*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,
name='adadelta_f_grad_shared',allow_input_downcast=True)
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,
dtype=emb11.dtype)
proj11 = getpl2(emb11, '1lstm1', mask11, False, rrng, 50,
self.tnewp)[-1]
proj21 = getpl2(emb21, '2lstm1', mask21, False, rrng, 50,
self.tnewp)[-1]
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:
cost.type()
<TensorType(float64, scalar)>
where as all others are in float32.
now please tell me how can i deal with the issue. Its urgent .
Regards,
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