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/* Binarized neural network inference example. | |
This shows a simple C++ program for doing inference on | |
binarized neural networks. To do this efficiently, the code | |
below makes use of the "bitset" class, which uses the "popcnt" | |
instruction to count the number of 1's that show up in the | |
matrix product, in constant time. This means that a matrix | |
multiplication between a (A, B) and (B, C) matrix takes | |
O(A * C) time; in other words, each value in the output matrix | |
is computed in constant time. | |
*/ |
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; Finds all factors of a number in O(sqrt n) time. | |
(define (factors n) | |
(define (@factors n i a) | |
(cond ((= (modulo n i) 0) (@factors (quotient n i) i (cons i a))) | |
((>= (* i i) n) (if (= 1 n) a (cons n a))) | |
(else (@factors n (+ i 1) a)))) | |
(@factors n 2 `())) | |
; Multiples all the elements in a list. | |
(define (mult l) |
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#!/usr/bin/env python | |
"""The training script for the DANN model.""" | |
from __future__ import division | |
from __future__ import print_function | |
import csv | |
import os | |
import itertools | |
import sys |
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import theano | |
import theano.tensor as T | |
import numpy as np | |
X = theano.shared(value=np.asarray([[1, 0], [0, 0], [0, 1], [1, 1]]), name='X') | |
y = theano.shared(value=np.asarray([[1], [0], [1], [0]]), name='y') | |
rng = np.random.RandomState(1234) | |
LEARNING_RATE = 0.01 |
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#!/usr/bin/env python | |
"""Example of building a model to solve an XOR problem in Keras.""" | |
import keras | |
import numpy as np | |
# XOR data. | |
x = np.array([ | |
[0, 1], | |
[1, 0], |
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"""Implementation of maximum noise entropy using TensorFlow. | |
Paper: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002249 | |
""" | |
# For Python 3 compatibility. | |
from __future__ import print_function | |
# For building the algorithm. | |
import tensorflow as tf |
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"""Implementation of sparse filtering using TensorFlow. | |
Original MATLAB code: https://github.com/jngiam/sparseFiltering | |
Paper: https://papers.nips.cc/paper/4334-sparse-filtering.pdf | |
""" | |
# For Python 3 compatibility. | |
from __future__ import print_function | |
# For building the algorithm. |
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execute pathogen#infect() | |
colorscheme badwolf | |
" turns of syntax highlighting | |
syntax enable | |
" use spaces not tabs | |
set tabstop=8 softtabstop=0 expandtab shiftwidth=2 smarttab | |
" show line numbers |
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from __future__ import print_function | |
import numpy as np | |
from keras.engine import Input, Model | |
from keras.layers import Dense | |
X = np.asarray([[0, 1], [1, 0], [0, 0], [1, 1]]) | |
y = np.asarray([[0], [0], [1], [1]]) | |
input = Input(shape=(2,)) |
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from keras.engine import InputSpec | |
from keras.layers import Dense | |
from keras.layers.wrappers import Wrapper, TimeDistributed | |
class Freeway(Wrapper): | |
def __init__(self, layer, gate=None, **kwargs): | |
self.supports_masking = True | |
self.gate = gate | |
super(Freeway, self).__init__(layer, **kwargs) |