def __init__(self, vocab_size=10000, wordvec_size=100, hidden_size=100):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
# initializing weights
embed_W = (rn(V, D) / 100).astype('f')
lstm_Wx = (rn(D, 4 * H) / np.sqrt(D)).astype('f')
lstm_Wh = (rn(H, 4 * H) / np.sqrt(H)).astype('f')
lstm_b = np.zeros(4 * H).astype('f')
affine_W = (rn(H, V) / np.sqrt(H)).astype('f')
affine_b = np.zeros(V).astype('f')
# generate each layers
self.layers = [
TimeEmbedding(embed_W),
TimeLSTM(lstm_Wx, lstm_Wh, lstm_b, stateful=True),
TimeAffine(affine_W, affine_b)
]
self.loss_layer = TimeSoftmaxWithLoss()
self.lstm_layer = self.layers[1]
# gather all weights and gradients
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
def __init__(self, vocab_size, wordvec_size, hidden_size):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
# Initialize of weights
embed_W = (rn(V, D) / 100).astype("f")
rnn_Wx = (rn(D, H) / np.sqrt(D)).astype("f")
rnn_Wh = (rn(H, H) / np.sqrt(H)).astype("f")
rnn_b = np.zeros(H).astype("f")
affine_W = (rn(H, V) / np.sqrt(H)).astype("f")
affine_b = np.zeros(V).astype("f")
# Making layers
self.layers = [
TimeEmbedding(embed_W),
TimeRNN(rnn_Wx, rnn_Wh, rnn_b, stateful=True),
TimeAffine(affine_W, affine_b),
]
self.loss_layer = TimeSoftmaxWithLoss()
self.rnn_layer = self.layers[1]
# Conclude all of weights & grads
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
class Rnnlm:
def __init__(self, vocab_size=10000, wordvec_size=100, hidden_size=100):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
# initializing weights
embed_W = (rn(V, D) / 100).astype('f')
lstm_Wx = (rn(D, 4 * H) / np.sqrt(D)).astype('f')
lstm_Wh = (rn(H, 4 * H) / np.sqrt(H)).astype('f')
lstm_b = np.zeros(4 * H).astype('f')
affine_W = (rn(H, V) / np.sqrt(H)).astype('f')
affine_b = np.zeros(V).astype('f')
# generate each layers
self.layers = [
TimeEmbedding(embed_W),
TimeLSTM(lstm_Wx, lstm_Wh, lstm_b, stateful=True),
TimeAffine(affine_W, affine_b)
]
self.loss_layer = TimeSoftmaxWithLoss()
self.lstm_layer = self.layers[1]
# gather all weights and gradients
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
def predict(self, xs):
for layer in self.layers:
xs = layer.forward(xs)
return xs
def forward(self, xs, ts):
score = self.predict(xs)
loss = self.loss_layer.forward(score, ts)
return loss
def backward(self, dout=1):
dout = self.loss_layer.backward(dout)
for layer in reversed(self.layers):
dout = layer.backward(dout)
return dout
def reset_state(self):
self.lstm_layer.reset_state()
def save_params(self, file_name="Rnnlm.pkl"):
with open(file_name, "wb") as fout:
pickle.dump(self.params, fout)
def load_params(self, file_name="Rnnlm.pkl"):
with open(file_name, "rb") as fin:
self.params = pickle.load(fin)
def __init__(self,
vocab_size,
wordvec_size,
head_size,
num_heads,
num_encoders=3,
num_decoders=3):
S, D, H = vocab_size, wordvec_size, head_size
rn = np.random.randn
self.num_encoders = num_encoders
self.num_decoders = num_decoders
self.params, self.grads = [], []
# Double embed (encoder, decoder)
embed_W1 = (rn(S, D) / 100).astype('f')
self.e_embed = PositionalEmbedding(embed_W1)
self.params += self.e_embed.params
self.grads += self.e_embed.grads
