def top_k_pooling(matrix, sentlength_1, sentlength_2, Np):
#tensor: (1, feature maps, 66, 66)
#sentlength_1=dim-left1-right1
#sentlength_2=dim-left2-right2
#core=tensor[:,:, left1:(dim-right1),left2:(dim-right2) ]
'''
repeat_row=Np/sentlength_1
extra_row=Np%sentlength_1
repeat_col=Np/sentlength_2
extra_col=Np%sentlength_2
'''
#repeat core
matrix_1=repeat_whole_tensor(matrix, 5, True)
matrix_2=repeat_whole_tensor(matrix_1, 5, False)
list_values=matrix_2.flatten()
neighborsArgSorted = T.argsort(list_values)
kNeighborsArg = neighborsArgSorted[-(Np**2):]
top_k_values=list_values[kNeighborsArg]
all_max_value=top_k_values.reshape((1, Np**2))
return all_max_value
def top_k_pooling(matrix, sentlength_1, sentlength_2, Np):
#tensor: (1, feature maps, 66, 66)
#sentlength_1=dim-left1-right1
#sentlength_2=dim-left2-right2
#core=tensor[:,:, left1:(dim-right1),left2:(dim-right2) ]
'''
repeat_row=Np/sentlength_1
extra_row=Np%sentlength_1
repeat_col=Np/sentlength_2
extra_col=Np%sentlength_2
'''
#repeat core
matrix_1=repeat_whole_tensor(matrix, 5, True)
matrix_2=repeat_whole_tensor(matrix_1, 5, False)
list_values=matrix_2.flatten()
neighborsArgSorted = T.argsort(list_values)
kNeighborsArg = neighborsArgSorted[-(Np**2):]
top_k_values=list_values[kNeighborsArg]
all_max_value=top_k_values.reshape((1, Np**2))
return all_max_value
def unify_eachone(matrix, sentlength_1, sentlength_2, Np):
#tensor: (1, feature maps, 66, 66)
#sentlength_1=dim-left1-right1
#sentlength_2=dim-left2-right2
#core=tensor[:,:, left1:(dim-right1),left2:(dim-right2) ]
repeat_row = Np / sentlength_1
extra_row = Np % sentlength_1
repeat_col = Np / sentlength_2
extra_col = Np % sentlength_2
#repeat core
matrix_1 = repeat_whole_tensor(matrix, 5, True)
matrix_2 = repeat_whole_tensor(matrix_1, 5, False)
new_rows = T.maximum(sentlength_1, sentlength_1 * repeat_row + extra_row)
new_cols = T.maximum(sentlength_2, sentlength_2 * repeat_col + extra_col)
#core=debug_print(core_2[:,:, :new_rows, : new_cols],'core')
new_matrix = debug_print(matrix_2[:new_rows, :new_cols], 'new_matrix')
#determine x, y start positions
size_row = new_rows / Np
remain_row = new_rows % Np
size_col = new_cols / Np
remain_col = new_cols % Np
xx = debug_print(
T.concatenate([
T.arange(Np - remain_row + 1) * size_row,
(Np - remain_row) * size_row + (T.arange(remain_row) + 1) *
(size_row + 1)
]), 'xx')
yy = debug_print(
T.concatenate([
T.arange(Np - remain_col + 1) * size_col,
(Np - remain_col) * size_col + (T.arange(remain_col) + 1) *
(size_col + 1)
]), 'yy')
list_of_maxs = []
for i in xrange(Np):
for j in xrange(Np):
region = debug_print(new_matrix[xx[i]:xx[i + 1], yy[j]:yy[j + 1]],
'region')
#maxvalue1=debug_print(T.max(region, axis=2), 'maxvalue1')
maxvalue = debug_print(T.max(region).reshape((1, 1)), 'maxvalue')
list_of_maxs.append(maxvalue)
all_max_value = T.concatenate(list_of_maxs, axis=1).reshape((1, Np**2))
return all_max_value
def compute_simi_feature_batch1(input_l_matrix, input_r_matrix, length_l,
length_r, para_matrix, dim):
