def forward(self, inputs, **kwargs):
key = 'batch_size'
if key in kwargs:
batch_size = kwargs[key]
else:
raise ValueError('batch_size not specified')
z = self.pre_forward(inputs, batch_size)
# z = super().forward(z)
z = T.tanh(z + self.b.dimshuffle('x', 0, 'x', 'x'))
# z = z.flatten(2)
# reshape to 2d
return z
def core_func(z):
return T.tanh(z)
def core_func(z):
return T.tanh(z)
