epoch : 所有训练集使用一次，叫一个 epoch
one epoch = one forward pass and one backward pass of all the training examples
对应代码中的参数是 n_epochs.
batch_size : 一个 batch 中 samples 的个数
一般情况下，一个训练集中会有大量的samples，受限于内存大小通常无法一次加载，同时为了提高训练速度，会将整个training set分为n_batch组，每组包含batch_size个samples
train_set = batch_size * n_batch
batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you’ll need.
iterations : 利用某个 batch 中的所有samples 进行一次训练，叫一次 iteration
number of iterations = number of passes, each pass using [batch size] number of examples. To be clear, one pass = one forward pass + one backward pass (we do not count the forward pass and backward pass as two different passes)
n_iterations = n_epoch * n_batch
具体流程是
# epoch个数
n_epochs = 100
# 样本总个数
numSamples = 100 000
# 要将样本分割为n_batch组
n_batch = 10
# 每个batch包含的samples
batch_size = numSamples / n_batch
# 进行训练
iterations = 0
for i in range(n_epochs ):
for j in range (n_batch):
#利用第j组batch进行training
train (j)
# iterations个数加1
iterations = iterations +1