LSTM Custom

def InitLSTM(self,LSTM,Name,InputSize)

      LSTM.Forget_Wights_X=tf.Variable(tf.truncated_normal(

                                                  shape=  [InputSize,LSTM.Size],stddev=0.1))

      LSTM.Forget_Wights_Z=tf.Variable(tf.truncated_normal(

                                                  shape=[LSTM.Size,LSTM.Size],stddev=0.1),)

      LSTM.Forget_Biases=tf.Variable(tf.zeros(shape=[LSTM.Size]))

      LSTM.Output_Wights_X=tf.Variable(tf.truncated_normal(

                                                 shape=[InputSize,LSTM.Size],stddev=0.1),)

     LSTM.Output_Wights_Z=tf.Variable(tf.truncated_normal(

                                                 shape=[LSTM.Size,LSTM.Size]),)

     LSTM.Output_Biases=tf.zeros(shape=[LSTM.Size],)

     LSTM.Generate_Wights_Xi=tf.Variable(tf.truncated_normal(

                                      shape=[InputSize,LSTM.Size],stddev=0.1),)

     LSTM.Generate_Wights_XC=tf.Variable(tf.truncated_normal(

                                      shape=[InputSize,LSTM.Size],stddev=0.1),)

     LSTM.Generate_Wights_Zi=tf.Variable(tf.truncated_normal(

                                     shape=[LSTM.Size.Size],stddev=0.1))

     LSTM.Generate_Wights_ZC=tf.Variable(tf.truncated_normal(

         shape=[LSTM.Size,LSTM.Size],stddev=0.1))

          LSTM.Generate_Biases=tf.zeros(shape=[LSTM.Size])

          LSTM.HState=tf.zeros(shape=[LSTM.Size],name="HState"+Name)

         LSTM.CState=tf.zeros(shape=[LSTM.Size],name="CState"+Name)

def      CalculateLSTM(self,LSTM,Input):

          Out=None

           for i in    range(LSTM.Step):

           Forget=tf.matmul(Input,LSTM.Forget_Wights_X)\

                             +tf.matmul(LSTM.HState,LSTM.Forget_Wights_Z)\

                                   +LSTM.Forget_Biases

          Generate_i=tf.matmul(Input,LSTM.Generate_Wights_Xi)\

                                    +tf.matmul(LSTM.HState,LSTM.Generate_Wights_Zi)\

                                        +LSTM.Generate_Biases_i

           Generate_C=tf.matmul(Input,LSTM.Generate_Wights_XC)\

                                       +tf.matmul(LSTM.HState,LSTM.Generate_Wights_ZC)\

                                            +LSTM.Generate_Biases_C

           Out=tf.matmul(Input,LSTM.Output_Wights_X)\

                                      +tf.matmul(LSTM.HState,LSTM.Output_Wights_Z)\

                                          +LSTM.Output_Biases

           #Updata State

              LSTM.CState=Forget*LSTM.HState+Generate_C*Generate_i

              LSTM.HState=Out*tf.tanh(LSTM.CState)

             returnOut