def __init__(self):
super().__init__()
# self.fc1 = nn.Sequential(
# gn.Spread(2),
# torch.nn.Softmax(),
# gn.Spread(4),
# torch.nn.Softmax(),
# gn.Spread(6),
# torch.nn.Softmax(),
# gn.Spread(8),
# torch.nn.Softmax(),
# gn.Slide(10),
# torch.nn.Softmax(),
# gn.Slide(10),
# torch.nn.Softmax(),
# gn.Gather(10),
# torch.nn.Softmax(),
# gn.Gather(8),
# torch.nn.Softmax(),
# gn.Gather(6),
# torch.nn.Softmax(),
# gn.Gather(4),
# torch.nn.Softmax(),
# gn.Gather(2),
# )
self.fc1 = nn.Sequential(
gn.Spread(2, bias=False),
torch.nn.Softmax(dim=1),
gn.Slide(4, bias=False),
torch.nn.Softmax(dim=1),
gn.Spread(4, bias=False),
torch.nn.Softmax(dim=1),
gn.Slide(6, bias=False),
torch.nn.Softmax(dim=1),
gn.Spread(6, bias=False),
torch.nn.Softmax(dim=1),
gn.Slide(8, bias=False),
torch.nn.Softmax(dim=1),
gn.Spread(8, bias=False),
torch.nn.Softmax(dim=1),
gn.Slide(10, bias=False),
torch.nn.Softmax(dim=1),
gn.Gather(10, bias=False),
torch.nn.Softmax(dim=1),
gn.Gather(8, bias=False),
torch.nn.Softmax(dim=1),
gn.Gather(6, bias=False),
torch.nn.Softmax(dim=1),
gn.Gather(4, bias=False),
torch.nn.Softmax(dim=1),
gn.Gather(2, bias=False),
)
def __init__(self, z_features=20, activation_function='Softmax', sigma=1):
# --------------------------------------------------------------------
# Init
super().__init__()
if z_features < 12:
raise ValueError("z_features must be >= 12.")
self.z_features = z_features
self.sigma = sigma
# Lookup activation function (a class)
AF = getattr(nn, activation_function)
# --------------------------------------------------------------------
# Def fc1:
glia1 = []
for s in reversed(range(12, self.z_features + 2, 2)):
glia1.append(gn.Gather(s))
glia1.append(gn.Leak(s - 2, sigma=sigma))
glia1.append(gn.Slide(s - 2))
glia1.append(gn.Leak(s - 2, sigma=sigma))
# Linear on the last output, for digit decode
if s > 12:
glia1.append(AF(dim=1))
self.glia1 = nn.Sequential(*glia1)
def __init__(self, in_features, num_actions):
super().__init__()
# -------------------------------------------------------------------
# Vision
self.conv = nn.Sequential(
nn.Conv2d(in_features[0], 32, kernel_size=8, stride=4), nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=4, stride=2), nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1), nn.ReLU())
conv_features = self.conv_layer_size(in_features)
# -------------------------------------------------------------------
# Decision
# TODO. shrink d before AGN layer. Neural decode should not be
# there.
# Build glia decision layer
glia1 = []
for s in reversed(range(512 + 2, conv_features, 2)):
glia1.append(gn.Gather(s, bias=False))
glia1.append(torch.nn.ELU())
self.fc1 = nn.Sequential(*glia1)
# Linear neurons (decoder?)
self.fc2 = nn.Linear(512, num_actions)