def optimize_function(params, config=None):
    """
    Create a optimizing function receives gradients.
    Parameters:
        params - parameters
        config - training configuration
    Returns:
        updating function receives gradients
    """
    gs = [dim_to_var(p.ndim) for p in params]
    updates, _ = optimize_updates(params, gs, config)
    return theano.function(gs, [], updates=updates)
def optimize_function(params, config=None):
    """
    Create a optimizing function receives gradients.
    Parameters:
        params - parameters
        config - training configuration
    Returns:
        updating function receives gradients
    """
    gs = [dim_to_var(p.ndim) for p in params]
    updates, _ = optimize_updates(params, gs, config)
    return theano.function(gs, [], updates=updates)
def optimize_function ( params , config = None ) : gs = [ dim_to_var ( p . ndim ) for p in params ] updates , _ = optimize_updates ( params , gs , config ) return theano . function ( gs , [ ] , updates = updates )
Create a optimizing function receives gradients.
    Parameters:
        params - parameters
        config - training configuration
    Returns:
        updating function receives gradients
Create a optimizing function receives gradients . Parameters : params - parameters config - training configuration Returns : updating function receives gradients