def __init__(self, input_dim=88, z_dim=100, emission_dim=100,
transition_dim=200, rnn_dim=600, num_layers=1, rnn_dropout_rate=0.0,
num_iafs=0, iaf_dim=50, use_cuda=False):
super(DMM, self).__init__()
# instantiate PyTorch modules used in the model and guide below
self.emitter = Emitter(input_dim, z_dim, emission_dim)
self.trans = GatedTransition(z_dim, transition_dim)
self.combiner = Combiner(z_dim, rnn_dim)
# dropout just takes effect on inner layers of rnn
rnn_dropout_rate = 0. if num_layers == 1 else rnn_dropout_rate
self.rnn = nn.RNN(input_size=input_dim, hidden_size=rnn_dim, nonlinearity='relu',
batch_first=True, bidirectional=False, num_layers=num_layers,
dropout=rnn_dropout_rate)
# if we're using normalizing flows, instantiate those too
self.iafs = [InverseAutoregressiveFlow(AutoRegressiveNN(z_dim, [iaf_dim])) for _ in range(num_iafs)]
self.iafs_modules = nn.ModuleList(self.iafs)
# define a (trainable) parameters z_0 and z_q_0 that help define the probability
# distributions p(z_1) and q(z_1)
# (since for t = 1 there are no previous latents to condition on)
self.z_0 = nn.Parameter(torch.zeros(z_dim))
self.z_q_0 = nn.Parameter(torch.zeros(z_dim))
# define a (trainable) parameter for the initial hidden state of the rnn
self.h_0 = nn.Parameter(torch.zeros(1, 1, rnn_dim))
self.use_cuda = use_cuda
# if on gpu cuda-ize all PyTorch (sub)modules
if use_cuda:
self.cuda()
def __init__(self,
input_dim=88,
z_dim=100,
emission_dim=100,
transition_dim=200,
rnn_dim=600,
num_layers=1,
rnn_dropout_rate=0.0,
num_iafs=0,
iaf_dim=50,
use_cuda=False):
super(DMM, self).__init__()
# instantiate PyTorch modules used in the model and guide below
# if we're using normalizing flows, instantiate those too
self.iafs = [
InverseAutoregressiveFlow(AutoRegressiveNN(z_dim, [iaf_dim]))
for _ in range(num_iafs)
]
self.iafs_modules = nn.ModuleList(self.iafs)
# define a (trainable) parameters z_0 and z_q_0 that help define the probability
# distributions p(z_1) and q(z_1)
# (since for t = 1 there are no previous latents to condition on)
# define a (trainable) parameter for the initial hidden state of the rnn
self.h_0 = nn.Parameter(torch.zeros(1, 1, rnn_dim))
self.use_cuda = use_cuda
# if on gpu cuda-ize all PyTorch (sub)modules
if use_cuda:
self.cuda()