33. LSTMをChainerをつかって実装する(２)
class RNN(Chain):
def __init__(self):
super(RNN, self).__init__(
embed=L.EmbedID(1000, 100), # word embedding
mid=L.LSTM(100, 50), # the first LSTM layer
out=L.Linear(50, 1000), # the feed-forward output layer
)
def reset_state(self):
self.mid.reset_state()
def __call__(self, cur_word):
# Given the current word ID, predict the next word.
x = self.embed(cur_word)
h = self.mid(x)
y = self.out(h)
return y
rnn = RNN()
model = L.Classifier(rnn)
optimizer = optimizers.SGD()
optimizer.setup(model)
34. LSTMをChainerをつかって実装する(３)
class RNN(Chain):
def __init__(self):
super(RNN, self).__init__(
embed=L.EmbedID(1000, 100), # word embedding
mid=L.LSTM(100, 50), # the first LSTM layer
out=L.Linear(50, 1000), # the feed-forward output layer
)
def reset_state(self):
self.mid.reset_state()
def __call__(self, cur_word):
# Given the current word ID, predict the next word.
x = self.embed(cur_word)
h = self.mid(x)
y = self.out(h)
return y
入力層 LSTM 出力層
DLの層を定義する
（今回だと右のよう
な3層）
35. LSTMをChainerをつかって実装する(４)
class RNN(Chain):
def __init__(self):
super(RNN, self).__init__(
embed=L.EmbedID(1000, 100), # word embedding
mid=L.LSTM(100, 50), # the first LSTM layer
out=L.Linear(50, 1000), # the feed-forward output layer
)
def reset_state(self):
self.mid.reset_state()
def __call__(self, cur_word):
# Given the current word ID, predict the next word.
x = self.embed(cur_word)
h = self.mid(x)
y = self.out(h)
return y
入力層 LSTM 出力層
各層に値をセットする
36. LSTMをChainerをつかって実装する(５)
class RNN(Chain):
def __init__(self):
super(RNN, self).__init__(
embed=L.EmbedID(1000, 100), # word embedding
mid=L.LSTM(100, 50), # the first LSTM layer
out=L.Linear(50, 1000), # the feed-forward output layer
)
def reset_state(self):
self.mid.reset_state()
def __call__(self, cur_word):
# Given the current word ID, predict the next word.
x = self.embed(cur_word)
h = self.mid(x)
y = self.out(h)
return y
rnn = RNN()
model = L.Classifier(rnn)
optimizer = optimizers.SGD()
optimizer.setup(model)
使用するネットワークを
定義する
37. LSTMをChainerをつかって実装する(６)
class RNN(Chain):
def __init__(self):
super(RNN, self).__init__(
embed=L.EmbedID(1000, 100), # word embedding
mid=L.LSTM(100, 50), # the first LSTM layer
out=L.Linear(50, 1000), # the feed-forward output layer
)
def reset_state(self):
self.mid.reset_state()
def __call__(self, cur_word):
# Given the current word ID, predict the next word.
x = self.embed(cur_word)
h = self.mid(x)
y = self.out(h)
return y
rnn = RNN()
model = L.Classifier(rnn)
optimizer = optimizers.SGD()
optimizer.setup(model)上記で設定したモデルを
SGDというアルゴリズムを
使って最適化する
