jacoblgoodman / neural_net Goto Github PK
View Code? Open in Web Editor NEWproof of concept nearal network from scratch
proof of concept nearal network from scratch
current func works on one observation update to run on an epoch over a full training set.
def in_random_order(data):
"""generator that returns the elements of data in random order"""
indexes = [i for i, _ in enumerate(data)] # create a list of indexes random.shuffle(indexes) # shuffle them
for i in indexes: # return the data in that order
yield data[i]
Possible function to utilize from data science from scratch book
Procedure should be generalized should default batch =1.
if batch size greater 1
Gradients summed together for pass % batch size.
hyperbolic tanget: need act func and der func
Needs both act func and Der func
matrixs should initialize with values between .5 and -.5.
update to allow floats and investigate preventing zeros.
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