graph(
                  tree_data, tree_labels, seed, data_spec=data_spec,
                  epoch=([0] if epoch is None else epoch),
                  **tree_kwargs))

    return control_flow_ops.group(*tree_graphs, name='train')

def _conditional_batch(tensors, accept_prob, batch_size, queue_threads=10):
  """Conditionally enqueue tensors based on accept_prob.

  Specifically, enqueue the element if accept_prob > rand_unif([0, 1]).

  Args:
      tensors: List of tensors to enqueue.
      accept_prob: Acceptance probability per example.
      batch_size: Size of batch.
      queue_threads: Number of threads enqueuing in the final queue.

  Returns:
      List of batched tensors.

  Raises:
      ValueError: `accept_prob` isn't 0D.
  """
  accept_prob.get_shape().assert_has_rank(0)
  # Determine shapes and types of to-be-enqueued-tensors.
  shapes_list = []
  dtypes_list = []
  for tensor in tensors:
    cur_shape = tensor.get_shape()
    cur_shape.assert_is_fully_defined()
    shapes_list.append(cur_shape)
    dtypes_list.append(tensor.dtype)

  final_q = data_flow_ops.FIFOQueue(capacity=batch_size,
                                    shapes=shapes_list,
                                    dtypes=dtypes_list,
                                    name='batched_queue')
  logging_ops.scalar_summary('queue/%s/size' % final_q.name, final_q.size())

  # Conditionally enqueue.
  # Reshape enqueue op to match no_op's shape.
  eq_tf = math_ops.less(random_ops.random_uniform([]), accept_prob)
  conditional_enqueue = control_flow_ops.cond(
      eq_tf,
      lambda: final_q.enqueue(tensors),
      control_flow_ops.no_op)
  queue_runner.add_queue_runner(queue_runner.QueueRunner(
      final_q, [conditional_enqueue] * queue_threads))

  out_tensor = final_q.dequeue_many(batch_size)
  # Queues return a single tensor if the list of enqued tensors is one. Since we
  # want the type to be the same in all cases, always return a list.
  if isinstance(out_tensor, ops.Tensor):
    out_tensor = [out_tensor]

  return out_tensor

def training_graph(self, input_data, input_labels, data_spec=None,
                     epoch=None, **tree_kwargs):
    """Constructs a TF graph for training a random forest.

    Args:
      input_data: A tensor or SparseTensor or placeholder for input data.
      input_labels: A tensor or placeholder for labels associated with
        input_data.
      data_spec: A list of tf.dtype values specifying the original types of
        each column.
      epoch: A tensor or placeholder for the epoch the training data comes from.
      **tree_kwargs: Keyword arguments passed to each tree's training_graph.

    Returns:
      The last op in the random forest training graph.
    """
    data_spec = [constants.DATA_FLOAT] if data_spec is None else data_spec
    tree_graphs = []
    for i in range(self.params.num_trees):
      with ops.device(self.device_assigner.get_device(i)):
        seed = self.params.base_random_seed
        if seed != 0:
          seed += i
        # If using bagging, randomly select some of the input.
        tree_data = input_data
        tree_labels = input_labels
        if self.params.bagging_fraction < 1.0:
          # TODO(thomaswc): This does sampling without replacment.  Consider
          # also allowing sampling with replacement as an option.
          batch_size = array_ops.slice(array_ops.shape(input_data), [0], [1])
          r = random_ops.random_uniform(batch_size, seed=seed)
          mask = math_ops.less(
              r, array_ops.ones_like(r) * self.params.bagging_fraction)
          gather_indices = array_ops.squeeze(
              array_ops.where(mask), squeeze_dims=[1])
          # TODO(thomaswc): Calculate out-of-bag data and labels, and store
          # them for use in calculating statistics later.
          tree_data = array_ops.gather(input_data, gather_indices)
          tree_labels = array_ops.gather(input_labels, gather_indices)
        if self.params.bagged_features:
          tree_data = self._bag_features(i, tree_data)

