bootstrap

prior-art/Code/S_remove_input_neuron.py

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+# Remove on input neuron from a NN
+
+from __future__ import print_function
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import pandas as pd
+import numpy as np
+import torch
+from torch.utils import data
+import pickle
+from matplotlib import pyplot as plt
+import torch.utils.data as utils
+import time
+import os
+
+is_cuda = torch.cuda.is_available()
+
+def remove_input_neuron(net,n_inp,idx_neuron,ct_median,save_filename):
+    removed_weights = net.linear1.weight[:,idx_neuron]
+    # Remove the weights associated with the removed input neuron                                                                                                               
+    t = torch.transpose(net.linear1.weight,0,1)
+    preserved_ids = torch.LongTensor(np.array(list(set(range(n_inp)) - set([idx_neuron]))))
+    t = nn.Parameter(t[preserved_ids, :])
+    net.linear1.weight = nn.Parameter(torch.transpose(t,0,1))
+    # Adjust the biases
+    if is_cuda:
+        net.linear1.bias = nn.Parameter(net.linear1.bias+torch.tensor(ct_median*removed_weights).float().cuda())
+    else:
+        net.linear1.bias = nn.Parameter(net.linear1.bias+torch.tensor(ct_median*removed_weights).float())
+    torch.save(net.state_dict(), save_filename)
+