def forward(self, input, hidden=None):
'''
Args:
input (Tensor): (effective_batch_size,input_size)
hidden (Tensor,None): (effective_batch_size,hidden_size)
Return:
Tensor: (effective_batch_size,hidden_size)
'''
# TODO: INSTRUCTIONS
# Perform matrix operations to construct the intermediary value from input and hidden tensors
# Remeber to handle the case when hidden = None. Construct a tensor of appropriate size, filled with 0s to use as the hidden.
#raise NotImplementedError('Implement Forward')
effective_batch_size, input_size = input.shape
if hidden is None:
requires_grad = True
hidden = Tensor(np.zeros((effective_batch_size, self.hidden_size)),
requires_grad=requires_grad,
is_leaf=not requires_grad)
sigmoid_ = Sigmoid()
tanh_ = Tanh()
r_t = sigmoid_(
input.matmul(self.weight_ir) + self.bias_ir +
hidden.matmul(self.weight_hr) + self.bias_hr)
z_t = sigmoid_(
input.matmul(self.weight_iz) + self.bias_iz +
hidden.matmul(self.weight_hz) + self.bias_hz)
n_t = tanh_(
input.matmul(self.weight_in) + self.bias_in + r_t *
(hidden.matmul(self.weight_hn) + self.bias_hn))
h_t = (Tensor(1) - z_t) * n_t + z_t * hidden
return h_t
def forward(self, x):
"""
Args:
x (Tensor): (batch_size, in_features)
Returns:
Tensor: (batch_size, out_features)
"""
return Tensor.matmul(x,self.weight.T()) + self.bias
def forward(self, input, hidden=None):
'''
Args:
input (Tensor): (effective_batch_size,input_size)
hidden (Tensor,None): (effective_batch_size,hidden_size)
Return:
Tensor: (effective_batch_size,hidden_size)
'''
# TODO: INSTRUCTIONS
# Perform matrix operations to construct the intermediary value from input and hidden tensors
# Apply the activation on the resultant
# Remeber to handle the case when hidden = None. Construct a tensor of appropriate size, filled with 0s to use as the hidden.
effective_batch_size, input_size = input.shape
if hidden is None:
requires_grad = True
hidden = Tensor(np.zeros((effective_batch_size, self.hidden_size)),
requires_grad=requires_grad,
is_leaf=not requires_grad)
#raise NotImplementedError('Implement Forward')
res = input.matmul(self.weight_ih) + self.bias_ih + hidden.matmul(
self.weight_hh) + self.bias_hh
return self.act(res)
