在skflow中添加正则化器

Question

我最近从tensorflow转到了skflow。在tensorflow中，我们会将我们的lambda*tf.nn.l2_loss(权重)添加到我们的损失中。现在，我在skflow中有了以下代码：

def deep_psi(X, y):
    layers = skflow.ops.dnn(X, [5, 10, 20, 10, 5], keep_prob=0.5)
    preds, loss = skflow.models.logistic_regression(layers, y)
    return preds, loss

def exp_decay(global_step):
    return tf.train.exponential_decay(learning_rate=0.01,
                                      global_step=global_step,
                                      decay_steps=1000,
                                      decay_rate=0.005)

deep_cd = skflow.TensorFlowEstimator(model_fn=deep_psi,
                                    n_classes=2,
                                    steps=10000,
                                    batch_size=10,
                                    learning_rate=exp_decay,
                                    verbose=True,)

我怎么在这里加一个正规化器？伊利亚暗示了一些这里的东西，但我搞不懂。

Answer 1

您仍然可以向损失中添加其他组件，只需从dnn / logistic_regression中检索权重并将它们添加到损失中：

def regularize_loss(loss, weights, lambda):
    for weight in weights:
        loss = loss + lambda * tf.nn.l2_loss(weight)
    return loss    


def deep_psi(X, y):
    layers = skflow.ops.dnn(X, [5, 10, 20, 10, 5], keep_prob=0.5)
    preds, loss = skflow.models.logistic_regression(layers, y)

    weights = []
    for layer in range(5): # n layers you passed to dnn
        weights.append(tf.get_variable("dnn/layer%d/linear/Matrix" % layer))
        # biases are also available at dnn/layer%d/linear/Bias
    weights.append(tf.get_variable('logistic_regression/weights'))

    return preds, regularize_loss(loss, weights, lambda)

注意，变量的路径可以是在这里发现的。

另外，我们希望增加对所有层的正则化支持，包括变量(如dnn、conv2d或fully_connected)，因此下周Tensorflow的夜间构建应该有类似于dnn(.., regularize=tf.contrib.layers.l2_regularizer(lambda))的内容。当这种情况发生时，我会更新这个答案。