tensorflow_probability/python/sts/fitting.py
def _minimize_in_graph(build_loss_fn, num_steps=200, optimizer=None):
  """Run an optimizer within the graph to minimize a loss function."""
  optimizer = tf.compat.v1.train.AdamOptimizer(
      0.1) if optimizer is None else optimizer

  def train_loop_body(step):
    train_op = optimizer.minimize(
        build_loss_fn if tf.executing_eagerly() else build_loss_fn())
    return tf.tuple(tensors=[tf.add(step, 1)], control_inputs=[train_op])

  minimize_op = tf.compat.v1.while_loop(
      cond=lambda step: step < num_steps,
      body=train_loop_body,
      loop_vars=[tf.constant(0)],
      return_same_structure=True)[0]  # Always return a single op.
  return minimize_op
def _minimize_in_graph(build_loss_fn, num_steps=200, optimizer=None):
  optimizer = tf.compat.v1.train.AdamOptimizer(
      0.1) if optimizer is None else optimizer

  def train_loop_body(step):
    train_op = optimizer.minimize(
        build_loss_fn if tf.executing_eagerly() else build_loss_fn())
    return tf.tuple(tensors=[tf.add(step, 1)], control_inputs=[train_op])

  minimize_op = tf.compat.v1.while_loop(
      cond=lambda step: step < num_steps,
      body=train_loop_body,
      loop_vars=[tf.constant(0)],
      return_same_structure=True)[0]  # Always return a single op.
  return minimize_op
def _minimize_in_graph ( build_loss_fn , num_steps = 200 , optimizer = None ) : optimizer = tf . compat . v1 . train . AdamOptimizer ( 0.1 ) if optimizer is None else optimizer def train_loop_body ( step ) : train_op = optimizer . minimize ( build_loss_fn if tf . executing_eagerly ( ) else build_loss_fn ( ) ) return tf . tuple ( tensors = [ tf . add ( step , 1 ) ] , control_inputs = [ train_op ] ) minimize_op = tf . compat . v1 . while_loop ( cond = lambda step : step < num_steps , body = train_loop_body , loop_vars = [ tf . constant ( 0 ) ] , return_same_structure = True ) [ 0 ] # Always return a single op. return minimize_op
Run an optimizer within the graph to minimize a loss function.
Run an optimizer within the graph to minimize a loss function .