common_theano.py 文件源码

def optimize_function(model, solver_params, config=None):
    """
    Create a optimizing function receives gradients.
    Parameters:
        params - parameters
        config - training configuration
    Returns:
        updating function receives gradients
    """
    gradients_ = [dim_to_var(p.ndim) for p in model.params]

    lr_ = tt.fscalar('lr_')

    updates = optimizers.optimizer(lr=lr_,
                                   model=model,
                                   gradients=gradients_,
                                   solver_params=solver_params)

    return t.function(inputs=[lr_]+ gradients_, outputs=[], updates=updates)