Sequential.py

from mxnet.gluon import nn,rnn
from mxnet import nd
from CnnCode import net
import copy
import mxnet as mx
from mxnet import gluon, init, nd, autograd
import random
from RnnCode import RnnModel,try_gpu
class Sequential(nn.Block):
    def __init__(self,EMBEDDING_DIM,INPUT_DIM,LATENT_DIM,**kwargs):
        super(Sequential, self).__init__(**kwargs)
        self.convolution=net
        self.dequence=RnnModel(EMBEDDING_DIM,INPUT_DIM,LATENT_DIM)
        self.begin_state=self.dequence.begin_state
    def forward(self, chessboard,maxlen_output,maxlen_input,decoder_inputs,weight,s,c):
        chessboard=chessboard.reshape(1,1,chessboard.shape[0],chessboard.shape[1])
        encoder_sequence=net(chessboard)
        encoder_output=encoder_sequence.reshape(19,19)
        output=self.dequence(maxlen_output,maxlen_input,decoder_inputs,weight,encoder_output,s,c)

        return output
# if __name__ == '__main__':
#     initlist=[0,0,0,0,135,255,0,0,0,135,0,255,135,0,0,0,0,0,0]
#     def shuf(seq):
#         random.seed(10)
#         s=copy.deepcopy(seq)
#         random.shuffle(s)
#         return s
#     chess_board=nd.array(list(map(lambda index:shuf(initlist),range(19))))
#     print(chess_board.shape)
#     seq=Sequential(EMBEDDING_DIM=19,INPUT_DIM=19*19,LATENT_DIM=19)
#     decoder_inputs = nd.array([[int(i) for i in range(j * 19, j * 19 + 19)] for j in range(19 * 19)], dtype="float32")
#     weight = nd.array([[0, 3, 4, 3, 3, 2, 3, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13],
#                        [1, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 55, 66, 47, 28, 120, 121, 112, 213]])
#     ctx=try_gpu()
#     s, c=seq.begin_state(batch_size=19, ctx=ctx)
#     seq.initialize(force_reinit=True, ctx=ctx, init=init.Xavier())
#     seq(chess_board,9*19,200,decoder_inputs,weight,s,c)