lizhangzhan’s gists

lizhangzhan / weibull.py

Created September 3, 2020 07:33 — forked from plasmaman/weibull.py

Python code for estimating the shape and scale parameters for a two-parameter Weibull distribution. It uses scipy.optimize.fmin to minimize the Likelihood function.

	from scipy.stats import exponweib
	from scipy.optimize import fmin
	import numpy as np

	# x is your data array
	# returns [shape, scale]

	def fitweibull(x):
	def optfun(theta):
	return -np.sum(np.log(exponweib.pdf(x, 1, theta[0], scale = theta[1], loc = 0)))

lizhangzhan / brnn.py

Last active October 13, 2017 16:43 — forked from allenanie/brnn.py

Bayesian Recurrent Neural Network Implementation

	"""
	An implementation of the `Local reparameterization trick`
	from Kingma & Wellings and
	Bayesian RNN
	from Fortunato, Blundell & Vinyals
	"""
	import os
	import time
	import copy
	from os.path import join as pjoin

lizhangzhan / cifar10_wide_resnet.py

Created July 20, 2017 14:50 — forked from kashif/cifar10_wide_resnet.py

Keras Wide Residual Networks CIFAR-10

	from __future__ import print_function

	from keras.datasets import cifar10
	from keras.layers import merge, Input
	from keras.layers.convolutional import Convolution2D, ZeroPadding2D, AveragePooling2D
	from keras.layers.core import Dense, Activation, Flatten, Dropout
	from keras.layers.normalization import BatchNormalization
	from keras.models import Model
	from keras.preprocessing.image import ImageDataGenerator
	from keras.utils import np_utils

lizhangzhan / DCGAN.ipynb

Created October 3, 2016 06:26 — forked from awjuliani/DCGAN.ipynb

An implementation of DCGAN in Tensorflow and Python.

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lizhangzhan / conv_deconv_vae.py

Created August 23, 2016 07:49 — forked from kastnerkyle/conv_deconv_vae.py

Convolutional Variational Autoencoder, modified from Alec Radford at (https://gist.github.com/Newmu/a56d5446416f5ad2bbac)

	# Alec Radford, Indico, Kyle Kastner
	# License: MIT
	"""
	Convolutional VAE in a single file.
	Bringing in code from IndicoDataSolutions and Alec Radford (NewMu)
	Additionally converted to use default conv2d interface instead of explicit cuDNN
	"""
	import theano
	import theano.tensor as T
	from theano.compat.python2x import OrderedDict

lizhangzhan / vae_mlp.py

Created August 22, 2016 12:03 — forked from tabacof/vae_mlp.py

Variational Auto-Encoder (MLP encoder/decoder)

	# -- coding: utf-8 --
	"""
	Reproducing the results of Auto-Encoding Variational Bayes by Kingma and Welling
	With a little help from the code from van Amersfoort and Otto Fabius (https://github.com/y0ast)

	@author: Pedro Tabacof (tabacof at gmail dot com)
	"""

	import random
	import numpy as np

lizhangzhan / VariationalAutoencoder.py

Created August 22, 2016 07:04 — forked from zomux/VariationalAutoencoder.py

	"""
	Joost van Amersfoort - <joost.van.amersfoort@gmail.com>
	Otto Fabius - <ottofabius@gmail.com

	License: MIT
	"""

	import numpy as np
	import theano as th
	import theano.tensor as T

lizhangzhan / min-char-rnn.py

Created August 12, 2016 15:28 — forked from karpathy/min-char-rnn.py

Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

lizhangzhan / bayesian_neural_network.ipynb

Created March 2, 2016 03:01 — forked from twiecki/bayesian_neural_network.ipynb

Bayesian Neural Network in PyMC3

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lizhangzhan / poisson_factorization_machine.py

Created December 18, 2015 08:49 — forked from vene/poisson_factorization_machine.py

	""" Poisson-loss Factorization Machines with Numba

	Follows the vanilla FM model from:
	Steffen Rendle (2012): Factorization Machines with libFM.
	In: ACM Trans. Intell. Syst. Technol., 3(3), May.
	http://doi.acm.org/10.1145/2168752.2168771

	See also: https://github.com/coreylynch/pyFM
	"""

lizhang lizhangzhan