parameters: (self, session=None, feed_dict=None, fetches=None,
                 step_callback=None, loss_callback=None, grad_callback=None)
docstring: Minimize a scalar `Tensor`.
  
      Variables subject to optimization are updated in-place at the end of
      optimization.
  
      Note that this method does *not* just return a minimization `Op`, unlike
      `Optimizer.minimize()`; instead it actually performs minimization by
      executing commands to control a `Session`.
  
      Args:
        session: A `Session` instance.
        feed_dict: A feed dict to be passed to calls to `session.run`.
        fetches: A list of `Tensor`s to fetch and supply to `loss_callback` and
          `grad_callback` as positional arguments.
        step_callback: A function to be called at each optimization step;
          arguments are the current values of all optimization variables
          flattened into a single vector.
        loss_callback: A function to be called every time the loss and gradients
          are computed, with evaluated fetches supplied as positional arguments.
        grad_callback: Deprecated.
docstring_summary: Minimize a scalar `Tensor`.
docstring_tokens: Minimize a scalar Tensor .
function: def minimize(self, session=None, feed_dict=None, fetches=None,
                 step_callback=None, loss_callback=None, grad_callback=None):
      """Minimize a scalar `Tensor`.
  
      Variables subject to optimization are updated in-place at the end of
      optimization.
  
      Note that this method does *not* just return a minimization `Op`, unlike
      `Optimizer.minimize()`; instead it actually performs minimization by
      executing commands to control a `Session`.
  
      Args:
        session: A `Session` instance.
        feed_dict: A feed dict to be passed to calls to `session.run`.
        fetches: A list of `Tensor`s to fetch and supply to `loss_callback` and
          `grad_callback` as positional arguments.
        step_callback: A function to be called at each optimization step;
          arguments are the current values of all optimization variables
          flattened into a single vector.
        loss_callback: A function to be called every time the loss and gradients
          are computed, with evaluated fetches supplied as positional arguments.
        grad_callback: Deprecated.
      """
      session = session or ops.get_default_session()
      feed_dict = feed_dict or {}
      fetches = fetches or []
  
      loss_callback = loss_callback or (lambda *fetches: None)
      step_callback = step_callback or (lambda xk: None)
      # TODO(chapelle): Remove grad_callback (b/30590858)
      if grad_callback:
        logging.warn('grad_callback is deprecated. Please use loss_callback.')
  
      # Construct loss function and associated gradient.
      loss_grad_func = self._make_eval_func(
          [self._loss, self._packed_loss_grad],
          session, feed_dict, fetches, loss_callback)
  
      # Construct equality constraint functions and associated gradients.
      equality_funcs = self._make_eval_funcs(
          self._equalities, session, feed_dict, fetches)
      equality_grad_funcs = self._make_eval_funcs(
          self._packed_equality_grads, session, feed_dict, fetches)
  
      # Construct inequality constraint functions and associated gradients.
      inequality_funcs = self._make_eval_funcs(
          self._inequalities, session, feed_dict, fetches)
      inequality_grad_funcs = self._make_eval_funcs(
          self._packed_inequality_grads, session, feed_dict, fetches)
  
      # Get initial value from TF session.
      initial_packed_var_val = session.run(self._packed_var)
  
      # Perform minimization.
      packed_var_val = self._minimize(
          initial_val=initial_packed_var_val, loss_grad_func=loss_grad_func,
          equality_funcs=equality_funcs,
          equality_grad_funcs=equality_grad_funcs,
          inequality_funcs=inequality_funcs,
          inequality_grad_funcs=inequality_grad_funcs,
          step_callback=step_callback, optimizer_kwargs=self.optimizer_kwargs)
      var_vals = [packed_var_val[packing_slice]
                  for packing_slice in self._packing_slices]
  
      # Set optimization variables to their new values.
      session.run(self._var_updates,
                  feed_dict=dict(zip(self._update_placeholders, var_vals)))
function_tokens: def minimize ( self , session = None , feed_dict = None , fetches = None , step_callback = None , loss_callback = None , grad_callback = None ) : session = session or ops . get_default_session ( ) feed_dict = feed_dict or { } fetches = fetches or [ ] loss_callback = loss_callback or ( lambda * fetches : None ) step_callback = step_callback or ( lambda xk : None ) # TODO(chapelle): Remove grad_callback (b/30590858) if grad_callback : logging . warn ( 'grad_callback is deprecated. Please use loss_callback.' ) # Construct loss function and associated gradient. loss_grad_func = self . _make_eval_func ( [ self . _loss , self . _packed_loss_grad ] , session , feed_dict , fetches , loss_callback ) # Construct equality constraint functions and associated gradients. equality_funcs = self . _make_eval_funcs ( self . _equalities , session , feed_dict , fetches ) equality_grad_funcs = self . _make_eval_funcs ( self . _packed_equality_grads , session , feed_dict , fetches ) # Construct inequality constraint functions and associated gradients. inequality_funcs = self . _make_eval_funcs ( self . _inequalities , session , feed_dict , fetches ) inequality_grad_funcs = self . _make_eval_funcs ( self . _packed_inequality_grads , session , feed_dict , fetches ) # Get initial value from TF session. initial_packed_var_val = session . run ( self . _packed_var ) # Perform minimization. packed_var_val = self . _minimize ( initial_val = initial_packed_var_val , loss_grad_func = loss_grad_func , equality_funcs = equality_funcs , equality_grad_funcs = equality_grad_funcs , inequality_funcs = inequality_funcs , inequality_grad_funcs = inequality_grad_funcs , step_callback = step_callback , optimizer_kwargs = self . optimizer_kwargs ) var_vals = [ packed_var_val [ packing_slice ] for packing_slice in self . _packing_slices ] # Set optimization variables to their new values. session . run ( self . _var_updates , feed_dict = dict ( zip ( self . _update_placeholders , var_vals ) ) )