Florian FlorianMuellerklein

This code snippet shows how we can change a layer in a pretrained model. In the following code, we change all the ReLU activation functions with SELU in a resnet18 model.

import torch
from torchvision import model

resnet18 = model.resnet18(pretrained=True)

def funct(list_mods):

	import pandas as pd
	import numpy as np

	from sklearn.ensemble import GradientBoostingClassifier

	train_loc = 'train.csv'
	test_loc = 'test.csv'
	TREES = 30
	NODES = 7

	import lasagne
	from lasagne.nonlinearities import rectify, softmax
	from lasagne.layers import InputLayer, DenseLayer, DropoutLayer, batch_norm, BatchNormLayer
	from lasagne.layers import ElemwiseSumLayer, NonlinearityLayer, GlobalPoolLayer
	from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
	from lasagne.init import HeNormal

	def ResNet_FullPre_Wide(input_var=None, n=3, k=2):
	'''
	Adapted from https://github.com/Lasagne/Recipes/tree/master/papers/deep_residual_learning.


	#!/usr/bin/env python

	"""
	Lasagne example of ResNet-110 from 'Identity Mappings in Deep Residual Networks', Kaiming He et al. 2016 (https://arxiv.org/abs/1603.05027)
	"""

	from __future__ import print_function

	import sys

	import multiprocessing as mp

	class threaded_batch_iter(object):
	'''
	Batch iterator to make transformations on the data.
	Uses multiprocessing so that batches can be created on CPU while GPU runs previous batch
	'''
	def __init__(self, batchsize):
	self.batchsize = batchsize

	from keras.layers import merge, Input, Dropout
	from keras.layers import Dense, Activation
	from keras.layers import Convolution2D, MaxPooling2D
	from keras.layers.pooling import GlobalAveragePooling2D
	from keras.layers import BatchNormalization
	from keras.models import Model

	'''
	Adapted from https://arxiv.org/pdf/1611.10080v1.pdf.
	It's a wide and deep residual network designed for optimal feature extraction and gradient flow.

	mytheme.scatter = theme_bw() + theme(legend.position = 'none', panel.border = element_blank(), axis.line = element_blank(), axis.title = element_text(size = rel(1.5), face = 'bold'))

	mytheme.bar = theme_bw() + theme(panel.grid.minor.x=element_blank(), panel.grid.major.x=element_blank(), axis.text.x = element_text(size = rel(1.1), face = 'bold'), axis.title.x = element_blank(), axis.text.y = element_text(size = rel(1.1), face = 'bold'), axis.title.y = element_text(vjust = 1), legend.position = 'none', panel.border = element_blank(), axis.line = element_blank(), axis.title = element_text(size = rel(1.5), face = 'bold'))