def __init__(self,
num_inputs,
num_hidden,
num_cond_inputs=None,
act='relu',
pre_exp_tanh=False):
super(MADE, self).__init__()
activations = {'relu': nn.ReLU, 'sigmoid': nn.Sigmoid, 'tanh': nn.Tanh}
act_func = activations[act]
input_mask = get_mask(
num_inputs, num_hidden, num_inputs, mask_type='input')
hidden_mask = get_mask(num_hidden, num_hidden, num_inputs)
output_mask = get_mask(
num_hidden, num_inputs * 2, num_inputs, mask_type='output')
self.joiner = nn.MaskedLinear(num_inputs, num_hidden, input_mask,
num_cond_inputs)
self.trunk = nn.Sequential(act_func(),
nn.MaskedLinear(num_hidden, num_hidden,
hidden_mask), act_func(),
nn.MaskedLinear(num_hidden, num_inputs * 2,
output_mask))
def __init__(self, num_inputs, num_hidden, use_tanh=True):
super(MADE, self).__init__()
self.use_tanh = use_tanh
input_mask = get_mask(
num_inputs, num_hidden, num_inputs, mask_type='input')
hidden_mask = get_mask(num_hidden, num_hidden, num_inputs)
output_mask = get_mask(
num_hidden, num_inputs * 2, num_inputs, mask_type='output')
self.main = nn.Sequential(
nn.MaskedLinear(num_inputs, num_hidden, input_mask), nn.ReLU(),
nn.MaskedLinear(num_hidden, num_hidden, hidden_mask), nn.ReLU(),
nn.MaskedLinear(num_hidden, num_inputs * 2, output_mask))
def __init__(self, num_inputs, num_hidden, num_cond_inputs=None, use_tanh=False):
super(MADE, self).__init__()
self.use_tanh = use_tanh
input_mask = get_mask(
num_inputs, num_hidden, num_inputs, mask_type='input')
hidden_mask = get_mask(num_hidden, num_hidden, num_inputs)
output_mask = get_mask(
num_hidden, num_inputs * 2, num_inputs, mask_type='output')
self.joiner = nn.MaskedLinear(num_inputs, num_hidden, input_mask, num_cond_inputs)
self.trunk = nn.Sequential(
nn.ReLU(),
nn.MaskedLinear(num_hidden, num_hidden, hidden_mask),
nn.ReLU(),
nn.MaskedLinear(num_hidden, num_inputs * 2, output_mask))
def _make_param_net(self):
input_mask = get_mask(self.num_inputs,
self.num_hidden,
self.num_inputs,
mask_type='input')
hidden_mask = get_mask(self.num_hidden, self.num_hidden,
self.num_inputs)
output_mask = get_mask(self.num_hidden,
self.num_inputs * 2,
self.num_inputs,
mask_type='output')
return nn.Sequential(
nn.MaskedLinear(self.num_inputs, self.num_hidden, input_mask),
nn.ReLU(),
nn.MaskedLinear(self.num_hidden, self.num_hidden, hidden_mask),
nn.ReLU(),
nn.MaskedLinear(self.num_hidden, self.num_inputs * 2, output_mask))
def __init__(self,
num_inputs,
num_hidden,
num_cond_inputs=None,
s_act='tanh',
t_act='relu',
pre_exp_tanh=False):
super(MADESplit, self).__init__()
self.pre_exp_tanh = pre_exp_tanh
activations = {'relu': nn.ReLU, 'sigmoid': nn.Sigmoid, 'tanh': nn.Tanh}
input_mask = get_mask(num_inputs, num_hidden, num_inputs,
mask_type='input')
hidden_mask = get_mask(num_hidden, num_hidden, num_inputs)
output_mask = get_mask(num_hidden, num_inputs, num_inputs,
mask_type='output')
act_func = activations[s_act]
self.s_joiner = nn.MaskedLinear(num_inputs, num_hidden, input_mask,
num_cond_inputs)
self.s_trunk = nn.Sequential(act_func(),
nn.MaskedLinear(num_hidden, num_hidden,
hidden_mask), act_func(),
nn.MaskedLinear(num_hidden, num_inputs,
output_mask))
act_func = activations[t_act]
self.t_joiner = nn.MaskedLinear(num_inputs, num_hidden, input_mask,
num_cond_inputs)
self.t_trunk = nn.Sequential(act_func(),
nn.MaskedLinear(num_hidden, num_hidden,
hidden_mask), act_func(),
nn.MaskedLinear(num_hidden, num_inputs,
output_mask))
