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https://github.com/vale981/ray
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56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
from ray.rllib.utils.framework import try_import_torch
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from ray.rllib.utils.framework import TensorType
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torch, nn = try_import_torch()
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class GRUGate(nn.Module):
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"""Implements a gated recurrent unit for use in AttentionNet"""
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def __init__(self, dim: int, init_bias: int = 0., **kwargs):
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"""
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input_shape (torch.Tensor): dimension of the input
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init_bias (int): Bias added to every input to stabilize training
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"""
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super().__init__(**kwargs)
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# Xavier initialization of torch tensors
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self._w_r = nn.Parameter(torch.zeros(dim, dim))
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self._w_z = nn.Parameter(torch.zeros(dim, dim))
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self._w_h = nn.Parameter(torch.zeros(dim, dim))
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nn.init.xavier_uniform_(self._w_r)
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nn.init.xavier_uniform_(self._w_z)
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nn.init.xavier_uniform_(self._w_h)
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self.register_parameter("_w_r", self._w_r)
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self.register_parameter("_w_z", self._w_z)
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self.register_parameter("_w_h", self._w_h)
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self._u_r = nn.Parameter(torch.zeros(dim, dim))
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self._u_z = nn.Parameter(torch.zeros(dim, dim))
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self._u_h = nn.Parameter(torch.zeros(dim, dim))
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nn.init.xavier_uniform_(self._u_r)
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nn.init.xavier_uniform_(self._u_z)
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nn.init.xavier_uniform_(self._u_h)
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self.register_parameter("_u_r", self._u_r)
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self.register_parameter("_u_z", self._u_z)
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self.register_parameter("_u_h", self._u_h)
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self._bias_z = nn.Parameter(torch.zeros(dim, ).fill_(init_bias))
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self.register_parameter("_bias_z", self._bias_z)
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def forward(self, inputs: TensorType, **kwargs) -> TensorType:
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# Pass in internal state first.
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h, X = inputs
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r = torch.tensordot(X, self._w_r, dims=1) + \
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torch.tensordot(h, self._u_r, dims=1)
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r = torch.sigmoid(r)
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z = torch.tensordot(X, self._w_z, dims=1) + \
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torch.tensordot(h, self._u_z, dims=1) - self._bias_z
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z = torch.sigmoid(z)
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h_next = torch.tensordot(X, self._w_h, dims=1) + \
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torch.tensordot((h * r), self._u_h, dims=1)
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h_next = torch.tanh(h_next)
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return (1 - z) * h + z * h_next
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