Hi,
I'm tying to resolve the follow problem: a theano function has as outputs
the value that a class method return after has made a while loop, within
which a parameter is updated:
import theanoimport theano.tensor as Timport numpy as npimport copy
theano.config.exception_verbosity = 'high'
class Test(object):
def __init__(self):
self.rate=0.01
W_val=40.00
self.W=theano.shared(value=W_val, borrow=True)
def start(self, x, y):
for i in range(5):
z=T.mean(x*self.W/y)
gz=T.grad(z, self.W)
self.W-=self.rate*gz
return z
x_set=np.array([1.,2.,1.,2.,1.,2.,1.,2.,1.,2.])
y_set=np.array([1,2,1,2,1,2,1,2,1,2])
x_set = theano.shared(x_set, borrow=True)
y_set = theano.shared(y_set, borrow=True)
y_set=T.cast(y_set, 'int32')
batch_size=2
x = T.dvector('x')
y = T.ivector('y')
index = T.lscalar()
test = Test()
cost=test.start(x,y)
train = theano.function(
inputs=[index],
outputs=cost,
givens={
x: x_set[index * batch_size: (index + 1) * batch_size],
y: y_set[index * batch_size: (index + 1) * batch_size]
})
for i in range(5):
result=train(i)
print(result)
this is the result of the print:
39.9600000008940739.9600000008940739.9600000008940739.9600000008940739.96000000089407
Now the gradient of mean(x*W/y) is equal to 1 (because x and y always have the
same value). So the first time i should have 39.95, than 39.90 and so on... Why
i always have the same result??
Thanks
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