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https://github.com/wassname/NALU-pytorch.git
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Kevin
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
import math
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import torch.nn as nn
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from .utils import str2act
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class MLP(nn.Module):
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"""A Multi-Layer Perceptron (MLP).
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Also known as a Fully-Connected Network (FCN). This
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implementation assumes that all hidden layers have
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the same hidden size and the same activation function.
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Attributes:
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num_layers: the number of layers in the network.
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in_dim: the size of the input sample.
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hidden_dim: the size of the hidden layers.
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out_dim: the size of the output.
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activation: the activation function.
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"""
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def __init__(self, num_layers, in_dim, hidden_dim, out_dim, activation='relu'):
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super().__init__()
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self.num_layers = num_layers
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self.in_dim = in_dim
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self.hidden_dim = hidden_dim
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self.out_dim = out_dim
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self.activation = str2act(activation)
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nonlin = True
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if self.activation is None:
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nonlin = False
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layers = []
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for i in range(num_layers - 1):
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layers.extend(
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self._layer(
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hidden_dim if i > 0 else in_dim,
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hidden_dim,
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nonlin,
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)
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)
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layers.extend(self._layer(hidden_dim, out_dim, False))
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self.model = nn.Sequential(*layers)
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# init
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for m in self.modules():
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if isinstance(m, nn.Linear):
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nn.init.kaiming_uniform_(m.weight, a=math.sqrt(5))
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fan_in, _ = nn.init._calculate_fan_in_and_fan_out(m.weight)
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bound = 1 / math.sqrt(fan_in)
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nn.init.uniform_(m.bias, -bound, bound)
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def _layer(self, in_dim, out_dim, activation=True):
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if activation:
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return [
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nn.Linear(in_dim, out_dim),
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self.activation,
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]
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else:
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return [
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nn.Linear(in_dim, out_dim),
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]
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def forward(self, x):
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out = self.model(x)
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return out
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