def __forward(self, is_training, src_batch, trg_batch = None, generation_limit = None):
m = self.__model
tanh = functions.tanh
lstm = functions.lstm
batch_size = len(src_batch)
src_len = len(src_batch[0])
src_stoi = self.__src_vocab.stoi
trg_stoi = self.__trg_vocab.stoi
trg_itos = self.__trg_vocab.itos
s_c = wrapper.zeros((batch_size, self.__n_hidden))
# encoding
s_x = wrapper.make_var([src_stoi('</s>') for _ in range(batch_size)], dtype=np.int32)
s_i = tanh(m.w_xi(s_x))
s_c, s_p = lstm(s_c, m.w_ip(s_i))
for l in reversed(range(src_len)):
s_x = wrapper.make_var([src_stoi(src_batch[k][l]) for k in range(batch_size)], dtype=np.int32)
s_i = tanh(m.w_xi(s_x))
s_c, s_p = lstm(s_c, m.w_ip(s_i) + m.w_pp(s_p))
s_c, s_q = lstm(s_c, m.w_pq(s_p))
hyp_batch = [[] for _ in range(batch_size)]
# decoding
if is_training:
accum_loss = wrapper.zeros(())
trg_len = len(trg_batch[0])
for l in range(trg_len):
s_j = tanh(m.w_qj(s_q))
r_y = m.w_jy(s_j)
s_t = wrapper.make_var([trg_stoi(trg_batch[k][l]) for k in range(batch_size)], dtype=np.int32)
accum_loss += functions.softmax_cross_entropy(r_y, s_t)
output = wrapper.get_data(r_y).argmax(1)
for k in range(batch_size):
hyp_batch[k].append(trg_itos(output[k]))
s_c, s_q = lstm(s_c, m.w_yq(s_t) + m.w_qq(s_q))
return hyp_batch, accum_loss
else:
while len(hyp_batch[0]) < generation_limit:
s_j = tanh(m.w_qj(s_q))
r_y = m.w_jy(s_j)
output = wrapper.get_data(r_y).argmax(1)
for k in range(batch_size):
hyp_batch[k].append(trg_itos(output[k]))
if all(hyp_batch[k][-1] == '</s>' for k in range(batch_size)): break
s_y = wrapper.make_var(output, dtype=np.int32)
s_c, s_q = lstm(s_c, m.w_yq(s_y) + m.w_qq(s_q))
return hyp_batch
def __forward(self, is_training, src_batch, trg_batch = None, generation_limit = None):
m = self.__model
tanh = functions.tanh
lstm = functions.lstm
batch_size = len(src_batch)
src_len = len(src_batch[0])
src_stoi = self.__src_vocab.stoi
trg_stoi = self.__trg_vocab.stoi
trg_itos = self.__trg_vocab.itos
s_c = wrapper.zeros((batch_size, self.__n_hidden))
# encoding
s_x = wrapper.make_var([src_stoi('</s>') for _ in range(batch_size)], dtype=np.int32)
s_i = tanh(m.w_xi(s_x))
s_c, s_p = lstm(s_c, m.w_ip(s_i))
for l in reversed(range(src_len)):
s_x = wrapper.make_var([src_stoi(src_batch[k][l]) for k in range(batch_size)], dtype=np.int32)
s_i = tanh(m.w_xi(s_x))
s_c, s_p = lstm(s_c, m.w_ip(s_i) + m.w_pp(s_p))
s_c, s_q = lstm(s_c, m.w_pq(s_p))
hyp_batch = [[] for _ in range(batch_size)]
# decoding
if is_training:
accum_loss = wrapper.zeros(())
trg_len = len(trg_batch[0])
for l in range(trg_len):
s_j = tanh(m.w_qj(s_q))
r_y = m.w_jy(s_j)
s_t = wrapper.make_var([trg_stoi(trg_batch[k][l]) for k in range(batch_size)], dtype=np.int32)
accum_loss += functions.softmax_cross_entropy(r_y, s_t)
output = wrapper.get_data(r_y).argmax(1)
