*hello @nouiz*

*..I read many posts of yours.. but still i could not map my problem 
accordingly. please help me. *

TypeError: ('An update must have the same type as the original shared 
variable (shared_var=1lstm1_U_rgrad2, shared_var.type=TensorType(float32, 
matrix), update_val=Elemwise{add,no_inplace}.0, 
update_val.type=TensorType(float64, matrix)).', 'If the difference is 
related to the broadcast pattern, you can call the tensor.unbroadcast(var, 
axis_to_unbroadcast[, ...]) function to remove broadcastable dimensions.')

the error appears on this line of code:

f_grad_shared = theano.function([emb11, mask11, emb21, mask21, y], cost, 
updates=zgup + rg2up,

whereas the created parameters that are passe dto this function are :

 y = tensor.vector('y', dtype='float32')
        mask11 = tensor.matrix('mask11', dtype='float32')
        mask21 = tensor.matrix('mask21', dtype='float32')
        emb11 = theano.tensor.ftensor3('emb11')
        emb21 = theano.tensor.ftensor3('emb21')
        trng = RandomStreams(1234)
        self.tnewp = init_tparams(newp)

        rate = 0.5
        rrng = trng.binomial(emb11.shape, p=1 - rate, n=1, 

        proj11 = getpl2(emb11, '1lstm1', mask11, False, rrng, 50, 
        proj21 = getpl2(emb21, '2lstm1', mask21, False, rrng, 50, 
        dif = (proj21 - proj11).norm(L=1, axis=1)
        s2 = T.exp(-dif)
        sim = T.clip(s2, 1e-7, 1.0 - 1e-7)
        lr = tensor.scalar(name='lr',dtype='float32')
        ys = T.clip((y - 1.0) / 4.0, 1e-7, 1.0 - 1e-7)
        cost = T.mean((sim - ys) ** 2)

i checked for data types:

<TensorType(float64, scalar)>
where as all others are in float32.
now please tell me how can i deal with the issue. Its urgent .



You received this message because you are subscribed to the Google Groups 
"theano-users" group.
To unsubscribe from this group and stop receiving emails from it, send an email 
to theano-users+unsubscr...@googlegroups.com.
For more options, visit https://groups.google.com/d/optout.

Reply via email to