num_state = env.state_dim
num_action = 128
torch.manual_seed(seed)
random.seed(seed)
Transition = namedtuple('Transition',
['state', 'action', 'reward', 'next_state'])
relation_dim = get_relation_num()
INPUT_SIZE_liner = relation_dim + 128
INPUT_SIZE_GRU = 3 * 128
OUT_SIZE_liner = 128
HIDDEN_SIZE_GRU = relation_dim
node_emb_matrix = get_node_emb_matrix()
edge_emb_dic = get_relation_emb_dic()
def _Max(matrix):
"""
取矩阵每一列最大值得到一行向量
:param matrix:
:return:
"""
return matrix[torch.argmax(matrix, dim=0),
torch.LongTensor(range(matrix.shape[1]))].view(
1, matrix.shape[1])
def __init__(self):
self.FA = nn.Linear(INPUT_SIZE_liner, OUT_SIZE_liner)
self.GRUCell = nn.GRUCell(INPUT_SIZE_GRU, HIDDEN_SIZE_GRU)
self.maxPool = nn.MaxPool1d(1, 1)
self.node_emb_matrix = get_node_emb_matrix()
self.edge_emb_dic = get_relation_emb_dic()