def outputs(self,inputs): if self.activation == 'sigmoid': outputs = T.nnet.sigmoid(T.dot(inputs,self.w)+self.b) elif self.activation == 'softmax': outputs = T.nnet.softmax(T.dot(inputs,self.w)+self.b) elif self.activation == 'liner': outputs = T.maximun((T.dot(inputs,self.w)+self.b),0) return outputs
def step(u_t,h_tm1):
if self.activation == 'sigmoid':
h_t = T.nnet.sigmoid(T.dot(u_t,self.w_ih)+T.dot(h_tm1,self.w_hh)+self.b_h)
y_t = T.dot(h_t,self.w_ho) + self.b_o
elif self.activation == 'tanh':
h_t = T.tanh(T.dot(u_t,self.w_ih)+T.dot(h_tm1,self.w_hh)+self.b_h)
y_t = T.dot(h_t,self.w_ho) + self.b_o
elif self.activation == 'relu':
h_t = T.maximun((T.dot(u_t,self.w_ih)+T.dot(h_tm1,self.w_hh)+self.b_h),0)
y_t = T.dot(h_t,self.w_ho) + self.b_o
else:
raise NotImplementedError
return h_t,y_t