| """ | |
| poisson_reconstruct.py | |
| Fast Poisson Reconstruction in Python | |
| Copyright (c) 2014 Jack Doerner | |
| Permission is hereby granted, free of charge, to any person obtaining a copy | |
| of this software and associated documentation files (the "Software"), to deal | |
| in the Software without restriction, including without limitation the rights | |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
| from __future__ import absolute_import | |
| from __future__ import print_function | |
| from keras.datasets import mnist | |
| from keras.models import Sequential | |
| from keras.layers.core import Dense, Dropout, Activation, Flatten | |
| from keras.layers.convolutional import Convolution2D, MaxPooling2D | |
| from keras.utils import np_utils | |
| from scipy.special import expit | |
| import numpy as np | |
| np.set_printoptions(suppress=True) |
| ## install Catalyst proprietary | |
| sudo ntfsfix /dev/sda2 | |
| sudo cp /etc/X11/xorg.conf /etc/X11/xorg.conf.BAK | |
| sudo apt-get remove --purge fglrx* | |
| sudo apt-get install linux-headers-generic | |
| sudo apt-get install fglrx xvba-va-driver libva-glx1 libva-egl1 vainfo | |
| sudo amdconfig --initial | |
| ## install build essentials | |
| sudo apt-get install cmake |
| # Example from http://jakevdp.github.io/blog/2013/06/01/ipython-notebook-javascript-python-communication/ adapted for IPython 2.0 | |
| # Add an input form similar to what we saw above | |
| from IPython.display import HTML | |
| from math import pi, sin | |
| input_form = """ | |
| <div style="background-color:gainsboro; border:solid black; width:600px; padding:20px;"> | |
| Code: <input type="text" id="code_input" size="50" height="2" value="sin(pi / 2)"><br> | |
| Result: <input type="text" id="result_output" size="50" value="1.0"><br> |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from nilearn.plotting.img_plotting import _load_anat | |
| fname = '/home/jrking/nilearn_data/haxby2001/subj1/anat.nii.gz' | |
| class MRI_viewer(): | |
| def __init__(self, fname): | |
| # setup figure | |
| fig, axes = plt.subplots(1, 3) |
The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.
Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.
It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.
| """ | |
| Here is a dice loss for keras which is smoothed to approximate a linear (L1) loss. | |
| It ranges from 1 to 0 (no error), and returns results similar to binary crossentropy | |
| """ | |
| # define custom loss and metric functions | |
| from keras import backend as K | |
| def dice_coef(y_true, y_pred, smooth=1): |
This is a guide for Scala and Java development on Windows, using Windows Subsystem for Linux, although a bunch of it is applicable to a VirtualBox / Vagrant / Docker subsystem environment. This is not complete, but is intended to be as step by step as possible.
Read the entire Decent Security guide, and follow the instructions, especially:
| #ifndef _POLYNOMIAL_REGRESSION_H | |
| #define _POLYNOMIAL_REGRESSION_H __POLYNOMIAL_REGRESSION_H | |
| /** | |
| * PURPOSE: | |
| * | |
| * Polynomial Regression aims to fit a non-linear relationship to a set of | |
| * points. It approximates this by solving a series of linear equations using | |
| * a least-squares approach. | |
| * | |
| * We can model the expected value y as an nth degree polynomial, yielding |