def forward(self, xs, h):
N, T = xs.shape
N, H = h.shape
self.lstm.set_state(h)
out = self.embed.forward(xs)
hs = np.repeat(h, T, axis=0).reshape(N, T, H)
out = np.concatenate((hs, out), axis=2)
out = self.lstm.forward(out)
out = np.concatenate((hs, out), axis=2)
score = self.affine.forward(out)
self.cache = H
return score
def forward(self, xs):
hs1 = self.forward_lstm.forward(xs)
hs2 = self.backward_lstm.forward(xs[:, ::-1])
hs2 = hs2[:, ::-1]
hs = np.concatenate((hs1, hs2), axis=2)
return hs
def forward(self, xs):
o1 = self.forward_lstm.forward(xs)
o2 = self.backward_lstm.forward(xs[:, ::-1])
o2 = o2[:, ::-1]
out = np.concatenate((o1, o2), axis=2)
return out
def forward(self, xs, enc_hs):
h = enc_hs[:, -1]
self.lstm.set_state(h)
out = self.embed.forward(xs)
dec_hs = self.lstm.forward(out)
c = self.attention.forward(enc_hs, dec_hs)
out = np.concatenate((c, dec_hs), axis=2)
score = self.affine.forward(out)
return score
def generate(self, h, start_id, sample_size):
sampled = []
char_id = start_id
self.lstm.set_state(h)
H = h.shape[1]
peeky_h = h.reshape(1, 1, H)
for _ in range(sample_size):
x = np.array([char_id]).reshape((1, 1))
out = self.embed.forward(x)
out = np.concatenate((peeky_h, out), axis=2)
out = self.lstm.forward(out)
out = np.concatenate((peeky_h, out), axis=2)
score = self.affine.forward(out)
char_id = np.argmax(score.flatten())
sampled.append(char_id)
return sampled
def generate(self, enc_hs, start_id, eos_id, max_len=40):
sampled = []
sample_id = start_id
h = enc_hs[:, -1]
self.lstm.set_state(h)
while sample_id != eos_id:
if len(sampled) > max_len:
break
x = np.array([int(sample_id)]).reshape((1, 1))
out = self.embed.forward(x)
dec_hs = self.lstm.forward(out)
c = self.attention.forward(enc_hs, dec_hs)
out = np.concatenate((c, dec_hs), axis=2)
score = self.affine.forward(out)
sample_id = np.argmax(score.flatten())
sampled.append(sample_id)
return sampled