self.encoders, self.decoders = [], []
for _ in range(num_encoders):
te = TransformerEncoder(wordvec_size=D,
head_size=H,
num_heads=num_heads)
self.encoders.append(te)
self.params += te.params
self.grads += te.grads
for _ in range(num_decoders):
td = TransformerDecoder(wordvec_size=D,
head_size=H,
num_heads=num_heads)
self.decoders.append(td)
self.params += td.params
self.grads += td.grads
# 편의를 위해 linear 변수에 따로 weight 저장
self.linear = MatMul((rn(D, S) / np.sqrt(D)).astype('f'))
self.params += self.linear.params
self.grads += self.linear.grads
# TimeSoftmaxWithLoss도 params와 grads가 있으나 사용되지 않기때문에 생략
self.softmax = TimeSoftmaxWithLoss(ignore_label=-1)
def __init__(self,
vocab_size=10000,
word_vec=650,
hidden_size=0.5,
dropout_ratio=0.5):
"""Rnnの改良版
LSTMの多層化(2層)
Dropoutを使用(深さ方向に使用)
重み共有(EmbeddingレイヤとAffineレイヤで重み共有)
"""
V, D, H = vocab_size, word_vec, hidden_size
rn = np.random.randn
# 重みの初期化
embed_W = (rn(V, D) / 100).astype('f')
lstm_Wx1 = (rn(D, 4 * H) / np.sqrt(D)).astype('f')
lstm_Wh1 = (rn(H, 4 * H) / np.sqrt(H)).astype('f')
lstm_b1 = np.zeros(4 * H).astype('f')
lstm_Wx2 = (rn(H, 4 * H) / np.sqrt(H)).astype('f')
lstm_Wh2 = (rn(H, 4 * H) / np.sqrt(H)).astype('f')
lstm_b2 = np.zeros(4 * H).astype('f')
affine_b = np.zeros(V).astype('f')
# 3つの改善
self.layers = [
TimeEmbedding(embed_W),
TimeDropout(dropout_ratio),
TimeLSTM(lstm_Wx1, lstm_Wh1, lstm_b1, stateful=True),
TimeDropout(dropout_ratio),
TimeLSTM(lstm_Wx2, lstm_Wh2, lstm_b2, stateful=True),
TimeDropout(dropout_ratio),
TimeAffine(embed_W.T, affine_b) # 重み共有
]
self.loss_layer = TimeSoftmaxWithLoss()
self.lstm_layers = [self.layers[2], self.layers[4]]
self.drop_layers = [self.layers[1], self.layers[3], self.layers[5]]
# 全ての重みと勾配をリストにまとめる
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
def __init__(self,
vocab_size=10000,
wordvec_size=650,
hidden_size=650,
dropout_ratio=0.5):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
embed_W = (rn(V, D) / 100).astype(np.float32)
lstm_Wx1 = (rn(D, 4 * H) / np.sqrt(D)).astype(np.float32)
lstm_Wh1 = (rn(H, 4 * H) / np.sqrt(H)).astype(np.float32)
lstm_b1 = np.zeros(4 * H).astype(np.float32)
lstm_Wx2 = (rn(D, 4 * H) / np.sqrt(D)).astype(np.float32)
lstm_Wh2 = (rn(H, 4 * H) / np.sqrt(H)).astype(np.float32)
lstm_b2 = np.zeros(4 * H).astype(np.float32)
affine_b = np.zeros(V).astype(np.float32)
# 3つの改善
# 1) LSTM層を重ねる
# 2) Dropout層の追加 (深さ方向でLSTM層の間に追加)
# 3) 重み共有 Time Embedding層とTime Affine層 @ W(V, D)
self.layers = [
TimeEmbedding(embed_W),
TimeDropout(dropout_ratio),
TimeLSTM(lstm_Wx1, lstm_Wh1, lstm_b1, statefull=True),
TimeDropout(dropout_ratio),
TimeLSTM(lstm_Wx2, lstm_Wh2, lstm_b2, statefull=True),
TimeDropout(dropout_ratio),
TimeAffine(embed_W.T, affine_b) # embed_W(V, D)とembed_W.T(D, V)を共有
]
self.loss_layer = TimeSoftmaxWithLoss()
self.lstm_layers = [self.layers[2], self.layers[4]]
self.drop_layers = [self.layers[1], self.layers[3], self.layers[5]]
# 重みと勾配をまとめる
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
class BetterRnnlm(BaseModel):
def __init__(self,
vocab_size=10000,
word_vec=650,
hidden_size=0.5,
dropout_ratio=0.5):
"""Rnnの改良版
LSTMの多層化(2層)
Dropoutを使用(深さ方向に使用)
重み共有(EmbeddingレイヤとAffineレイヤで重み共有)
"""
V, D, H = vocab_size, word_vec, hidden_size
