def __init__(self,
loss,
var_list=None,
equalities=None,
inequalities=None,
var_to_bounds=None,
**optimizer_kwargs):
"""Initialize a new interface instance.
Args:
loss: A scalar `Tensor` to be minimized.
var_list: Optional `list` of `Variable` objects to update to minimize
`loss`. Defaults to the list of variables collected in the graph
under the key `GraphKeys.TRAINABLE_VARIABLES`.
equalities: Optional `list` of equality constraint scalar `Tensor`s to be
held equal to zero.
inequalities: Optional `list` of inequality constraint scalar `Tensor`s
to be held nonnegative.
var_to_bounds: Optional `dict` where each key is an optimization
`Variable` and each corresponding value is a length-2 tuple of
`(low, high)` bounds. Although enforcing this kind of simple constraint
could be accomplished with the `inequalities` arg, not all optimization
algorithms support general inequality constraints, e.g. L-BFGS-B. Both
`low` and `high` can either be numbers or anything convertible to a
NumPy array that can be broadcast to the shape of `var` (using
`np.broadcast_to`). To indicate that there is no bound, use `None` (or
`+/- np.infty`). For example, if `var` is a 2x3 matrix, then any of
the following corresponding `bounds` could be supplied:
* `(0, np.infty)`: Each element of `var` held positive.
* `(-np.infty, [1, 2])`: First column less than 1, second column less
than 2.
* `(-np.infty, [[1], [2], [3]])`: First row less than 1, second row less
than 2, etc.
* `(-np.infty, [[1, 2, 3], [4, 5, 6]])`: Entry `var[0, 0]` less than 1,
`var[0, 1]` less than 2, etc.
**optimizer_kwargs: Other subclass-specific keyword arguments.
"""
self.optimizer_kwargs = optimizer_kwargs
self._loss = loss
self._equalities = equalities or []
self._inequalities = inequalities or []
self._var_to_bounds = var_to_bounds
if var_list is None:
self._vars = variables.trainable_variables()
elif var_list == []:
raise ValueError("No variables to optimize.")
else:
self._vars = list(var_list)
self._packed_bounds = []
self._update_placeholders = []
self._var_updates = []
self._packed_var = None
self._packed_loss_grad = None
self._packed_equality_grads = []
self._packed_inequality_grads = []
self._var_shapes = None
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