training.py 文件源码

def set_optimizer(inputs, cost, tparams, constants, updates, extra_outs,
                  optimizer='sgd', optimizer_args=None,
                  **learning_args):
    '''Sets the parameter update functions with optimizer.

    Args:
        inputs (T.tensor): input variables.
        cost (T.scalar): cost
        tparams (OrderedDict): directionary of tensor parameters
        constants (list): list of constant tensors.
        updates (theano.OrderedUpdates): updates.
        extra_outs (list): list of extra output tensors.
        optimizer (Optional[str]): optimizer string. See `utils.op` for details.
            Defaults to `sgd`.
        optimizer_args (Optional[dict]): optional arguments for optimizer.
        **learning_args: extra kwargs for learning not used.

    Returns:
        theano.function: gradient function.
        theano.function: update function.
        dict: extra learning keyword arguments.

    '''

    if optimizer_args is None:
        optimizer_args = dict()
    grads = T.grad(cost, wrt=itemlist(tparams),
                   consider_constant=constants)

    updates = theano.OrderedUpdates(updates)

    lr = T.scalar(name='lr')
    f_grad_shared, f_grad_updates = eval('op.' + optimizer)(
        lr, tparams, grads, inputs, cost, extra_ups=updates,
        extra_outs=extra_outs, **optimizer_args)

    return f_grad_shared, f_grad_updates, learning_args