rn = np.random.randn
# 重みの初期化
embed_W = (rn(V, D) / 100).astype('f')
lstm_Wx1 = (rn(D, 4 * H) / np.sqrt(D)).astype('f')
lstm_Wh1 = (rn(H, 4 * H) / np.sqrt(H)).astype('f')
lstm_b1 = np.zeros(4 * H).astype('f')
lstm_Wx2 = (rn(H, 4 * H) / np.sqrt(H)).astype('f')
lstm_Wh2 = (rn(H, 4 * H) / np.sqrt(H)).astype('f')
lstm_b2 = np.zeros(4 * H).astype('f')
affine_b = np.zeros(V).astype('f')
# 3つの改善
self.layers = [
TimeEmbedding(embed_W),
TimeDropout(dropout_ratio),
TimeLSTM(lstm_Wx1, lstm_Wh1, lstm_b1, stateful=True),
TimeDropout(dropout_ratio),
TimeLSTM(lstm_Wx2, lstm_Wh2, lstm_b2, stateful=True),
TimeDropout(dropout_ratio),
TimeAffine(embed_W.T, affine_b) # 重み共有
]
self.loss_layer = TimeSoftmaxWithLoss()
self.lstm_layers = [self.layers[2], self.layers[4]]
self.drop_layers = [self.layers[1], self.layers[3], self.layers[5]]
# 全ての重みと勾配をリストにまとめる
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
def predict(self, xs, train_flg=False):
for layer in self.drop_layers:
layer.train_flg = train_flg
for layer in self.layers:
xs = layer.forward(xs)
return xs
def forward(self, xs, ts, train_flg=True):
score = self.predict(xs, train_flg)
loss = self.loss_layer.forward(score, ts)
return loss
def backward(self, dout=1):
dout = self.loss_layer.backward(dout)
for layer in reversed(self.layers):
dout = layer.backward(dout)
return dout
def reset_state(self):
for layer in self.lstm_layers:
layer.reset_state()
class BetterRnnlm(BaseModel):
def __init__(self,
vocab_size=10000,
wordvec_size=650,
hidden_size=650,
dropout_ratio=0.5):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
embed_W = (rn(V, D) / 100).astype(np.float32)
lstm_Wx1 = (rn(D, 4 * H) / np.sqrt(D)).astype(np.float32)
lstm_Wh1 = (rn(H, 4 * H) / np.sqrt(H)).astype(np.float32)
lstm_b1 = np.zeros(4 * H).astype(np.float32)
lstm_Wx2 = (rn(D, 4 * H) / np.sqrt(D)).astype(np.float32)
lstm_Wh2 = (rn(H, 4 * H) / np.sqrt(H)).astype(np.float32)
lstm_b2 = np.zeros(4 * H).astype(np.float32)
affine_b = np.zeros(V).astype(np.float32)
# 3つの改善
# 1) LSTM層を重ねる
# 2) Dropout層の追加 (深さ方向でLSTM層の間に追加)
# 3) 重み共有 Time Embedding層とTime Affine層 @ W(V, D)
self.layers = [
TimeEmbedding(embed_W),
TimeDropout(dropout_ratio),
TimeLSTM(lstm_Wx1, lstm_Wh1, lstm_b1, statefull=True),
TimeDropout(dropout_ratio),
TimeLSTM(lstm_Wx2, lstm_Wh2, lstm_b2, statefull=True),
TimeDropout(dropout_ratio),
TimeAffine(embed_W.T, affine_b) # embed_W(V, D)とembed_W.T(D, V)を共有
]
self.loss_layer = TimeSoftmaxWithLoss()
self.lstm_layers = [self.layers[2], self.layers[4]]
self.drop_layers = [self.layers[1], self.layers[3], self.layers[5]]
# 重みと勾配をまとめる
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
def predict(self, xs, train_flg=False):
for layer in self.drop_layers:
layer.train_flg = train_flg
for layer in self.layers:
xs = layer.forward(xs)
return xs
def forward(self, xs, ts, train_flg=True):
score = self.predict(xs, train_flg)
loss = self.loss_layer.forward(score, ts)
return loss
def backward(self, dout=1):
dout = self.loss_layer.backward(dout)
for layer in reversed(self.layers):
dout = layer.backward(dout)
return dout
def reset_state(self):
for layer in self.lstm_layers:
layer.reset_state()
class Transformer(BaseModel):
