def lstm_network_t(size_in, size_out, num_units, num_mems, dbg_out={}):
def s_func_lstm(_in, _s_in, _s_out, name=''):
c_prev = cgt.matrix(fixed_shape=(None, _s_out))
h_prev = cgt.matrix(fixed_shape=(None, _s_out))
c_cur, h_cur = lstm_block(h_prev, c_prev, _in, _s_in, _s_out, name)
net_c_prev.append(c_prev)
net_h_prev.append(h_prev)
net_c_curr.append(c_cur)
net_h_curr.append(h_cur)
return h_cur
assert len(num_units) == len(num_mems)
net_c_prev, net_h_prev, net_c_curr, net_h_curr = [], [], [], []
net_in = cgt.matrix(fixed_shape=(None, size_in))
prev_num_units, prev_out = size_in, net_in
curr_layer = 1
for curr_num_units, curr_num_mem in zip(num_units, num_mems):
assert curr_num_units >= curr_num_mem >= 0
prev_out = combo_layer(
prev_out, prev_num_units, curr_num_units,
(curr_num_mem,),
s_funcs=(s_func_lstm, s_func_ip),
o_funcs=(None, cgt.sigmoid),
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="Out")(prev_out)
dbg_out['NET~out'] = net_out
return net_in, net_out, net_c_prev, net_h_prev, net_c_curr, net_h_curr
def lstm_network_t(size_in, size_out, num_units, num_mems, dbg_out={}):
def s_func_lstm(_in, _s_in, _s_out, name=''):
c_prev = cgt.matrix(fixed_shape=(None, _s_out))
h_prev = cgt.matrix(fixed_shape=(None, _s_out))
c_cur, h_cur = lstm_block(h_prev, c_prev, _in, _s_in, _s_out, name)
net_c_prev.append(c_prev)
net_h_prev.append(h_prev)
net_c_curr.append(c_cur)
net_h_curr.append(h_cur)
return h_cur
assert len(num_units) == len(num_mems)
net_c_prev, net_h_prev, net_c_curr, net_h_curr = [], [], [], []
net_in = cgt.matrix(fixed_shape=(None, size_in))
prev_num_units, prev_out = size_in, net_in
curr_layer = 1
for curr_num_units, curr_num_mem in zip(num_units, num_mems):
assert curr_num_units >= curr_num_mem >= 0
prev_out = combo_layer(prev_out,
prev_num_units,
curr_num_units, (curr_num_mem, ),
s_funcs=(s_func_lstm, s_func_ip),
o_funcs=(None, cgt.sigmoid),
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="Out")(prev_out)
dbg_out['NET~out'] = net_out
return net_in, net_out, net_c_prev, net_h_prev, net_c_curr, net_h_curr
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
