def train_forward(self, x: Variable, h=None):
"""
:param x: shape: (batch_size, sequence_length, vocab_size)
:param h:
:return:
"""
if self.initialize:
self.u = Variable(np.random.normal(
0, 1, (x.shape[1], self.hidden_units)),
trainable=True)
self.initialize = False
if h is None:
# In the first RNNcell, we don't have any hidden layers, so we initialize one
h = Variable(
np.random.normal(0, 1, (x.shape[0], self.hidden_units)))
xu = x.dot(self.u)
hw = h.dot(self.w)
self.a = xu + hw + self.b
self.h = self.activation(self.a)
self.o = self.h.dot(self.v) + self.c
return self.o, self.h
def forward(self, x: Variable):
"""
:param x: x [:::::: vocab_size] a one-hot value
Thing is, we don't really care what's at the front.
We only need to use the last dimension ( which must be a one-hot) to find its mapping.
:return:
"""
self.vocab_size = x.shape[-1]
if self.initialize:
self.mapping = Variable(np.random.normal(0, 1, (self.vocab_size, self.embed_size)),
trainable=True)
self.initialize = False
# First, find the corresponding word representation
embedded_word = x.dot(self.mapping) # n x embed_size
return embedded_word
