In the neural network terminology:

one epoch = one forward pass and one backward pass of all the training examples
batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you'll need.
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).
Example: if you have 1000 training examples, and your batch size is 500, then it will take 2 iterations to complete 1 epoch.
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in tensor flow: if there are n samples and the batch size is b, total steps is: s, then number of epochs will be s*b/n

STEPS = 5000
periods = 10
steps_per_period = STEPS / periods
for period in range (0, periods):


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