unoptimized_self_network.py 文件源码

def rmsprop_updates(grads, params, learning_rate=1.0, rho=0.9, epsilon=1e-6):
    """
    """
    updates = OrderedDict()

    # Using theano constant to prevent upcasting of float32
    one = T.constant(1)

    for param, grad in zip(params, grads):
        value = param.get_value(borrow=True)
        accu = theano.shared(np.zeros(value.shape, dtype=value.dtype),
                             broadcastable=param.broadcastable)
        accu_new = rho * accu + (one - rho) * grad ** 2
        updates[accu] = accu_new
        try: 
            updates[param] = lasagne.updates.norm_constraint( param - (learning_rate * grad /
                                  T.sqrt(accu_new + epsilon)) , MAX_NORM )
        except:
            updates[param] = param - (learning_rate * grad /
                                 T.sqrt(accu_new + epsilon))

    return updates