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import torch
import torch.nn as nn
import numpy as np
class FreqNetwork(nn.Module):
def __init__(self,
tfidf_input_dim,
tfidf_output_dim,
tfidf_hidden_dim,
n_layers,
skip_in=(4,),
weight_norm=True):
super(FreqNetwork, self).__init__()
dims = [tfidf_input_dim] + [tfidf_hidden_dim for _ in range(n_layers)] + [tfidf_output_dim]
self.num_layers = n_layers
self.skip_in = skip_in
for l in range(0, self.num_layers+1):
if l+1 in self.skip_in:
out_dim = dims[l + 1] + dims[0]
dims[l + 1] = out_dim
else:
out_dim = dims[l + 1]
lin = nn.Linear(dims[l], out_dim)
if weight_norm:
lin = nn.utils.weight_norm(lin)
setattr(self, "lin" + str(l), lin)
self.activation = nn.ReLU
def forward(self, inputs):
x = inputs
for l in range(0, self.num_layers+1):
lin = getattr(self, "lin" + str(l))
if l in self.skip_in:
x = torch.cat([x, inputs], 1) / np.sqrt(2)
x = lin(x)
if l < self.num_layers:
x = self.activation()(x)
# x = torch.dropout(x, p=0.2, train=self.training)
return x |