class AttentionRiEmbedding(object):
def __init__(self, dict_path, words_to_load=None, normalize=True):
self.dictionary = RiDictionary(dict_path, words_to_include=words_to_load, normalize=normalize)
# Init thetas to ones
self.thetas = np.ones((self.dictionary.n+1, self.dictionary.k*2), dtype="float32") # +1 for the zero vector
# Create theno variables
self.contexts_var = T.ftensor3("contexts")
self.thetas_var = theano.shared(self.thetas, "thetas")
self.theta_idxs_var = T.ivector("theta_idxs")
self.idx_var = T.ivector("idx")
@property
def d(self):
return self.dictionary.d
def get_embeddings_expr(self):
res, upd = theano.scan(lambda i: T.dot(self.thetas_var[self.theta_idxs_var[i],:],self.contexts_var[:,:,i]), self.idx_var)
return res
def get_embeddings(self, words):
# TODO: Implement
pass
def has(self, word):
return self.dictionary.has(word)
def get_variable_vars(self):
return [self.contexts_var, self.theta_idxs_var, self.idx_var]
def get_variables(self, words):
contexts = np.empty((2*self.dictionary.k, self.d, len(words)), dtype="float32")
theta_idxs = []
idx = []
i = 0
for word in words:
if word == "##zero##":
contexts[:,:,i] = np.zeros((2*self.dictionary.k, self.d), dtype="float32")
theta_idxs.append(self.dictionary.n)
idx.append(i)
i += 1
else:
context = self.dictionary.get_context(word)
if context is not None:
contexts[:,:,i] = context
theta_idxs.append(self.dictionary.get_word_meta(word).dict_idx)
idx.append(i)
i += 1
return [contexts[:,:,0:i], theta_idxs, idx]
def get_update_parameter_vars(self):
return [self.thetas_var]
def reset(self):
self.thetas_var.set_value(np.ones((self.dictionary.n+1, self.dictionary.k*2), dtype="float32"))
def __init__(self, dict_path, words_to_load=None, normalize=True):
self.dictionary = RiDictionary(dict_path, words_to_include=words_to_load, normalize=normalize)
# Init thetas to ones
self.thetas = np.ones((self.dictionary.n+1, self.dictionary.k*2), dtype="float32") # +1 for the zero vector
# Create theno variables
self.contexts_var = T.ftensor3("contexts")
self.thetas_var = theano.shared(self.thetas, "thetas")
self.theta_idxs_var = T.ivector("theta_idxs")
self.idx_var = T.ivector("idx")
