class Encoder:
def __init__(self, vocab_size, wordvec_size, hidden_size):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
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')
self.embed = TimeEmbedding(embed_W)
self.lstm = TimeLSTM(lstm_Wx, lstm_Wh, lstm_b, stateful=False)
self.params = self.embed.params + self.lstm.params
self.grads = self.embed.grads + self.lstm.grads
self.hs = None
def forward(self, xs):
xs = self.embed.forward(xs)
hs = self.lstm.forward(xs)
self.hs = hs
return hs[:, -1, :]
def backward(self, dh):
dhs = np.zeros_like(self.hs)
dhs[:, -1, :] = dh
dout = self.lstm.backward(dhs)
dout = self.embed.backward(dout)
return dout
class AttnBiEncoder:
def __init__(self, vocab_size, wordvec_size, hidden_size):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
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(D, 4 * H) / np.sqrt(D)).astype('f')
lstm_Wh2 = (rn(H, 4 * H) / np.sqrt(H)).astype('f')
lstm_b2 = np.zeros(4 * H).astype('f')
self.embed = TimeEmbedding(embed_W)
self.lstm = TimeBiLSTM(lstm_Wx1,
lstm_Wh1,
lstm_b1,
lstm_Wx2,
lstm_Wh2,
lstm_b2,
stateful=False)
self.params = self.embed.params + self.lstm.params
self.grads = self.embed.grads + self.lstm.grads
self.hs = None
def forward(self, xs):
xs = self.embed.forward(xs)
hs = self.lstm.forward(xs)
return hs
def backward(self, dhs):
dout = self.lstm.backward(dhs)
dout = self.embed.backward(dout)
return dout
class Decoder:
def __init__(self, vocab_size, wordvec_size, hidden_size):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
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')
self.embed = TimeEmbedding(embed_W)
self.lstm = TimeLSTM(lstm_Wx, lstm_Wh, lstm_b, stateful=True)
self.affine = TimeAffine(affine_W, affine_b)
self.params, self.grads = [], []
for layer in (self.embed, self.lstm, self.affine):
self.params += layer.params
self.grads += layer.grads
def forward(self, xs, h):
self.lstm.set_state(h)
out = self.embed.forward(xs)
out = self.lstm.forward(out)
score = self.affine.forward(out)
return score
def backward(self, dscore):
dout = self.affine.backward(dscore)
dout = self.lstm.backward(dout)
dout = self.embed.backward(dout)
dh = self.lstm.dh
return dh
def generate(self, h, start_id, sample_size):
'''
h: Encoderから受け取る隠れ状態
start_id: 最初に与える文字ID
sample_size: 生成する文字数
'''
sampled = []
sample_id = start_id
self.lstm.set_state(h)
for _ in range(sample_size):
x = np.array(sample_id).reshape((1, 1))
out = self.embed.forward(x)
out = self.lstm.forward(out)
score = self.affine.forward(out)
sample_id = np.argmax(score.flatten())
sampled.append(int(sample_id))
return sampled
def __init__(self, vocab_size, wordvec_size, hidden_size):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
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')
self.embed = TimeEmbedding(embed_W)
self.lstm = TimeLSTM(lstm_Wx, lstm_Wh, lstm_b, stateful=False)
self.params = self.embed.params + self.lstm.params
self.grads = self.embed.grads + self.lstm.grads
self.hs = None
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: int = 10000,
wordvec_size: int = 100,
hidden_size: int = 100) -> None:
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
# Initialize of weights
embed_W = (rn(V, D) / 100).astype('f')
lstm_Wx = (rn(D, 4 * H) / np.sqrt(D).astype('f'))
lstm_Wh = (rn(D, 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')
# Generating 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]
# Conclude all of weights and grads as a list
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
def __init__(self, vocabulary_size, wordvec_size, hidden_size):
V, D, H = vocabulary_size, wordvec_size, hidden_size
rn = np.random.randn
# Initialize 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')
# generate 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]
# list all weights and gradiants
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
def __init__(self, vocab_size, wordvec_size, hidden_size):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
# 重みの初期化
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')
# レイヤの生成
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]
# 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, wordvec_size, hidden_size):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
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')
self.embed = TimeEmbedding(embed_W)
self.lstm = TimeLSTM(lstm_Wx, lstm_Wh, lstm_b, stateful=True)
self.affine = TimeAffine(affine_W, affine_b)
self.params, self.grads = [], []
for layer in (self.embed, self.lstm, self.affine):
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
embed_W = (np.random.randn(V, D) / 100).astype(np.float32)
lstm_Wx = (np.random.randn(D, 4 * H) / np.sqrt(D)).astype(np.float32)
lstm_Wh = (np.random.randn(H, 4 * H) / np.sqrt(H)).astype(np.float32)
lstm_b = np.zeros(4 * H).astype(np.float32)
affine_W = (np.random.randn(2 * H, V) / np.sqrt(2 * H)).astype(np.float32)
