我正在尝试理解官方文档中逻辑回归的代码，但我很难理解以下代码的逻辑，我有一个问题，我们如何在ANN上应用k-fold交叉验证小批量？
{# 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
}