for k in range(batch_size):
hyp_batch[k].append(trg_itos(output[k]))
s_c, s_q = lstm(s_c, m.w_yq(s_t) + m.w_qq(s_q))
return hyp_batch, accum_loss
else:
while len(hyp_batch[0]) < generation_limit:
s_j = tanh(m.w_qj(s_q))
r_y = m.w_jy(s_j)
output = wrapper.get_data(r_y).argmax(1)
for k in range(batch_size):
hyp_batch[k].append(trg_itos(output[k]))
if all(hyp_batch[k][-1] == '</s>' for k in range(batch_size)): break
s_y = wrapper.make_var(output, dtype=np.int32)
s_c, s_q = lstm(s_c, m.w_yq(s_y) + m.w_qq(s_q))
return hyp_batch
def __forward(self,
is_training,
src_batch,
trg_batch=None,
generation_limit=None):
m = self.__model
tanh = functions.tanh
lstm = functions.lstm
batch_size = len(src_batch)
hidden_size = self.__n_hidden
src_len = len(src_batch[0])
trg_len = len(trg_batch[0]) - 1 if is_training else generation_limit
src_stoi = self.__src_vocab.stoi
trg_stoi = self.__trg_vocab.stoi
trg_itos = self.__trg_vocab.itos
hidden_zeros = wrapper.zeros((batch_size, hidden_size))
sum_e_zeros = wrapper.zeros((batch_size, 1))
# make embedding
list_x = []
for l in range(src_len):
s_x = wrapper.make_var(
[src_stoi(src_batch[k][l]) for k in range(batch_size)],
dtype=np.int32)
list_x.append(s_x)
# forward encoding
c = hidden_zeros
s_a = hidden_zeros
list_a = []
for l in range(src_len):
s_x = list_x[l]
s_i = tanh(m.w_xi(s_x))
c, s_a = lstm(c, m.w_ia(s_i) + m.w_aa(s_a))
list_a.append(s_a)
# backward encoding
c = hidden_zeros
s_b = hidden_zeros
list_b = []
for l in reversed(range(src_len)):
s_x = list_x[l]
s_i = tanh(m.w_xi(s_x))
c, s_b = lstm(c, m.w_ib(s_i) + m.w_bb(s_b))
list_b.insert(0, s_b)
# decoding
c = hidden_zeros
s_p = tanh(m.w_ap(list_a[-1]) + m.w_bp(list_b[0]))
s_y = wrapper.make_var([trg_stoi('<s>') for k in range(batch_size)],
dtype=np.int32)
hyp_batch = [[] for _ in range(batch_size)]
accum_loss = wrapper.zeros(()) if is_training else None
#for n in range(src_len):
# print(src_batch[0][n], end=' ')
#print()
for l in range(trg_len):
# calculate attention weights
list_e = []
sum_e = sum_e_zeros
for n in range(src_len):
s_w = tanh(m.w_aw(list_a[n]) + m.w_bw(list_b[n]) + m.w_pw(s_p))
r_e = functions.exp(m.w_we(s_w))
#list_e.append(functions.concat(r_e for _ in range(self.__n_hidden)))
list_e.append(r_e)
sum_e += r_e
#sum_e = functions.concat(sum_e for _ in range(self.__n_hidden))
# make attention vector
s_c = hidden_zeros
s_d = hidden_zeros
for n in range(src_len):
s_e = list_e[n] / sum_e
#s_c += s_e * list_a[n]
#s_d += s_e * list_b[n]
s_c += functions.reshape(
functions.batch_matmul(list_a[n], s_e),
(batch_size, hidden_size))
s_d += functions.reshape(
functions.batch_matmul(list_b[n], s_e),
(batch_size, hidden_size))
#zxcv = wrapper.get_data(s_e)[0][0]
#if zxcv > 0.9: asdf='#'
#elif zxcv > 0.7: asdf='*'
#elif zxcv > 0.3: asdf='+'
#elif zxcv > 0.1: asdf='.'