def __init__(self,
vocab_size,
wordvec_size,
head_size,
num_heads,
num_encoders=3,
num_decoders=3):
S, D, H = vocab_size, wordvec_size, head_size
rn = np.random.randn
self.num_encoders = num_encoders
self.num_decoders = num_decoders
self.params, self.grads = [], []
# Double embed (encoder, decoder)
embed_W1 = (rn(S, D) / 100).astype('f')
self.e_embed = PositionalEmbedding(embed_W1)
self.params += self.e_embed.params
self.grads += self.e_embed.grads
self.encoders, self.decoders = [], []
for _ in range(num_encoders):
te = TransformerEncoder(wordvec_size=D,
head_size=H,
num_heads=num_heads)
self.encoders.append(te)
self.params += te.params
self.grads += te.grads
for _ in range(num_decoders):
td = TransformerDecoder(wordvec_size=D,
head_size=H,
num_heads=num_heads)
self.decoders.append(td)
self.params += td.params
self.grads += td.grads
# 편의를 위해 linear 변수에 따로 weight 저장
self.linear = MatMul((rn(D, S) / np.sqrt(D)).astype('f'))
self.params += self.linear.params
self.grads += self.linear.grads
# TimeSoftmaxWithLoss도 params와 grads가 있으나 사용되지 않기때문에 생략
self.softmax = TimeSoftmaxWithLoss(ignore_label=-1)
def forward(self, xs, ts):
# xs->(N,T) / eout, dout, ts->N,(T,D)
eout = self.e_embed.forward(xs)
dout = self.e_embed.forward(ts)
N, T, D = eout.shape
for encoder in self.encoders:
eout = encoder.forward(eout)
for decoder in self.decoders:
ts = decoder.forward(dout, eout)
ts = ts.reshape(N * T, D)
# score->(N*T,S)
score = self.linear.forward(ts)
_, S = score.shape
# 순서 주의 score는 linear된 2차원 행렬, xs는 임베딩되기전 2차원 행렬
# loss->(N*T,1)
score = score.reshape(N, T, S)
loss = self.softmax.forward(score, xs)
return loss
def backward(self, dout=1):
# dout->N,(T,S)
dout = self.softmax.backward(dout)
N, T, S = dout.shape
dout = dout.reshape(N * T, S)
# dout->(N*T,S) / self.linear.W->(D,S)
dout = self.linear.backward(dout)
# dout->(N*T,D)
_, D = dout.shape
dout = dout.reshape(N, T, D)
# ddout->N,(T,D)
for i in range(self.num_decoders - 1, 0, -1):
_, dout = self.decoders[i].backward(dout)
ddout, dout = self.decoders[0].backward(dout)
# dout->N,(T,D)
for i in range(self.num_encoders - 1, -1, -1):
ddout = self.encoders[i].backward(ddout)
self.e_embed.backward(ddout)
def generate(self, xs, type='GPT'):
sampled = []
# 'GPT'는 transformer의 decoder만 이용
if type == 'GPT':
# xs->(T,), out->(T,D)
out = self.e_embed.forward(xs)
# out->(1,T,D)
# out = out[np.newaxis,:]
for i in range(self.num_decoders):
out = self.decoders[i].generate(out)
# out->(1,T,D)
N, T, D = out.shape
out = out.reshape(N * T, D)
# score->(1,T,S)
score = self.linear.forward(out)
sampled = np.argmax(score, axis=-1).flatten()
# 'BERT'는 transformer의 encoder만 이용
# 하지만 아직 masking 처리가 되어있지 않은 구조고
# positional embedding 이외에 segment embedding이 추가되어야함
# 따라서 현재 이 코드에서 BERT는 사용하는 의미가 없으며 GPT를 이용해야함
elif type == 'BERT':
# xs->(T,), out->(T,D)
out = self.e_embed.forward(xs)
# out->(1,T,D)
out = out[np.newaxis, :]
for i in range(self.num_encoders):
out = self.encoders[i].generate(out)
# decoder의 linear를 그대로 이용하기로 하자
N, T, D = out.shape
out = out.reshape(N * T, D)
# score->(1,T,S)
score = self.linear.forward(out)
sampled = np.argmax(score, axis=-1).flatten()
else:
print('invalid generate type')
return sampled
