def __init__(self, hparams, data_path=None):
super(Model, self).__init__()
self.hparams = hparams
self.data_path = data_path
self.T_pred = self.hparams.T_pred
self.loss_fn = torch.nn.MSELoss(reduction='none')
self.recog_net_1 = MLP_Encoder(64 * 64, 300, 3, nonlinearity='elu')
self.recog_net_2 = MLP_Encoder(64 * 64, 300, 3, nonlinearity='elu')
self.obs_net_1 = MLP_Decoder(1, 100, 64 * 64, nonlinearity='elu')
self.obs_net_2 = MLP_Decoder(1, 100, 64 * 64, nonlinearity='elu')
V_net = MLP(4, 100, 1)
M_net = PSD(4, 300, 2)
g_net = MatrixNet(4, 100, 4, shape=(2, 2))
self.ode = Lag_Net(q_dim=2,
u_dim=2,
g_net=g_net,
M_net=M_net,
V_net=V_net)
self.link1_para = torch.nn.Parameter(
torch.tensor(0.0, dtype=self.dtype))
self.train_dataset = None
self.non_ctrl_ind = 1
def __init__(self, hparams, data_path=None):
super(Model, self).__init__()
self.hparams = hparams
self.data_path = data_path
self.T_pred = self.hparams.T_pred
self.loss_fn = torch.nn.MSELoss(reduction='none')
self.recog_q_net = MLP_Encoder(32*32, 300, 3, nonlinearity='elu')
self.obs_net = MLP_Encoder(1, 100, 32*32, nonlinearity='elu')
g_net = MLP(2, 50, 1)
g_baseline_net = MLP(3, 100, 1)
self.ode = Lag_Net(q_dim=1, u_dim=1, g_net=g_net,
g_baseline=g_baseline_net,
dyna_model='g_baseline')
self.train_dataset = None
self.non_ctrl_ind = 1
def __init__(self, hparams, data_path=None):
super(Model, self).__init__()
self.hparams = hparams
self.data_path = data_path
self.T_pred = self.hparams.T_pred
self.loss_fn = torch.nn.MSELoss(reduction='none')
self.recog_net_1 = MLP_Encoder(64*64, 300, 2, nonlinearity='elu')
self.recog_net_2 = MLP_Encoder(64*64, 300, 3, nonlinearity='elu')
self.obs_net = MLP_Decoder(3, 200, 3*64*64, nonlinearity='elu')
V_net = MLP(3, 100, 1) ; M_net = PSD(3, 300, 2)
g_net = MatrixNet(3, 100, 4, shape=(2,2))
self.ode = Lag_Net_R1_T1(g_net=g_net, M_net=M_net, V_net=V_net)
self.train_dataset = None
self.non_ctrl_ind = 1
def __init__(self, hparams, data_path=None):
super(Model, self).__init__()
self.hparams = hparams
self.data_path = data_path
self.T_pred = self.hparams.T_pred
self.loss_fn = torch.nn.MSELoss(reduction='none')
self.recog_net_1 = MLP_Encoder(64 * 64, 300, 2, nonlinearity='elu')
self.recog_net_2 = MLP_Encoder(64 * 64, 300, 3, nonlinearity='elu')
self.obs_net_1 = MLP_Decoder(1, 100, 64 * 64, nonlinearity='elu')
self.obs_net_2 = MLP_Decoder(1, 100, 64 * 64, nonlinearity='elu')
g_net = MatrixNet(3, 100, 4, shape=(2, 2))
g_baseline_net = MLP(5, 400, 2)
self.ode = Lag_Net_R1_T1(g_net=g_net,
g_baseline=g_baseline_net,
dyna_model='g_baseline')
self.train_dataset = None
self.non_ctrl_ind = 1