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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from sklearn.linear_model import LogisticRegression
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import numpy as np
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class Classifier(nn.Module):
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def __init__(self,
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combined_input,
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combined_dim,
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num_classes,
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n_layers,
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skip_in=(4,),
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weight_norm=True):
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super(Classifier, self).__init__()
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self.num_layers = n_layers
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self.skip_in = skip_in
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self.model = LogisticRegression()
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dims = [combined_input] + [combined_dim for _ in range(n_layers)] + [num_classes]
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for l in range(0, self.num_layers + 1):
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if l+1 in self.skip_in:
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out_dim = dims[l + 1] + dims[0]
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dims[l + 1] = out_dim
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else:
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out_dim = dims[l + 1]
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lin = nn.Linear(dims[l], out_dim)
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if weight_norm:
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lin = nn.utils.weight_norm(lin)
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setattr(self, "lin" + str(l), lin)
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self.activation = nn.ReLU
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def forward(self, inputs):
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x = inputs
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for l in range(0, self.num_layers + 1):
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lin = getattr(self, "lin" + str(l))
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if l+1 in self.skip_in:
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x = torch.cat([x, inputs], 1) / np.sqrt(2)
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x = lin(x)
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if l < self.num_layers:
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x = self.activation()(x)
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return x |
