def hybrid_layer(X, size_in, size_out, size_random, dbg_out=[]):
assert size_out >= size_random >= 0
out = cgt.sigmoid(nn.Affine(
size_in, size_out, name="InnerProd(%d->%d)" % (size_in, size_out)
)(X))
dbg_out.append(out)
if size_random == 0:
return out
if size_random == size_out:
out_s = cgt.bernoulli(out)
return out_s
out_s = cgt.bernoulli(out[:, :size_random])
out = cgt.concatenate([out_s, out[:, size_random:]], axis=1)
return out
def hybrid_layer(X, size_in, size_out, size_random, dbg_out=[]):
assert size_out >= size_random >= 0
out = cgt.sigmoid(
nn.Affine(size_in,
size_out,
name="InnerProd(%d->%d)" % (size_in, size_out))(X))
dbg_out.append(out)
if size_random == 0:
return out
if size_random == size_out:
out_s = cgt.bernoulli(out)
return out_s
out_s = cgt.bernoulli(out[:, :size_random])
out = cgt.concatenate([out_s, out[:, size_random:]], axis=1)
return out
def hybrid_network(size_in, size_out, num_units, num_stos, dbg_out={}):
assert len(num_units) == len(num_stos)
net_in = cgt.matrix("X", fixed_shape=(None, size_in))
prev_num_units, prev_out = size_in, net_in
dbg_out['NET~in'] = net_in
curr_layer = 1
for (curr_num_units, curr_num_sto) in zip(num_units, num_stos):
assert curr_num_units >= curr_num_sto >= 0
prev_out = combo_layer(
prev_out,
prev_num_units,
curr_num_units, (curr_num_sto, ),
s_funcs=s_func_ip,
o_funcs=(lambda x: cgt.bernoulli(cgt.sigmoid(x)), cgt.nn.rectify),
name=str(curr_layer),
dbg_out=dbg_out)
dbg_out['L%d~out' % curr_layer] = prev_out
prev_num_units = curr_num_units
curr_layer += 1
net_out = nn.Affine(prev_num_units,
size_out,
name="InnerProd(%d->%d)" %
(prev_num_units, size_out))(prev_out)
dbg_out['NET~out'] = net_out
return net_in, net_out
def hybrid_network(size_in, size_out, num_units, num_stos, dbg_out={}):
assert len(num_units) == len(num_stos)
net_in = cgt.matrix("X", fixed_shape=(None, size_in))
prev_num_units, prev_out = size_in, net_in
dbg_out['NET~in'] = net_in
curr_layer = 1
for (curr_num_units, curr_num_sto) in zip(num_units, num_stos):
assert curr_num_units >= curr_num_sto >= 0
prev_out = combo_layer(prev_out, prev_num_units, curr_num_units,
(curr_num_sto,),
s_funcs=s_func_ip,
o_funcs=(lambda x: cgt.bernoulli(cgt.sigmoid(x)), cgt.nn.rectify),
name=str(curr_layer), dbg_out=dbg_out)
dbg_out['L%d~out' % curr_layer] = prev_out
prev_num_units = curr_num_units
curr_layer += 1
net_out = nn.Affine(prev_num_units, size_out,
name="InnerProd(%d->%d)" % (prev_num_units, size_out)
)(prev_out)
dbg_out['NET~out'] = net_out
return net_in, net_out
