I have written the following code in theano. It keeps giving me a gradient
error. Can someone please help me? The error I get is: NotImplementedError:
Grad is not implemented for inputs withnumber of dimension other than 1.
nodes_np = np.array([1,2,3,4,5,6])
child_np = np.array([2,3,4,5,6])
n_child_np = np.array([2,0,3,0,0,0])
leaves_np = np.array([4,0,3,0,0,0])
nodes, child, n_child, leaves = shared(nodes_np.astype(float)), shared(
child_np.astype(float)), shared(n_child_np), shared(leaves_np)
embed_dim = 3
vocab = np.arange(200)
n_vocab = len(vocab)
vci = np.random.random((1 + n_vocab, embed_dim))
VCi = shared(vci, name='Embeddings')
unique_ind = T.eq(nodes[:,None],vocab[None,:])
Ci = VCi[unique_ind.nonzero()[1]]
... Initialize a few more weights here
nonzero_par = n_child.nonzero()
nonzero_child = n_child.nonzero_values()
luc = leaves[1:]
lup = repeat(leaves[nonzero_par], nonzero_child)
l = luc/lup
sep = cumsum(nonzero_child)
i = T.arange(1, child.shape[0]+1)
i = i - repeat(T.concatenate((T.zeros(1), i[sep-1][:-1])), nonzero_child)
n = repeat(nonzero_child, nonzero_child)
li, ri = (n-i)/(n-1), (i-1)/(n-1)
li, ri = T.set_subtensor(li[T.eq(n,1)],0.5),
T.set_subtensor(ri[T.eq(n,1)],0.5)
W_code_i = li[:, None, None]*Wl[None,:,:]+ri[:, None, None]*Wr[None,:,:] #
Wl and Wr are previously initialized weights
product = l[:,None]*T.sum(W_code_i*Ci[1:,None,:], axis=2) + Bias
def split_sum(array, split_cts):
'''
Given an array like and an array of counts, it returns an array where
each element is the sum of count number of indices in array
That is retval[i] = sum(array[counts.cumsum()[i-1]:counts.cumsum()[i]])
'''
sep = cumsum(split_cts) - 1
return diff(T.concatenate((T.zeros((array.shape[1], ))[None,:],
cumsum(array, axis=0)[sep]), axis=0), axis=0)
activation = T.tanh(split_sum(product, nonzero_child))
Ci = T.sum(Wcomb1[None,:,:]*Ci[:,None,:], axis=2)
activation = T.sum(Wcomb2[None,:,:]*activation[:,None,:], axis=2)
Ci = T.inc_subtensor(Ci[nonzero_par], activation)
owp = T.max(Ci, axis=-1)
T.grad(owp.norm(2), wrt=VCi)
Any help would be appreciated. Thanks in advance for your help.
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