        initialization = self.trees[i].tree_initialization()

        with ops.control_dependencies([initialization]):
          tree_graphs.append(
              self.trees[i].training_graph(
                  tree_data, tree_labels, seed, data_spec=data_spec,
                  epoch=([0] if epoch is None else epoch),
                  **tree_kwargs))

    return control_flow_ops.group(*tree_graphs, name='train')

def construct_rnn(initial_state,
                  sequence_input,
                  cell,
                  num_label_columns,
                  dtype=dtypes.float32,
                  parallel_iterations=32,
                  swap_memory=False):
  """Build an RNN and apply a fully connected layer to get the desired output.

  Args:
    initial_state: The initial state to pass the the RNN. If `None`, the
      default starting state for `self._cell` is used.
    sequence_input: A `Tensor` with shape `[batch_size, padded_length, d]`
      that will be passed as input to the RNN.
    cell: An initialized `RNNCell`.
    num_label_columns: The desired output dimension.
    dtype: dtype of `cell`.
    parallel_iterations: Number of iterations to run in parallel. Values >> 1
      use more memory but take less time, while smaller values use less memory
      but computations take longer.
    swap_memory: Transparently swap the tensors produced in forward inference
      but needed for back prop from GPU to CPU.  This allows training RNNs
      which would typically not fit on a single GPU, with very minimal (or no)
      performance penalty.
  Returns:
    activations: The output of the RNN, projected to `num_label_columns`
      dimensions.
    final_state: The final state output by the RNN.
  """
  with ops.name_scope('RNN'):
    rnn_outputs, final_state = rnn.dynamic_rnn(
        cell=cell,
        inputs=sequence_input,
        initial_state=initial_state,
        dtype=dtype,
        parallel_iterations=parallel_iterations,
        swap_memory=swap_memory,
        time_major=False)
    activations = layers.fully_connected(
        inputs=rnn_outputs,
        num_outputs=num_label_columns,
        activation_fn=None,
        trainable=True)
    return activations, final_state

def training_graph(self,
                     input_data,
                     input_labels,
                     data_spec=None,
                     **tree_kwargs):
    """Constructs a TF graph for training a random forest.

    Args:
      input_data: A tensor or SparseTensor or placeholder for input data.
      input_labels: A tensor or placeholder for labels associated with
        input_data.
      data_spec: A list of tf.dtype values specifying the original types of
        each column.
      **tree_kwargs: Keyword arguments passed to each tree's training_graph.

    Returns:
      The last op in the random forest training graph.
    """
    data_spec = [constants.DATA_FLOAT] if data_spec is None else data_spec
    tree_graphs = []
    for i in range(self.params.num_trees):
      with ops.device(self.device_assigner.get_device(i)):
        seed = self.params.base_random_seed
        if seed != 0:
          seed += i
        # If using bagging, randomly select some of the input.
        tree_data = input_data
        tree_labels = input_labels
        if self.params.bagging_fraction < 1.0:
          # TODO(thomaswc): This does sampling without replacment.  Consider
          # also allowing sampling with replacement as an option.
          batch_size = array_ops.slice(array_ops.shape(input_data), [0], [1])
          r = random_ops.random_uniform(batch_size, seed=seed)
          mask = math_ops.less(
              r, array_ops.ones_like(r) * self.params.bagging_fraction)
          gather_indices = array_ops.squeeze(
              array_ops.where(mask), squeeze_dims=[1])
          # TODO(thomaswc): Calculate out-of-bag data and labels, and store
          # them for use in calculating statistics later.
          tree_data = array_ops.gather(input_data, gather_indices)
          tree_labels = array_ops.gather(input_labels, gather_indices)
        if self.params.bagged_features:
          tree_data = self._bag_features(i, tree_data)

        initialization = self.trees[i].tree_initialization()