#matrix_r_after_translate=debug_print(T.dot(para_matrix, input_r_matrix), 'matrix_r_after_translate')
matrix_r_after_translate = input_r_matrix
repeated_1 = debug_print(
T.repeat(input_l_matrix, dim,
axis=1)[:, :(length_l * length_r)], 'repeated_1'
) # add 10 because max_sent_length is only input for conv, conv will make size bigger
repeated_2 = debug_print(
repeat_whole_tensor(matrix_r_after_translate, dim,
False)[:, :(length_l * length_r)], 'repeated_2')
'''
#cosine attention
length_1=debug_print(1e-10+T.sqrt(T.sum(T.sqr(repeated_1), axis=0)),'length_1')
length_2=debug_print(1e-10+T.sqrt(T.sum(T.sqr(repeated_2), axis=0)), 'length_2')
multi=debug_print(repeated_1*repeated_2, 'multi')
sum_multi=debug_print(T.sum(multi, axis=0),'sum_multi')
list_of_simi= debug_print(sum_multi/(length_1*length_2),'list_of_simi') #to get rid of zero length
simi_matrix=debug_print(list_of_simi.reshape((length_l, length_r)), 'simi_matrix')
'''
#euclid, effective for wikiQA
gap = debug_print(repeated_1 - repeated_2, 'gap')
eucli = debug_print(T.sqrt(1e-10 + T.sum(T.sqr(gap), axis=0)), 'eucli')
simi_matrix = debug_print((1.0 / (1.0 + eucli)).reshape(
(length_l, length_r)), 'simi_matrix')
return simi_matrix #[:length_l, :length_r]
def compute_simi_feature_batch1(input_l_matrix, input_r_matrix, length_l, length_r, para_matrix, dim):
#matrix_r_after_translate=debug_print(T.dot(para_matrix, input_r_matrix), 'matrix_r_after_translate')
matrix_r_after_translate=input_r_matrix
repeated_1=debug_print(T.repeat(input_l_matrix, dim, axis=1)[:, : (length_l*length_r)],'repeated_1') # add 10 because max_sent_length is only input for conv, conv will make size bigger
repeated_2=debug_print(repeat_whole_tensor(matrix_r_after_translate, dim, False)[:, : (length_l*length_r)],'repeated_2')
'''
#cosine attention
length_1=debug_print(1e-10+T.sqrt(T.sum(T.sqr(repeated_1), axis=0)),'length_1')
length_2=debug_print(1e-10+T.sqrt(T.sum(T.sqr(repeated_2), axis=0)), 'length_2')
multi=debug_print(repeated_1*repeated_2, 'multi')
sum_multi=debug_print(T.sum(multi, axis=0),'sum_multi')
list_of_simi= debug_print(sum_multi/(length_1*length_2),'list_of_simi') #to get rid of zero length
simi_matrix=debug_print(list_of_simi.reshape((length_l, length_r)), 'simi_matrix')
'''
#euclid, effective for wikiQA
gap=debug_print(repeated_1-repeated_2, 'gap')
eucli=debug_print(T.sqrt(1e-10+T.sum(T.sqr(gap), axis=0)),'eucli')
simi_matrix=debug_print((1.0/(1.0+eucli)).reshape((length_l, length_r)), 'simi_matrix')
return simi_matrix#[:length_l, :length_r]
def unify_eachone(matrix, sentlength_1, sentlength_2, Np):
#tensor: (1, feature maps, 66, 66)
#sentlength_1=dim-left1-right1
#sentlength_2=dim-left2-right2
#core=tensor[:,:, left1:(dim-right1),left2:(dim-right2) ]
repeat_row=Np/sentlength_1
extra_row=Np%sentlength_1
repeat_col=Np/sentlength_2
extra_col=Np%sentlength_2
#repeat core
matrix_1=repeat_whole_tensor(matrix, 5, True)
matrix_2=repeat_whole_tensor(matrix_1, 5, False)
new_rows=T.maximum(sentlength_1, sentlength_1*repeat_row+extra_row)
new_cols=T.maximum(sentlength_2, sentlength_2*repeat_col+extra_col)
