tensorflow_probability/python/optimizer/nelder_mead.py
def _prepare_args_with_initial_vertex(objective_function,
                                      initial_vertex,
                                      step_sizes,
                                      objective_at_initial_vertex,
                                      batch_evaluate_objective):
  """Constructs a standard axes aligned simplex."""
  dim = tf.size(input=initial_vertex)
  num_vertices = dim + 1
  unit_vectors_along_axes = tf.reshape(
      tf.eye(dim, dim, dtype=initial_vertex.dtype.base_dtype),
      tf.concat([[dim], tf.shape(input=initial_vertex)], axis=0))

  # If step_sizes does not broadcast to initial_vertex, the multiplication
  # in the second term will fail.
  simplex_face = initial_vertex + step_sizes * unit_vectors_along_axes
  simplex = tf.concat([tf.expand_dims(initial_vertex, axis=0),
                       simplex_face], axis=0)
  num_evaluations = 0
  # Evaluate the objective function at the simplex vertices.
  if objective_at_initial_vertex is None:
    objective_at_initial_vertex = objective_function(initial_vertex)
    num_evaluations += 1

  objective_at_simplex_face, num_evals = _evaluate_objective_multiple(
      objective_function, simplex_face, batch_evaluate_objective)
  num_evaluations += num_evals

  objective_at_simplex = tf.concat(
      [
          tf.expand_dims(objective_at_initial_vertex, axis=0),
          objective_at_simplex_face
      ], axis=0)

  return (dim,
          num_vertices,
          simplex,
          objective_at_simplex,
          num_evaluations)
def _prepare_args_with_initial_vertex(objective_function,
                                      initial_vertex,
                                      step_sizes,
                                      objective_at_initial_vertex,
                                      batch_evaluate_objective):
  """Constructs a standard axes aligned simplex."""
  dim = tf.size(input=initial_vertex)
  num_vertices = dim + 1
  unit_vectors_along_axes = tf.reshape(
      tf.eye(dim, dim, dtype=initial_vertex.dtype.base_dtype),
      tf.concat([[dim], tf.shape(input=initial_vertex)], axis=0))

  # If step_sizes does not broadcast to initial_vertex, the multiplication
  # in the second term will fail.
  simplex_face = initial_vertex + step_sizes * unit_vectors_along_axes
  simplex = tf.concat([tf.expand_dims(initial_vertex, axis=0),
                       simplex_face], axis=0)
  num_evaluations = 0
  # Evaluate the objective function at the simplex vertices.
  if objective_at_initial_vertex is None:
    objective_at_initial_vertex = objective_function(initial_vertex)
    num_evaluations += 1

  objective_at_simplex_face, num_evals = _evaluate_objective_multiple(
      objective_function, simplex_face, batch_evaluate_objective)
  num_evaluations += num_evals

  objective_at_simplex = tf.concat(
      [
          tf.expand_dims(objective_at_initial_vertex, axis=0),
          objective_at_simplex_face
      ], axis=0)

  return (dim,
          num_vertices,
          simplex,
          objective_at_simplex,
          num_evaluations)
def _prepare_args_with_initial_vertex ( objective_function , initial_vertex , step_sizes , objective_at_initial_vertex , batch_evaluate_objective ) : dim = tf . size ( input = initial_vertex ) num_vertices = dim + 1 unit_vectors_along_axes = tf . reshape ( tf . eye ( dim , dim , dtype = initial_vertex . dtype . base_dtype ) , tf . concat ( [ [ dim ] , tf . shape ( input = initial_vertex ) ] , axis = 0 ) ) # If step_sizes does not broadcast to initial_vertex, the multiplication # in the second term will fail. simplex_face = initial_vertex + step_sizes * unit_vectors_along_axes simplex = tf . concat ( [ tf . expand_dims ( initial_vertex , axis = 0 ) , simplex_face ] , axis = 0 ) num_evaluations = 0 # Evaluate the objective function at the simplex vertices. if objective_at_initial_vertex is None : objective_at_initial_vertex = objective_function ( initial_vertex ) num_evaluations += 1 objective_at_simplex_face , num_evals = _evaluate_objective_multiple ( objective_function , simplex_face , batch_evaluate_objective ) num_evaluations += num_evals objective_at_simplex = tf . concat ( [ tf . expand_dims ( objective_at_initial_vertex , axis = 0 ) , objective_at_simplex_face ] , axis = 0 ) return ( dim , num_vertices , simplex , objective_at_simplex , num_evaluations )
Constructs a standard axes aligned simplex.
Constructs a standard axes aligned simplex .