| import tensorflow as tf | |
| config=tf.ConfigProto( | |
| log_device_placement=True, | |
| ) | |
| sess = tf.Session(config=config) | |
| assert tf.test.is_gpu_available() | |
| assert tf.test.is_built_with_cuda() | |
| op = tf.shape( | |
| tf.nn.conv2d(tf.random_normal([1,10,10,10]), | |
| tf.random_normal([2,10,10,10]), |
| import tensorflow as tf | |
| from tensorflow.python.saved_model import tag_constants | |
| from tensorflow.python.saved_model.signature_def_utils_impl import predict_signature_def | |
| KERAS_HDF5 = 'weights.hdf5' | |
| EXPORT_PATH = '/tmp/tf_model/' # where to save the tensorflow model | |
| model = create_model(params) # build and compile the architecture | |
| model.load_weights(KERAS_HDF5) |
| [alias] | |
| unadd = reset HEAD | |
| st = status | |
| co = checkout | |
| uncommit = reset --soft HEAD^ | |
| changed = diff-tree -no-commit-id --name-only -r | |
| updatesubmodules = pull --recurse-submodules && git submodule update | |
| hist = log --pretty=format:\"%h %ad | %s%d [%an]\" --graph --date=short | |
| [pull] | |
| rebase = true |
| # USAGE: | |
| # ./parallel.sh <command> <input directory> [<threads> [<log file>]] | |
| # | |
| # EXAMPLE: | |
| # ./parallel.sh longrunningcommand.java /myfiles/input/ 10 process.log | |
| shell_command=$1 | |
| input_dir=$2 | |
| threads=$3 | |
| threads=${threads:=10} | |
| output_log=$4 |
| # display the biggest files within a directory | |
| find mypath -type f -printf '%s %p\n'| sort -nr | head -10 | |
| #!/bin/sh | |
| find . -name '*.py' -exec dos2unix {} \; | |
| find . -name '*.py' -exec 2to3 -w {} \; | |
| find . -name '*.py' -exec autopep8 --in-place --aggressive --aggressive {} \; | |
| find . -name '*.py' -exec docformatter --in-place {} \; | |
| isort -rc --atomic . | |
| find . -name '*.py' -exec vim -c '%s/except \(.*\), e:/except \1 as e:/' {} -c 'wq' \; | |
| find . -name '*.py' -exec autoflake --in-place --remove-unused-variables {} \; | |
| find . -name '*.py' -exec vim -c '%s/xrange/range/' {} -c 'wq' \; |
| import sys | |
| import pandas as pd | |
| from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor | |
| from sklearn.datasets import load_iris | |
| def extract_bins(tree): | |
| '''Extract bins from tree | |
| ''' | |
| edges = {} |
adapted ftom from https://github.com/jonathansick/dotfiles/wiki/Notes-for-Mac-OS-X
ATLAS & LAPACK
ATLAS can be downloaded from SourceForge; these instructions are for atlas 3.10.3. Also grab a copy of LAPACK with
wget http://www.netlib.org/lapack/lapack-3.8.0.tar.gz
Setup Move ATLAS to ~/build. Unpack and setup via:
| # found at https://stackoverflow.com/questions/6620471/fitting-empirical-distribution-to-theoretical-ones-with-scipy-python | |
| %matplotlib inline | |
| import warnings | |
| import numpy as np | |
| import pandas as pd | |
| import scipy.stats as st | |
| import statsmodels as sm | |
| import matplotlib |
| import streams, nre | |
| #[ This script reads a template file and substitutes according to | |
| key-value pairs. All variables in the template start with $. For | |
| example $elementid. | |
| ]# | |
| type | |
| Substitutions = seq[tuple[name: string, value: string]] |