#core=debug_print(core_2[:,:, :new_rows, : new_cols],'core')
new_matrix=debug_print(matrix_2[:new_rows,:new_cols], 'new_matrix')
#determine x, y start positions
size_row=new_rows/Np
remain_row=new_rows%Np
size_col=new_cols/Np
remain_col=new_cols%Np
xx=debug_print(T.concatenate([T.arange(Np-remain_row+1)*size_row, (Np-remain_row)*size_row+(T.arange(remain_row)+1)*(size_row+1)]),'xx')
yy=debug_print(T.concatenate([T.arange(Np-remain_col+1)*size_col, (Np-remain_col)*size_col+(T.arange(remain_col)+1)*(size_col+1)]),'yy')
list_of_maxs=[]
for i in xrange(Np):
for j in xrange(Np):
region=debug_print(new_matrix[xx[i]:xx[i+1], yy[j]:yy[j+1]],'region')
#maxvalue1=debug_print(T.max(region, axis=2), 'maxvalue1')
maxvalue=debug_print(T.max(region).reshape((1,1)), 'maxvalue')
list_of_maxs.append(maxvalue)
all_max_value=T.concatenate(list_of_maxs, axis=1).reshape((1, Np**2))
return all_max_value
def __init__(self, rng, tensor_l, tensor_r, dim,kern, l_left_pad, l_right_pad, r_left_pad, r_right_pad): # length_l, length_r: valid lengths after conv
#first reshape into matrix
matrix_l=tensor_l.reshape((tensor_l.shape[2], tensor_l.shape[3]))
matrix_r=tensor_r.reshape((tensor_r.shape[2], tensor_r.shape[3]))
#start
repeated_1=debug_print(T.repeat(matrix_l, dim, axis=1),'repeated_1') # add 10 because max_sent_length is only input for conv, conv will make size bigger
repeated_2=debug_print(repeat_whole_tensor(matrix_r, dim, False),'repeated_2')
'''
#cosine attention
length_1=debug_print(1e-10+T.sqrt(T.sum(T.sqr(repeated_1), axis=0)),'length_1')
length_2=debug_print(1e-10+T.sqrt(T.sum(T.sqr(repeated_2), axis=0)), 'length_2')
multi=debug_print(repeated_1*repeated_2, 'multi')
sum_multi=debug_print(T.sum(multi, axis=0),'sum_multi')
list_of_simi= debug_print(sum_multi/(length_1*length_2),'list_of_simi') #to get rid of zero length
simi_matrix=debug_print(list_of_simi.reshape((length_l, length_r)), 'simi_matrix')
'''
#euclid, effective for wikiQA
gap=debug_print(repeated_1-repeated_2, 'gap')
eucli=debug_print(T.sqrt(1e-10+T.sum(T.sqr(gap), axis=0)),'eucli')
simi_matrix=debug_print((1.0/(1.0+eucli)).reshape((dim, dim)), 'simi_matrix')
W_bound = numpy.sqrt(6. / (dim + kern))
self.W = theano.shared(numpy.asarray(rng.uniform(low=-W_bound, high=W_bound, size=(kern, dim)),dtype=theano.config.floatX),borrow=True) #a weight matrix kern*kern
matrix_l_attention=debug_print(T.dot(self.W, simi_matrix.T), 'matrix_l_attention')
matrix_r_attention=debug_print(T.dot(self.W, simi_matrix), 'matrix_r_attention')
#reset zero at both side
left_zeros_l=T.set_subtensor(matrix_l_attention[:,:l_left_pad], T.zeros((matrix_l_attention.shape[0], l_left_pad), dtype=theano.config.floatX))
right_zeros_l=T.set_subtensor(left_zeros_l[:,-l_right_pad:], T.zeros((matrix_l_attention.shape[0], l_right_pad), dtype=theano.config.floatX))
left_zeros_r=T.set_subtensor(matrix_r_attention[:,:r_left_pad], T.zeros((matrix_r_attention.shape[0], r_left_pad), dtype=theano.config.floatX))