affine_b = np.zeros(V).astype(np.float32)
self.embed = TimeEmbedding(embed_W)
self.lstm = TimeLSTM(lstm_Wx, lstm_Wh, lstm_b, statefull=True)
self.attention = TimeAttention()
self.affine = TimeAffine(affine_W, affine_b)
layers = [self.embed, self.lstm, self.attention, self.affine]
self.params, self.grads = [], []
for layer in layers:
self.params += layer.params
self.grads += layer.grads
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
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
# initializing weight
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')
# generating layers
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)
]
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]]
# gathering weights and gradients
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
class PeekyDecoder:
def __init__(self, vocab_size, wordvec_size, hidden_size):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
embed_W = (rn(V, D) / 100).astype('f')
lstm_Wx = (rn(H + D, 4 * H) / np.sqrt(H + 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 + H, V) / np.sqrt(H + H)).astype('f')
affine_b = np.zeros(V).astype('f')
self.embed = TimeEmbedding(embed_W)
self.lstm = TimeLSTM(lstm_Wx, lstm_Wh, lstm_b, stateful=True)
self.affine = TimeAffine(affine_W, affine_b)
self.params, self.grads = [], []
for layer in (self.embed, self.lstm, self.affine):
self.params += layer.params
self.grads += layer.grads
self.cache = None
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 backward(self, dscore):
H = self.cache
dout = self.affine.backward(dscore)
dout, dhs0 = dout[:, :, H:], dout[:, :, :H]
dout = self.lstm.backward(dout)
dembed, dhs1 = dout[:, :, H:], dout[:, :, :H]
self.embed.backward(dembed)
dhs = dhs0 + dhs1
dh = self.lstm.dh + np.sum(dhs, axis=1)
return dh
def generate(self, h, start_id, sample_size):
'''
h: Encoderから受け取る隠れ状態
start_id: 最初に与える文字ID
sample_size: 生成する文字数
'''
sampled = []
sample_id = start_id
self.lstm.set_state(h)
for _ in range(sample_size):
x = np.array(sample_id).reshape((1, 1))
out = self.embed.forward(x)
out = self.lstm.forward(out)
score = self.affine.forward(out)
sample_id = np.argmax(score.flatten())
sampled.append(int(sample_id))
return sampled
class AttentionDecoder:
def __init__(self, vocab_size, wordvec_size, hidden_size):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
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(2 * H, V) / np.sqrt(2 * H)).astype('f')
affine_b = np.zeros(V).astype('f')
self.embed = TimeEmbedding(embed_W)
self.lstm = TimeLSTM(lstm_Wx, lstm_Wh, lstm_b, stateful=True)
self.attention = TimeAttention()
self.affine = TimeAffine(affine_W, affine_b)
layers = [self.embed, self.lstm, self.attention, self.affine]
self.params, self.grads = [], []
for layer in layers:
self.params += layer.params
self.grads += layer.grads
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 backward(self, dscore):
dout = self.affine.backward(dscore)
_, _, H2 = dout.shape
H = H2 // 2
dc, ddec_hs0 = dout[:, :, :H], dout[:, :, H:]
denc_hs, ddec_hs1 = self.attention.backward(dc)
ddec_hs = ddec_hs0 + ddec_hs1
dout = self.lstm.backward(ddec_hs)
dh = self.lstm.dh
denc_hs[:, -1] += dh
self.embed.backward(dout)
return denc_hs
def generate(self, enc_hs, start_id, sample_size):
sampled = []
sample_id = start_id
h = enc_hs[:, -1]
self.lstm.set_state(h)
for _ in range(sample_size):
x = np.array([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
class PeekyDecoder:
def __init__(self, vocab_size, wordvec_size, hidden_size):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
embed_W = (rn(V, D) / 100).astype(np.float32)
lstm_Wx = (rn(H + D, 4 * H) / np.sqrt(H + D)).astype(np.float32)
lstm_Wh = (rn(H, 4 * H) / np.sqrt(H)).astype(np.float32)
lstm_b = np.zeros(4 * H, dtype=np.float32)
affine_W = (rn(H + H, V) / np.sqrt(H + H)).astype(np.float32)
affine_b = np.zeros(V, dtype=np.float32)
self.embed = TimeEmbedding(embed_W)
self.lstm = TimeLSTM(lstm_Wx, lstm_Wh, lstm_b, statefull=True)
self.affine = TimeAffine(affine_W, affine_b)
self.params, self.grads = [], []
for layer in (self.embed, self.lstm, self.affine):
self.params += layer.params
self.grads += layer.grads
self.cache = None
def forward(self, xs, h):
N, T = xs.shape
N, H = h.shape
self.lstm.set_state(h)
out = self.embed.forward(xs)
# TimeLSTMへの入力を拡張する
hs = np.repeat(h, T, axis=0).reshape(N, T, H)
out = np.concatenate((hs, out), axis=2) # (N, T, H + D)
# TimeAffineへの入力を拡張する
out = self.lstm.forward(out)
out = np.concatenate((hs, out), axis=2)
score = self.affine.forward(out)
self.cache = H
return score
def backward(self, dscore):
H = self.cache
dout = self.affine.backward(dscore)
dout, dhs0 = dout[:, :, H:], dout[:, :, :H]
dout = self.lstm.backward(dout)
dembed, dhs1 = dout[:, :, H:], dout[:, :, :H]
self.embed.backward(dembed)
dhs = dhs0 + dhs1
dh = self.lstm.dh + np.sum(dhs, axis=1) # 時間方向に集約
return dh
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