I am following a tutorial that shows how to implement logistic regression using Theano. The listed line is giving me an error. I don't know how to fix it.
from theano import tensor
TS = tensor.matrix('training-set')
W = tensor.matrix('weights')
E = tensor.matrix('expected')
O = tensor.dot(TS,W)
def_err = ((E-O)**2).sum()
e = function([W,TS,E],def_err)
grad_err = function([W,TS,E],grad(e,W))
This is the error I am getting:
\in grad(cost, wrt, consider_constant, disconnected_inputs, add_names, known_grads, return_disconnected, null_gradients)
428 raise AssertionError("cost and known_grads can't both be None.")
429
--> 430 if cost is not None and isinstance(cost.type, NullType):
431 raise ValueError("Can't differentiate a NaN cost."
432 "cost is NaN because " +
AttributeError: 'Function' object has no attribute 'type'
In line grad_err = function([W,TS,E],grad(e,W))
you want to compute gradient of error 'def_err' w.r.t 'W', but you are passing a function 'e' to grad(..) without the list of inputs, this will never work.
Also please note that TS, W, E, O etc are tensor/symbolic variables which are general expressions and need to be provided with extra input to determine their value.
I would recommend going through the following tutorial for logistic regression, If you have just started Theano then these tutorials will definitely help you to get started.
This should work:
from theano import tensor, function, grad
TS = tensor.matrix('training-set')
W = tensor.matrix('weights')
E = tensor.matrix('expected')
O = tensor.dot(TS,W)
def_err = ((E-O)**2).sum()
e = function([W,TS,E],def_err)
grad_err = function([W,TS,E],grad(def_err,W))