How to use the torchx.layers.ReLU function in torchx
To help you get started, we’ve selected a few torchx examples, based on popular ways it is used in public projects.
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SurrealAI / surreal / surreal / model / model_builders / builders.py View on Github 
def __init__(self, D_in, D_act, hidden_sizes=[300, 200], use_layernorm=True):
super(ActorNetworkX, self).__init__()
xp_input = L.Placeholder((None, D_in))
xp = L.Linear(hidden_sizes[0])(xp_input)
xp = L.ReLU()(xp)
if use_layernorm:
# Normalize 1 dimension
xp = L.LayerNorm(1)(xp)
xp = L.Linear(hidden_sizes[1])(xp)
xp = L.ReLU()(xp)
if use_layernorm:
xp = L.LayerNorm(1)(xp)
xp = L.Linear(D_act)(xp)
xp = L.Tanh()(xp)
self.model = L.Functional(inputs=xp_input, outputs=xp)
self.model.build((None, D_in))def __init__(self, D_obs, D_act, hidden_sizes=[64, 64], init_log_sig=0):
'''
Constructor for PPO actor network
Args:
D_obs: observation space dimension, scalar
D_act: action space dimension, scalar
hidden_sizes: list of fully connected dimension
init_log_sig: initial value for log standard deviation parameter
'''
super(PPO_ActorNetwork, self).__init__()
# assumes D_obs here is the correct RNN hidden dim
xp_input = L.Placeholder((None, D_obs))
xp = L.Linear(hidden_sizes[0])(xp_input)
xp = L.ReLU()(xp)
xp = L.Linear(hidden_sizes[1])(xp)
xp = L.ReLU()(xp)
xp = L.Linear(D_act)(xp)
xp = L.Tanh()(xp)
self.model = L.Functional(inputs=xp_input, outputs=xp)
self.model.build((None, D_obs))
self.log_var = nn.Parameter(torch.zeros(1, D_act) + init_log_sig)def __init__(self, D_in, D_act, hidden_sizes=[400, 300], use_layernorm=True):
super(CriticNetworkX, self).__init__()
xp_input_obs = L.Placeholder((None, D_in))
xp = L.Linear(hidden_sizes[0])(xp_input_obs)
xp = L.ReLU()(xp)
if use_layernorm:
xp = L.LayerNorm(1)(xp)
self.model_obs = L.Functional(inputs=xp_input_obs, outputs=xp)
self.model_obs.build((None, D_in))
xp_input_concat = L.Placeholder((None, hidden_sizes[0] + D_act))
xp = L.Linear(hidden_sizes[1])(xp_input_concat)
xp = L.ReLU()(xp)
if use_layernorm:
xp = L.LayerNorm(1)(xp)
xp = L.Linear(1)(xp)
self.model_concat = L.Functional(inputs=xp_input_concat, outputs=xp)
self.model_concat.build((None, D_act + hidden_sizes[0]))def __init__(self, D_obs, D_act, hidden_sizes=[64, 64], init_log_sig=0):
'''
Constructor for PPO actor network
Args:
D_obs: observation space dimension, scalar
D_act: action space dimension, scalar
hidden_sizes: list of fully connected dimension
init_log_sig: initial value for log standard deviation parameter
'''
super(PPO_ActorNetwork, self).__init__()
# assumes D_obs here is the correct RNN hidden dim
xp_input = L.Placeholder((None, D_obs))
xp = L.Linear(hidden_sizes[0])(xp_input)
xp = L.ReLU()(xp)
xp = L.Linear(hidden_sizes[1])(xp)
xp = L.ReLU()(xp)
xp = L.Linear(D_act)(xp)
xp = L.Tanh()(xp)
self.model = L.Functional(inputs=xp_input, outputs=xp)
self.model.build((None, D_obs))
self.log_var = nn.Parameter(torch.zeros(1, D_act) + init_log_sig)def __init__(self, D_obs, D_out, conv_channels=[16, 32], kernel_sizes=[8, 4], strides=[4,2]):
super(CNNStemNetwork, self).__init__()
layers = []
for i in range(len(conv_channels)):
layers.append(L.Conv2d(conv_channels[i], kernel_size=kernel_sizes[i], stride=strides[i]))
layers.append(L.ReLU())
layers.append(L.Flatten())
layers.append(L.Linear(D_out))
layers.append(L.ReLU())
self.model = L.Sequential(*layers)
# instantiate parameters
self.model.build((None, *D_obs))
