def __init__(self, dim_x, dim_y, embed_size=16, hidden_layer_size=96):
super(RRN, self).__init__()
self.max_digit = dim_x * dim_y
self.embed_size = embed_size
self.hidden_layer_size = hidden_layer_size
self.edges = sudoku_model_utils.determine_edges(dim_x, dim_y)
self.embed_layer = nn.Embedding(self.max_digit + 1, self.embed_size)
self.input_mlp = MLP([self.embed_size,
self.hidden_layer_size,
self.hidden_layer_size,
self.hidden_layer_size])
self.f = MLP([2 * self.hidden_layer_size,
self.hidden_layer_size,
self.hidden_layer_size,
self.hidden_layer_size])
self.g_mlp = MLP([2 * self.hidden_layer_size,
self.hidden_layer_size,
self.hidden_layer_size,
self.hidden_layer_size])
self.g_lstm = nn.LSTM(self.hidden_layer_size, self.hidden_layer_size)
self.r = MLP([self.hidden_layer_size,
self.hidden_layer_size,
self.hidden_layer_size,
self.max_digit])
def __init__(self, dim_x, dim_y, embed_size, hidden_layer_size):
super(RelNet, self).__init__()
self.max_digit = dim_x * dim_y
self.embed_size = embed_size
self.hidden_layer_size = hidden_layer_size
self.edges = sudoku_model_utils.determine_edges(dim_x, dim_y)
self.encoder = nn.Embedding(self.max_digit + 1, self.embed_size)
self.embed_mlp = MLP(
[self.embed_size + self.max_digit, self.hidden_layer_size])
self.rel_layer = RelationalLayer(
[self.hidden_layer_size, self.hidden_layer_size], self.edges)
self.decode_mlp = MLP([self.hidden_layer_size, self.self.embed_size])
self.decoder = MLP([self.embed_size, self.max_digit])