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
# 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=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
def __init__(self, vocab_size, wordvec_size, hidden_size):
args = vocab_size, wordvec_size, hidden_size
self.encoder = AttentionEncoder(*args)
self.decoder = AttentionDecoder(*args)
self.softmax = TimeSoftmaxWithLoss()
self.params = self.encoder.params + self.decoder.params
self.grads = self.encoder.grads + self.decoder.grads
def __init__(self, vocab_size, wordvec_size, hidden_size):
V, D, H = vocab_size, wordvec_size, hidden_size
self.encoder = Encoder(V, D, H)
self.decoder = PeekyDecoder(V, D, H)
self.softmax = TimeSoftmaxWithLoss()
self.params = self.encoder.params + self.decoder.params
self.grads = self.encoder.grads + self.decoder.grads
def __init__(self, src_vocab_size, tgt_vocab_size, wordvec_size,
hidden_size):
Vs, Vt, D, H = src_vocab_size, tgt_vocab_size, wordvec_size, hidden_size
self.encoder = Encoder(Vs, D, H)
self.decoder = Decoder(Vt, D, H)
self.softmax = TimeSoftmaxWithLoss()
self.params = self.encoder.params + self.decoder.params
self.grads = self.encoder.grads + self.decoder.grads
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
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
