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
# Initialize Weights
embed_W = (rn(V, D) / 100).astype('f')
lstm_Wx = (rn(D, 4 * H) / np.sqrt(D)).astype('f') # Xavier Initialize
lstm_Wh = (rn(H, 4 * H) / np.sqrt(H)).astype('f') # Xavier Initialize
lstm_b = np.zeros(4 * H).astype('f')
affine_W = (rn(H, V) / np.sqrt(H)).astype('f') # Xavier Initialize
affine_b = np.zeros(V).astype('f')
# Create 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]
# Aggregate 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
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)
# statefule=False : 짧은 시계열 데이터가 여러 개인 문제이므로,
# 문제마다 LSTM 은닉상태를 다시 초기화한 상태(영벡터)로 설정함
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, 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=10000,
wordvec_size=650,
hidden_size=650,
dropout_ratio=0.5):
V, D, H = vocab_size, wordvec_size, hidden_size
rn = np.random.randn
# Initialize Weights
embed_W = (rn(V, D) / 100).astype('f')
lstm_Wx1 = (rn(D, 4 * H) / np.sqrt(D)).astype('f') # Xavier Initialize
lstm_Wh1 = (rn(H, 4 * H) / np.sqrt(H)).astype('f') # Xavier Initialize
lstm_b1 = np.zeros(4 * H).astype('f')
lstm_Wx2 = (rn(D, 4 * H) / np.sqrt(H)).astype('f') # Xavier Initialize
lstm_Wh2 = (rn(H, 4 * H) / np.sqrt(H)).astype('f') # Xavier Initialize
lstm_b2 = np.zeros(4 * H).astype('f')
affine_b = np.zeros(V).astype('f')
# 세 가지 개선
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) # Weight Tying
]
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]]
# Aggregate all Weights and Gradients
self.params, self.grads = [], []
for layer in self.layers:
self.params += layer.params
self.grads += layer.grads
