早期停药用于logistic回归。西亚诺

Question

我试图理解正式文档上的逻辑回归代码，但我很难理解这段代码背后的逻辑：

# early-stopping parameters
patience = 5000  # look as this many examples regardless
patience_increase = 2     # wait this much longer when a new best is
                              # found
improvement_threshold = 0.995  # a relative improvement of this much is
                               # considered significant
validation_frequency = min(n_train_batches, patience/2)
                              # go through this many
                              # minibatches before checking the network
                              # on the validation set; in this case we
                              # check every epoch

best_params = None
best_validation_loss = numpy.inf
test_score = 0.
start_time = time.clock()

done_looping = False
epoch = 0
while (epoch < n_epochs) and (not done_looping):
    # Report "1" for first epoch, "n_epochs" for last epoch
    epoch = epoch + 1
    for minibatch_index in xrange(n_train_batches):

        d_loss_wrt_params = ... # compute gradient
        params -= learning_rate * d_loss_wrt_params # gradient descent

        # iteration number. We want it to start at 0.
        iter = (epoch - 1) * n_train_batches + minibatch_index
        # note that if we do `iter % validation_frequency` it will be
        # true for iter = 0 which we do not want. We want it true for
        # iter = validation_frequency - 1.
        if (iter + 1) % validation_frequency == 0:

            this_validation_loss = ... # compute zero-one loss on validation set

            if this_validation_loss < best_validation_loss:

                # improve patience if loss improvement is good enough
                if this_validation_loss < best_validation_loss * improvement_threshold:

                    patience = max(patience, iter * patience_increase)
                best_params = copy.deepcopy(params)
                best_validation_loss = this_validation_loss

        if patience <= iter:
            done_looping = True
            break

任何一个人，向我解释，变量:耐心，patience_increase，improvement_threshold，validation_frequency，iter代表什么？

这种情况会怎样？

if (iter + 1) % validation_frequency == 0:

Answer 1

Patience是停止前要做的训练批次的数量。iter是你见过的训练批次的数量。每次迭代时，您都会决定您的验证是否低于以前的最佳值。只有当分数低于improvement_threshold * validation_score时，改进才会被存储。

看来patience_increase是一个乘数。每次获得新的最低分数时，您都会将总数或培训批次提高到iter*patience_increase，但不低于patience的当前值。

validation_frequency只是检查验证分数之间的批数。