How to use the torchx.layers 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_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))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))
