def __init__(self, input_shape, owner_name=""):
super(SRNetS, self).__init__(input_shape, 1, owner_name)
self.conv = [
nn.Sequential(BaseN.conv3_2(input_shape[0], 8), nn.Softplus(),
BaseN.conv3_2(8, 16))
]
x = BaseN.output_shape(input_shape, self.conv[0])
self.model1 = nn.Sequential(self.conv[0], BaseN.Flatten(),
nn.Linear(np.prod(x), 256),
nn.Linear(256, 64), nn.Tanh())
self.model2 = nn.Sequential(BaseN.conv3_2(input_shape[0], 8),
nn.Softplus(), BaseN.conv3_2(8, 16),
BaseN.Flatten(),
nn.Linear(np.prod(x), 256),
nn.Linear(256, 64), nn.Tanh())
self.model3 = nn.Sequential(nn.Linear(128, 64), nn.Linear(64, 1))
self.compile()
self.loss = nn.MSELoss()
self.optimizer = self.optimizer = torch.optim.RMSprop(filter(
lambda p: p.requires_grad, self.parameters()),
lr=1e-3)
def __init__(self, input_shape, output_shape, owner_name=""):
super(ConvNet, self).__init__(input_shape, output_shape, owner_name)
self.conv = [
nn.Sequential(BaseN.conv3_2(input_shape[0], 8), nn.ReLU(),
BaseN.conv3_2(8, 16), nn.ReLU(), BaseN.conv3_2(8, 8))
]
x = BaseN.output_shape(self.conv[0], input_shape)
self.model = nn.Sequential(
self.conv[0], BaseN.Flatten(), nn.Linear(np.prod(x), 512),
BaseN.AdaptiveTanh(), nn.Linear(512, 256),
BaseN.EigenLayer(256, self.output_shape[0], bias=False))
self.compile()
def __init__(self, input_shape, output_shape, owner_name=""):
super(ConvNetBigAtari, self).__init__(input_shape, output_shape,
owner_name)
self.conv = [
nn.Sequential(BaseN.conv3_2(input_shape[0], 8), nn.Softplus(),
BaseN.conv3_2(8, 16), BaseN.conv3_2(16, 32))
]
x = BaseN.output_shape(self.conv[0], input_shape)
self.model = nn.Sequential(
self.conv[0], BaseN.Flatten(), nn.Linear(np.prod(x), 512),
nn.Linear(512, 512), nn.Tanh(), nn.Linear(512, 1024),
BaseN.EigenLayer(1024, self.output_shape[0]))
self.compile()
def __init__(self,input_shape,output_shape,**kwargs):
super(QFunction,self).__init__(input_shape,output_shape,**kwargs)
self.conv = [nn.Sequential(nn.Conv2d(self.input_shape[0], 8, kernel_size=6, stride=3, padding=2), nn.ReLU(),
B.conv3_2(8, 16),nn.ReLU(),
B.conv3_2(16, 32))]
x = B.output_shape(self.conv[0],self.input_shape)
self.model = nn.Sequential(self.conv[0],
B.Flatten(),nn.Tanh(),
nn.Linear(np.prod(x), 512),
nn.Linear(512,self.output_shape))
self.compile()
self.loss = torch.nn.SmoothL1Loss()
self.optimizer = optim.RMSprop(self.parameters(), lr=0.00025,alpha=0.95,eps=0.01,momentum=0.95)
def __init__(self, input_shape, output_shape, **kwargs):
super(ConvNetMNIST, self).__init__(**kwargs)
self.n = output_shape
self.conv = [BaseN.ResNetBlock(1, 32), BaseN.conv3_2(32, 64)]
x = BaseN.output_shape(self.conv[0], input_shape)
self.model = nn.Sequential(self.conv[0], nn.Softplus(),
BaseN.Flatten(), nn.Linear(np.prod(x), 512),
nn.Linear(512, 256), nn.Tanh(),
BaseN.EigenLayer(256, self.output_shape[0]))
self.compile()
def __init__(self, input_shape, output_shape, owner_name=""):
super(ConvNetSimple, self).__init__(input_shape, output_shape,
owner_name)
self.conv = [
nn.Sequential(BaseN.conv3_2(input_shape[0], 4), nn.Softplus())
]
x = BaseN.output_shape(self.conv[0], input_shape)
self.model = nn.Sequential(self.conv[0], BaseN.Flatten(),
nn.Linear(np.prod(x), 512),
nn.Linear(512, 256), nn.Tanh(),
nn.Linear(256, self.output_shape[0]))
self.compile()