class SkipGram:
def __init__(self, vocab_size, hidden_size, window_size, corpus):
# おもみ
w_in = 0.01 * np.random.randn(vocab_size, hidden_size).astype('f')
w_out = 0.01 * np.random.randn(vocab_size, hidden_size).astype('f')
# layers
self.embed_layer = Embedding(w_in)
self.ns_loss_layers = [
NegativeSamplingLoss(w_out, corpus) for _ in range(2 * window_size)
]
# おもみ, 勾配まとめ
layers = [self.embed_layer] + self.ns_loss_layers
self.params, self.grads = [], []
for l in layers:
self.params += l.params
self.grads += l.grads
# 単語の分散表現
self.word_vecs = w_in
def forward(self, contexts, target):
h = self.embed_layer.forward(target)
loss = sum([
l.forward(h, contexts[:, i])
for i, l in enumerate(self.ns_loss_layers)
])
return loss
def backward(self, dl=1):
dh = sum([l.backward(dl) for i, l in enumerate(self.ns_loss_layers)])
self.embed_layer.backward(dh)
class Decoder(Sequential):
def __init__(self, vocab_size, embedding_size, hidden_size, output_size):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.output_size = output_size
self.lstm = LSTM(embedding_size, hidden_size)
self.lstm_output = TimeDistributed(hidden_size,
output_size,
activation='tanh')
self.softmax = TimeDistributed(output_size,
vocab_size,
activation='softmax')
self.embedding = Embedding(vocab_size, embedding_size)
self.layers = [
self.lstm, self.lstm_output, self.softmax, self.embedding
]
self.params = list(
itertools.chain(*[
layer.params for layer in self.layers
if hasattr(layer, 'params')
]))
def forward(self, ec_H, ec_C, mask):
(sens_size, batch_size) = T.shape(mask)
def step(m, prev_Y, prev_H, prev_C):
"""Forward a time step of the decoder."""
# LSTM forward time step
(H, C) = self.lstm.step(prev_Y, m, prev_H, prev_C)
# LSTM output
O = self.lstm_output.forward(H)
# Apply softmax to LSTM output
P = self.softmax.forward(O)
# Make prediction
one_hot_Y = T.argmax(P, axis=1)
# Feed the output to the next time step
Y = self.embedding.forward(one_hot_Y)
# FIXME: Deal with differ length ?
return (P, Y, H, C)
results, updates = theano.scan(fn=step,
sequences=[mask],
outputs_info=[
None,
dict(initial=T.zeros(
(batch_size,
self.embedding_size)),
taps=[-1]),
dict(initial=ec_H, taps=[-1]),
dict(initial=ec_C, taps=[-1])
])
# return np.swapaxes(results[0], 0, 1) # returns the softmax probabilities
return results[0]
def forward(self, xs: np.ndarray) -> np.ndarray:
N, T = xs.shape
V, D = self.W.shape
out = np.empty((N, T, D), dtype=float)
self.layers = []
for t in range(T):
layer = Embedding(self.W)
out[:, t, :] = layer.forward(xs[:, t])
self.layers.append(layer)
return out
def forward(self, idxs):
w, = self.params
N, T = idxs.shape
V, D = w.shape # 語彙数, 分散表現の次元数
self.layers = []
ys = np.empty((N, T, D), dtype='f')
for t in range(T):
layer = Embedding(w)
ys[:, t, :] = layer.forward(idxs[:, t])
self.layers.append(layer)
return ys
class Decoder(Sequential):
def __init__(self, vocab_size, embedding_size, hidden_size, output_size):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.output_size = output_size
self.lstm = LSTM(embedding_size, hidden_size)
self.lstm_output = TimeDistributed(hidden_size, output_size, activation='tanh')
self.softmax = TimeDistributed(output_size, vocab_size, activation='softmax')
self.embedding = Embedding(vocab_size, embedding_size)
self.layers = [self.lstm, self.lstm_output, self.softmax, self.embedding]
self.params = list(itertools.chain(*[layer.params for layer in self.layers if hasattr(layer, 'params')]))
def forward(self, ec_H, ec_C, mask):
(sens_size, batch_size) = T.shape(mask)
def step(m, prev_Y, prev_H, prev_C):
"""Forward a time step of the decoder."""
# LSTM forward time step
(H, C) = self.lstm.step(prev_Y, m, prev_H, prev_C)
# LSTM output
O = self.lstm_output.forward(H)
# Apply softmax to LSTM output
P = self.softmax.forward(O)
# Make prediction
one_hot_Y = T.argmax(P, axis=1)
# Feed the output to the next time step
Y = self.embedding.forward(one_hot_Y)
