def fx(func, i, nodes, debug):
if func is "sigm":
res = nnlib.sigmoid(nodes[i])
elif func is "tanh":
res = nnlib.hyper_tan(nodes[i])
log.d(debug, log.hl("f(%d) \t(%.2f)\t---> %s \t---> (%.2f) " %(i, nodes[i], func, res),2))
nodes[i] = res
def genWeight(weights):
try:
w = weights[0]
del weights[0]
return w
except IndexError:
print(log.hl("genWeight: Index out of range. len=%d" % (len(weights)),0))
return 1
def main():
"""
eta and alpha are parameters that can be tweaked.
"""
eta = 0.90
alpha = 0.24
"""
maxIter represents the limit of how many times the data can be
rerun and the weights can be adjusted before giving up.
"""
maxIter = 500000
"""
stop_err represents the acceptable value for giving up
and will kick in if the error value becomes smaller and
maxIter still has not stopped the script.
"""
stop_err = 0.001
"""
The data set is splitted into:
[[input],[output]]
"""
dataset = input("Name of dataset: ")
data = io.readDataset("data/%s.data" % dataset)
inodes = 1 # This must match the number of input fields in the data set
hnodes = 4
onodes = 1 # This must match the number of output fields in the data set
"""
The bias node will be placed at the end of the node-array
and will have connections to all nodes in the hidden layer
and the output layer
"""
b = inodes + hnodes + onodes
wtot = inodes*hnodes+hnodes+hnodes*onodes+onodes
"""
The initial weight values for each connection will
be set randomly and later changed by the backpropagation.
"""
weights = [random.random() for r in range(wtot)]
"""
The following is index ranges for the different layers.
Do not change!
"""
ilayer = range(0,inodes)
hlayer = range(inodes,inodes+hnodes)
olayer = range(inodes+hnodes,b)
"""
Debug is turned on during the last iteration if turned off.
Otherwise, the debug information will be printed for each
entry in the data set every time.
"""
debug = False
con = [[],[]]
"""
Add connections between the nodes in the hidden layer and the nodes in
the input layer.
"""
net.connect_nodes(0, hlayer, ilayer, con, weights, b)
net.connect_nodes(1, olayer, hlayer, con, weights, b)
lastIter = False
err = 0
minNErr = 1
idata = False
for times in range(maxIter):
###########################
# Propagating
###########################
if err != 0 and err < stop_err:
lastIter = True
#debug = True
idata = []
random.shuffle(data)
"""
Testing all rows of the data set
"""
(err, t, nodes) = net.testData(data, False, lastIter, idata, b, ilayer, hlayer, olayer, debug, con, inodes, hnodes)
if err < minNErr:
minNErr = err
print(log.hl("%i \t> Error = %.8f\n" % (times, err),1))
if lastIter:
for ida in idata:
print(ida)
log.d(debug, " Stopped after %s iterations.\n\n" % log.hl(times+1, 1))
break
###########################
# backpropagating
###########################
"""
inserting gradient.
"""
hGrad = net.mkarr(b)
for i in olayer:
hGrad[i] = nnlib.tanh_grad(t[i-(inodes+hnodes)], nodes[i])
for c in con[1]:
c.backpropagate(hGrad)
for i in hlayer:
hGrad[i] = nnlib.sig_grad(hGrad[i], nodes[i])
#log.d(debug, " hGrad[%d] = %.2f" % (i, hGrad[i]))
for c in con[0]:
c.backpropagate(hGrad)
net.adjust_weights(1, eta, hGrad, nodes, alpha, con)
net.adjust_weights(0, eta, hGrad, nodes, alpha, con)
try:
testdata = io.readDataset("data/%s.test" % dataset)
(err, t, nodes) = net.testData(testdata, True, False, [], b, ilayer, hlayer, olayer, con. inodes, hnodes)
print(log.hl("\nRunning Testdata...",2))
print("--------------------------------")
print("MAE: %.8f\n" % (err))
while True:
inrow = io.splitTestData(input("Manual input:"));
for new_data in inrow:
print("In: %.4f" % new_data)
nodes = net.propagateData(inrow, b, ilayer, hlayer, olayer, debug, con)
for i in olayer:
print("Out: %.4f" % nodes[i])
except KeyboardInterrupt:
print("Quitting...")