matpalm’s gists

matpalm / gist:418151b124d060d9e97e72a51a46ed80

Last active January 26, 2019 09:45

	#!/usr/bin/env python3

	from tensorflow.keras import models
	from tensorflow.keras import layers

	inp = layers.Input(shape=(1,))
	out = layers.ReLU()(inp)

	model = models.Model(inp, out)

matpalm / gist:ff5908992dafeb8986caa1850f1e3ba5

Created November 18, 2018 01:21

	{
	"encoder_enabled": {"x": 1, "y": 1, "z": 1 },
	"encoder_invert": {"x": 0, "y": 0, "z": 0 },
	"encoder_missed_steps_decay": {"x": 5, "y": 5, "z": 5 },
	"encoder_missed_steps_max": {"x": 5, "y": 5, "z": 5 },
	"encoder_scaling": {"x": 5556, "y": 5556, "z": 5556 },
	"encoder_type": {"x": 0, "y": 0, "z": 0 },
	"encoder_use_for_pos": {"x": 0, "y": 0, "z": 0 },
	"movement_axis_nr_steps": {"x": 0, "y": 0, "z": 0 },
	"movement_enable_endpoints": {"x": 0, "y": 0, "z": 0 },

matpalm / gist:23dc5804c6d673b800093d0d15e5de0e

Created April 20, 2018 21:13

	#!/usr/bin/env python3

	from PIL import Image
	import numpy as np
	import tensorflow as tf
	import tensorflow_hub as hub

	# smooth values from point a to point b.
	STEPS = 100
	pt_a = np.random.normal(size=(512))

matpalm / BUGS.PAS

Last active November 2, 2016 03:29

	{ This is an implementation of Scientific American "bugs" I did back in 1996 }

	Program bugs; { fucking evil bugs, no less}
	uses graph,crt;
	const max_num=870;
	sizex=160;
	sizey=160;
	var bnum,fnum,lastx,lasty:integer;
	sx,sy :real;
	b:array[1..max_num,1..7] of integer; {age,life,g1,g2,dir,xco,yxo}

matpalm / gpu_stat.py

Last active February 24, 2022 00:01

	#!/usr/bin/env python
	# gpu_stat.py [DELAY [COUNT]]
	# dump gpu stats as a line of json
	# {"time": 1474168378.146957, "pci_tx": 146000, "pci_rx": 1508000,
	# "gpu_util": 42, "mem_util": 24, "mem_used": 11710,
	# "temp": 76, "fan_speed": 44, "power": 65 }

matpalm / deciles.py

Created August 10, 2016 19:22

	#!/usr/bin/env python
	import sys
	import numpy as np
	print np.percentile(map(float, sys.stdin.readlines()),
	np.linspace(0, 100, 11))

matpalm / gist:503130427d9631ff11bc

Created July 24, 2015 15:33

	#!/usr/bin/env python
	import theano
	import theano.tensor as T
	import numpy as np

	NUM_TOKENS = 5 # number of tokens in sequence being attended to
	D = 3 # generate embedding dim

	np.random.seed(123)
	# params of dummy RNN to gen data

matpalm / theano_word_embeddings.py

Created May 6, 2015 02:12

trivial word embeddings eg

	#!/usr/bin/env python
	# see http://matpalm.com/blog/2015/03/28/theano_word_embeddings/
	import theano
	import theano.tensor as T
	import numpy as np
	import random

	E = np.asarray(np.random.randn(6, 2), dtype='float32')
	t_E = theano.shared(E)
	t_idxs = T.ivector()

matpalm / gist:a81ee7f848c3f7db2ad8

Created March 31, 2015 02:43

numpy.__config__.show()

	>>> numpy.__config__.show()
	atlas_threads_info:
	libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
	library_dirs = ['/usr/lib/atlas-base/atlas', '/usr/lib/atlas-base']
	define_macros = [('ATLAS_INFO', '"\\"3.8.4\\""')]
	language = f77
	include_dirs = ['/usr/include/atlas']
	blas_opt_info:
	libraries = ['ptf77blas', 'ptcblas', 'atlas']
	library_dirs = ['/usr/lib/atlas-base']

matpalm / gist:fcf734a01964c4e08cd1

Created March 3, 2015 04:08

	mat@mat-desktop:~/dev/GTX970test$ make run
	./test_bandwidth1.out
	The bandwidth should stay be about the same each time:
	Data size: 0.125000 GB; Bandwidth: 101725.257812 GB/s
	Data size: 0.375000 GB; Bandwidth: 930059.500000 GB/s
	Data size: 0.625000 GB; Bandwidth: 1514050.500000 GB/s
	Data size: 0.875000 GB; Bandwidth: 2119670.750000 GB/s
	Data size: 1.125000 GB; Bandwidth: 3662109.250000 GB/s
	Data size: 1.375000 GB; Bandwidth: 4475911.500000 GB/s
	Data size: 1.625000 GB; Bandwidth: 3998523.500000 GB/s

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