# FIXME: Deal with differ length ?
return (P, Y, H, C)
results, updates = theano.scan(
fn=step,
sequences=[mask],
outputs_info=[
None,
dict(initial=T.zeros((batch_size, self.embedding_size)), taps=[-1]),
dict(initial=ec_H, taps=[-1]),
dict(initial=ec_C, taps=[-1])
]
)
# return np.swapaxes(results[0], 0, 1) # returns the softmax probabilities
return results[0]
class Encoder(Sequential):
def __init__(self, vocab_size, embedding_size, hidden_size):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.embedding = Embedding(vocab_size, embedding_size)
self.lstm = LSTM(embedding_size, hidden_size)
self.layers = [self.embedding, self.lstm]
self.params = list(itertools.chain(*[layer.params for layer in self.layers if hasattr(layer, 'params')]))
def forward(self, batch, mask):
# ``batch`` is a matrix whose row ``x`` is a sentence, e.g. x = [1, 4, 5, 2, 0]
# ``emb`` is a list of embedding matrix, e[i].shape = (sene_size, embedding_size)
emb = self.embedding.forward(batch)
(H, C) = self.lstm.forward(emb, mask)
return (H[-1], C[-1])
class EmbeddingDot:
def __init__(self, w):
self.embed = Embedding(w)
self.params = self.embed.params
self.grads = self.embed.grads
self.cache = None
def forward(self, h, idx):
w_idx = self.embed.forward(idx)
s = np.sum(h * w_idx, axis=1)
self.cache = (h, w_idx)
return s
def backward(self, ds):
ds = ds.reshape(ds.shape[0], 1) # ???
h, w_idx = self.cache
dw_idx = ds * h
self.embed.backward(dw_idx)
dh = ds * w_idx
return dh
class Encoder(Sequential):
def __init__(self, vocab_size, embedding_size, hidden_size):
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.embedding = Embedding(vocab_size, embedding_size)
self.lstm = LSTM(embedding_size, hidden_size)
self.layers = [self.embedding, self.lstm]
self.params = list(
itertools.chain(*[
layer.params for layer in self.layers
if hasattr(layer, 'params')
]))
def forward(self, batch, mask):
# ``batch`` is a matrix whose row ``x`` is a sentence, e.g. x = [1, 4, 5, 2, 0]
# ``emb`` is a list of embedding matrix, e[i].shape = (sene_size, embedding_size)
emb = self.embedding.forward(batch)
(H, C) = self.lstm.forward(emb, mask)
return (H[-1], C[-1])
class EmbeddingDot:
def __init__(self, W: np.ndarray) -> None:
self.embed = Embedding(W)
self.params = self.embed.params
self.grads = self.embed.grads
self.cache = None
def forward(self, h: np.ndarray, idx: List[int]):
target_W = self.embed.forward(idx)
out = np.sum(target_W * h, axis=1)
self.cache = (h, target_W)
return out
def backward(self, dout: np.ndarray) -> np.ndarray:
h, target_W = self.cache
dout = dout.reshape(dout.shape[0], 1)
dtarget_W = dout * h
self.embed.backward(dtarget_W)
dh = dout * target_W
return dh
class LSTM_DQN(torch.nn.Module):
model_name = 'lstm_dqn'
def __init__(self,
model_config,
word_vocab,
generate_length=5,
enable_cuda=False):
super(LSTM_DQN, self).__init__()
self.model_config = model_config
self.enable_cuda = enable_cuda
self.word_vocab_size = len(word_vocab)
self.id2word = word_vocab
self.generate_length = generate_length
self.read_config()
self._def_layers()
self.init_weights()
# self.print_parameters()
def print_parameters(self):
amount = 0
for p in self.parameters():
amount += np.prod(p.size())
print("total number of parameters: %s" % (amount))
parameters = filter(lambda p: p.requires_grad, self.parameters())
amount = 0
for p in parameters:
amount += np.prod(p.size())
print("number of trainable parameters: %s" % (amount))
def read_config(self):
# model config
self.embedding_size = self.model_config['embedding_size']
self.encoder_rnn_hidden_size = self.model_config[
'encoder_rnn_hidden_size']
self.action_scorer_hidden_dim = self.model_config[
'action_scorer_hidden_dim']
self.dropout_between_rnn_layers = self.model_config[
'dropout_between_rnn_layers']
def _def_layers(self):
# word embeddings
self.word_embedding = Embedding(embedding_size=self.embedding_size,
vocab_size=self.word_vocab_size,
enable_cuda=self.enable_cuda)
# lstm encoder
self.encoder = FastUniLSTM(
ninp=self.embedding_size,
nhids=self.encoder_rnn_hidden_size,
dropout_between_rnn_layers=self.dropout_between_rnn_layers)
self.action_scorer_shared = torch.nn.Linear(
self.encoder_rnn_hidden_size[-1], self.action_scorer_hidden_dim)
action_scorers = []
for _ in range(self.generate_length):
action_scorers.append(
torch.nn.Linear(self.action_scorer_hidden_dim,
self.word_vocab_size,
bias=False))
self.action_scorers = torch.nn.ModuleList(action_scorers)
self.fake_recurrent_mask = None
def init_weights(self):
torch.nn.init.xavier_uniform_(self.action_scorer_shared.weight.data)
for i in range(len(self.action_scorers)):
torch.nn.init.xavier_uniform_(self.action_scorers[i].weight.data)
self.action_scorer_shared.bias.data.fill_(0)
def representation_generator(self, _input_words):
embeddings, mask = self.word_embedding.forward(
_input_words) # batch x time x emb
encoding_sequence, _, _ = self.encoder.forward(
embeddings, mask) # batch x time x h
mean_encoding = masked_mean(encoding_sequence, mask) # batch x h
return mean_encoding
def action_scorer(self, state_representation):
hidden = self.action_scorer_shared.forward(
state_representation) # batch x hid
hidden = F.relu(hidden) # batch x hid
action_ranks = []
for i in range(len(self.action_scorers)):
action_ranks.append(
self.action_scorers[i].forward(hidden)) # batch x n_vocab
return action_ranks
