How to use the torchx.layers.Functional 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.
Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
SurrealAI / surreal / surreal / model / model_builders / builders.py View on Github 
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_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]))Constructor for PPO critic network
Args:
D_obs: observation space dimension, scalar
hidden_sizes: list of fully connected dimension
'''
super(PPO_CriticNetwork, self).__init__()
# assumes D_obs here is the correct RNN hidden dim if necessary
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(1)(xp)
self.model = L.Functional(inputs=xp_input, outputs=xp)
self.model.build((None, D_obs))