right_zeros_r=T.set_subtensor(left_zeros_r[:,-r_right_pad:], T.zeros((matrix_r_attention.shape[0], r_right_pad), dtype=theano.config.floatX))
#combine with original input matrix
self.new_tensor_l=T.concatenate([matrix_l,right_zeros_l], axis=0).reshape((tensor_l.shape[0], 2*tensor_l.shape[1], tensor_l.shape[2], tensor_l.shape[3]))
self.new_tensor_r=T.concatenate([matrix_r,right_zeros_r], axis=0).reshape((tensor_r.shape[0], 2*tensor_r.shape[1], tensor_r.shape[2], tensor_r.shape[3]))
self.params=[self.W]
def __init__(self, rng, tensor_l, tensor_r, dim,kern, l_left_pad, l_right_pad, r_left_pad, r_right_pad): # length_l, length_r: valid lengths after conv
#first reshape into matrix
matrix_l=tensor_l.reshape((tensor_l.shape[2], tensor_l.shape[3]))
matrix_r=tensor_r.reshape((tensor_r.shape[2], tensor_r.shape[3]))
#start
repeated_1=debug_print(T.repeat(matrix_l, dim, axis=1),'repeated_1') # add 10 because max_sent_length is only input for conv, conv will make size bigger
repeated_2=debug_print(repeat_whole_tensor(matrix_r, dim, False),'repeated_2')
'''
#cosine attention
length_1=debug_print(1e-10+T.sqrt(T.sum(T.sqr(repeated_1), axis=0)),'length_1')
length_2=debug_print(1e-10+T.sqrt(T.sum(T.sqr(repeated_2), axis=0)), 'length_2')
multi=debug_print(repeated_1*repeated_2, 'multi')
sum_multi=debug_print(T.sum(multi, axis=0),'sum_multi')
list_of_simi= debug_print(sum_multi/(length_1*length_2),'list_of_simi') #to get rid of zero length
simi_matrix=debug_print(list_of_simi.reshape((length_l, length_r)), 'simi_matrix')
'''
#euclid, effective for wikiQA
gap=debug_print(repeated_1-repeated_2, 'gap')
eucli=debug_print(T.sqrt(1e-10+T.sum(T.sqr(gap), axis=0)),'eucli')
simi_matrix=debug_print((1.0/(1.0+eucli)).reshape((dim, dim)), 'simi_matrix')
W_bound = numpy.sqrt(6. / (dim + kern))
self.W = theano.shared(numpy.asarray(rng.uniform(low=-W_bound, high=W_bound, size=(kern, dim)),dtype=theano.config.floatX),borrow=True) #a weight matrix kern*kern
matrix_l_attention=debug_print(T.dot(self.W, simi_matrix.T), 'matrix_l_attention')
matrix_r_attention=debug_print(T.dot(self.W, simi_matrix), 'matrix_r_attention')
#reset zero at both side
left_zeros_l=T.set_subtensor(matrix_l_attention[:,:l_left_pad], T.zeros((matrix_l_attention.shape[0], l_left_pad), dtype=theano.config.floatX))
right_zeros_l=T.set_subtensor(left_zeros_l[:,-l_right_pad:], T.zeros((matrix_l_attention.shape[0], l_right_pad), dtype=theano.config.floatX))
left_zeros_r=T.set_subtensor(matrix_r_attention[:,:r_left_pad], T.zeros((matrix_r_attention.shape[0], r_left_pad), dtype=theano.config.floatX))
right_zeros_r=T.set_subtensor(left_zeros_r[:,-r_right_pad:], T.zeros((matrix_r_attention.shape[0], r_right_pad), dtype=theano.config.floatX))
#combine with original input matrix
self.new_tensor_l=T.concatenate([matrix_l,right_zeros_l], axis=0).reshape((tensor_l.shape[0], 2*tensor_l.shape[1], tensor_l.shape[2], tensor_l.shape[3]))
self.new_tensor_r=T.concatenate([matrix_r,right_zeros_r], axis=0).reshape((tensor_r.shape[0], 2*tensor_r.shape[1], tensor_r.shape[2], tensor_r.shape[3]))
self.params=[self.W]