        with ops.control_dependencies([initialization]):
          tree_graphs.append(
              self.trees[i].training_graph(
                  tree_data, tree_labels, seed, data_spec=data_spec,
                  **tree_kwargs))

    return control_flow_ops.group(*tree_graphs, name='train')

def test_keyed_features_filter(self):
    gfile.Glob = self._orig_glob
    lines = [
        '{"features": {"feature": {"age": {"int64_list": {"value": [2]}}}}}',
        '{"features": {"feature": {"age": {"int64_list": {"value": [0]}}}}}',
        '{"features": {"feature": {"age": {"int64_list": {"value": [1]}}}}}',
        '{"features": {"feature": {"age": {"int64_list": {"value": [0]}}}}}',
        '{"features": {"feature": {"age": {"int64_list": {"value": [3]}}}}}',
        '{"features": {"feature": {"age": {"int64_list": {"value": [5]}}}}}'
    ]
    filename = self._create_temp_file("\n".join(lines))

    batch_size = 2
    queue_capacity = 4
    name = "my_batch"
    features = {"age": parsing_ops.FixedLenFeature([], dtypes_lib.int64)}

    def filter_fn(keys, examples_json):
      del keys
      serialized = parsing_ops.decode_json_example(examples_json)
      examples = parsing_ops.parse_example(serialized, features)
      return math_ops.less(examples["age"], 2)

    with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
      keys, inputs = graph_io._read_keyed_batch_examples_helper(
          filename,
          batch_size,
          reader=io_ops.TextLineReader,
          randomize_input=False,
          num_epochs=1,
          read_batch_size=batch_size,
          queue_capacity=queue_capacity,
          filter_fn=filter_fn,
          name=name)
      self.assertAllEqual((None,), keys.get_shape().as_list())
      self.assertAllEqual((None,), inputs.get_shape().as_list())
      session.run(variables.local_variables_initializer())

      coord = coordinator.Coordinator()
      threads = queue_runner_impl.start_queue_runners(session, coord=coord)
      # First batch of two filtered examples.
      out_keys, out_vals = session.run((keys, inputs))
      self.assertAllEqual(
          [filename.encode("utf-8") + b":2", filename.encode("utf-8") + b":3"],
          out_keys)
      self.assertAllEqual([lines[1].encode("utf-8"), lines[2].encode("utf-8")],
                          out_vals)

      # Second batch will only have one filtered example as that's the only
      # remaining example that satisfies the filtering criterion.
      out_keys, out_vals = session.run((keys, inputs))
      self.assertAllEqual([filename.encode("utf-8") + b":4"], out_keys)
      self.assertAllEqual([lines[3].encode("utf-8")], out_vals)

      # Exhausted input.
      with self.assertRaises(errors.OutOfRangeError):
        session.run((keys, inputs))

      coord.request_stop()
      coord.join(threads)

def pick_vector(cond,
                true_vector,
                false_vector,
                name="pick_vector"):
  """Picks possibly different length row `Tensor`s based on condition.

  Value `Tensor`s should have exactly one dimension.

  If `cond` is a python Boolean or `tf.constant` then either `true_vector` or
  `false_vector` is immediately returned. I.e., no graph nodes are created and
  no validation happens.

  Args:
    cond: `Tensor`. Must have `dtype=tf.bool` and be scalar.
    true_vector: `Tensor` of one dimension. Returned when cond is `True`.
    false_vector: `Tensor` of one dimension. Returned when cond is `False`.
    name: `String`. The name to give this op.

  Example:

  ```python
  pick_vector(tf.less(0, 5), tf.range(10, 12), tf.range(15, 18))
  # result is tensor: [10, 11].
  pick_vector(tf.less(5, 0), tf.range(10, 12), tf.range(15, 18))
  # result is tensor: [15, 16, 17].

def softplus_inverse(x, name=None):
  """Computes the inverse softplus, i.e., x = softplus_inverse(softplus(x)).

  Mathematically this op is equivalent to:

  ```none
  softplus_inverse = log(exp(x) - 1.)