#else: asdf=' '
#print(asdf * len(src_batch[0][n]), end=' ')
# generate next word
c, s_p = lstm(
c,
m.w_yp(s_y) + m.w_pp(s_p) + m.w_cp(s_c) + m.w_dp(s_d))
r_y = m.w_py(s_p)
output = wrapper.get_data(r_y).argmax(1)
for k in range(batch_size):
hyp_batch[k].append(trg_itos(output[k]))
#print(hyp_batch[0][-1])
if is_training:
s_t = wrapper.make_var(
[trg_stoi(trg_batch[k][l + 1]) for k in range(batch_size)],
dtype=np.int32)
accum_loss += functions.softmax_cross_entropy(r_y, s_t)
s_y = s_t
else:
if all(hyp_batch[k][-1] == '</s>' for k in range(batch_size)):
break
s_y = wrapper.make_var(output, dtype=np.int32)
return hyp_batch, accum_loss
def __forward(self, is_training, src_batch, trg_batch = None, generation_limit = None):
m = self.__model
tanh = functions.tanh
lstm = functions.lstm
batch_size = len(src_batch)
hidden_size = self.__n_hidden
src_len = len(src_batch[0])
trg_len = len(trg_batch[0]) - 1 if is_training else generation_limit
src_stoi = self.__src_vocab.stoi
trg_stoi = self.__trg_vocab.stoi
trg_itos = self.__trg_vocab.itos
hidden_zeros = wrapper.zeros((batch_size, hidden_size))
sum_e_zeros = wrapper.zeros((batch_size, 1))
# make embedding
list_x = []
for l in range(src_len):
s_x = wrapper.make_var([src_stoi(src_batch[k][l]) for k in range(batch_size)], dtype=np.int32)
list_x.append(s_x)
# forward encoding
c = hidden_zeros
s_a = hidden_zeros
list_a = []
for l in range(src_len):
s_x = list_x[l]
s_i = tanh(m.w_xi(s_x))
c, s_a = lstm(c, m.w_ia(s_i) + m.w_aa(s_a))
list_a.append(s_a)
# backward encoding
c = hidden_zeros
s_b = hidden_zeros
list_b = []
for l in reversed(range(src_len)):
s_x = list_x[l]
s_i = tanh(m.w_xi(s_x))
c, s_b = lstm(c, m.w_ib(s_i) + m.w_bb(s_b))
list_b.insert(0, s_b)
# decoding
c = hidden_zeros
s_p = tanh(m.w_ap(list_a[-1]) + m.w_bp(list_b[0]))
s_y = wrapper.make_var([trg_stoi('<s>') for k in range(batch_size)], dtype=np.int32)
hyp_batch = [[] for _ in range(batch_size)]
accum_loss = wrapper.zeros(()) if is_training else None
#for n in range(src_len):
# print(src_batch[0][n], end=' ')
#print()
for l in range(trg_len):
# calculate attention weights
list_e = []
sum_e = sum_e_zeros
for n in range(src_len):
s_w = tanh(m.w_aw(list_a[n]) + m.w_bw(list_b[n]) + m.w_pw(s_p))
r_e = functions.exp(m.w_we(s_w))
#list_e.append(functions.concat(r_e for _ in range(self.__n_hidden)))
list_e.append(r_e)
sum_e += r_e
#sum_e = functions.concat(sum_e for _ in range(self.__n_hidden))
# make attention vector
s_c = hidden_zeros
s_d = hidden_zeros
for n in range(src_len):
s_e = list_e[n] / sum_e
#s_c += s_e * list_a[n]
#s_d += s_e * list_b[n]
s_c += functions.reshape(functions.batch_matmul(list_a[n], s_e), (batch_size, hidden_size))
s_d += functions.reshape(functions.batch_matmul(list_b[n], s_e), (batch_size, hidden_size))
#zxcv = wrapper.get_data(s_e)[0][0]
#if zxcv > 0.9: asdf='#'
#elif zxcv > 0.7: asdf='*'
#elif zxcv > 0.3: asdf='+'
#elif zxcv > 0.1: asdf='.'
#else: asdf=' '
#print(asdf * len(src_batch[0][n]), end=' ')
# generate next word
c, s_p = lstm(c, m.w_yp(s_y) + m.w_pp(s_p) + m.w_cp(s_c) + m.w_dp(s_d))
r_y = m.w_py(s_p)
output = wrapper.get_data(r_y).argmax(1)
for k in range(batch_size):
hyp_batch[k].append(trg_itos(output[k]))
#print(hyp_batch[0][-1])
if is_training:
s_t = wrapper.make_var([trg_stoi(trg_batch[k][l + 1]) for k in range(batch_size)], dtype=np.int32)
accum_loss += functions.softmax_cross_entropy(r_y, s_t)
s_y = s_t
else:
if all(hyp_batch[k][-1] == '</s>' for k in range(batch_size)): break
s_y = wrapper.make_var(output, dtype=np.int32)
return hyp_batch, accum_loss
