Puzer

ProductId,Score,Text
B000KV61FC,5,"Jack, our 3 year old LabraDoodle (I refer to him as a ""LabraDingo"" sometimes because he's so nutsy) really enjoyed this Jug -- our second one lasted 7 months of heavy use before the rubberized rope broke.  (Another reviewer suggested inserting a tennis ball into the now-ropeless jug in order to salvage it; I'll try that, but am not enamored with the idea of mixing a tennis ball with edible things).  It seems to be his favorite toy -- a good balance of enticement, challenge and reward.<br /><br />PRO:<br />Keeps our dog engaged.<br />Good price ($10 currently)<br /><br />CON:<br />It can be loud rolling around and crashing into things.<br />Hard plastic should be washed before use; unknown chemical safety.<br />Limited durability, but fine for the price.<br /><br />Net: I'm ordering our third!"
B000NMJWZO,4,"I was diagnosed with Gluten Intolerance recently, and was at a loss as to what I could eat - and enjoy!  Upon discovering Pamela's Baking Mix I was pleasantly surprised

Puzer

import daal4py as d4p
from lightgbm import LGBMClassifier

model = LGBMClassifier()
model.fit(X_data, y_data)
y_pred_orig = model.predict_proba(x_data_dense)[:,1]

daal_model = d4p.get_gbt_model_from_lightgbm(model.booster_)

predictions_container = d4p.gbt_classification_prediction(nClasses=2, resultsToEvaluate='computeClassProbabilities', fptype='float')

Puzer

import tensorflow as tf
from tensorflow.contrib.image import matrices_to_flat_transforms, transform

sess = tf.Session()


# affine_M = [[  0.14,   0.  , -18.4 ],
#             [ -0.  ,   0.14, -16.28],
#             [0,        0,     1]]

Puzer

# Assuming that conda enviroment installed and activated
sudo apt install gcc build-essential
conda install numpy ninja pyyaml mkl mkl-include setuptools cmake cffi
git clone --recursive https://github.com/pytorch/pytorch
export USE_CUDA=0
export USE_DISTRIBUTED=0
export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
python setup.py install

Puzer

#!/bin/bash
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O $HOME/miniconda.sh
bash $HOME/miniconda.sh -b -p $HOME/miniconda
rm miniconda.sh
$HOME/miniconda/condabin/conda install jupyter scipy pandas numpy scikit-learn -y
$HOME/miniconda/condabin/conda init
$HOME/miniconda/bin/jupyter-notebook --generate-config
echo "c.NotebookApp.allow_remote_access = True" >> $HOME/.jupyter/jupyter_notebook_config.py
echo "c.NotebookApp.open_browser = False" >> $HOME/.jupyter/jupyter_notebook_config.py
echo "c.NotebookApp.password_required = False" >> $HOME/.jupyter/jupyter_notebook_config.py

Puzer

bpemb_ru = BPEmb(lang='ru', dim=50)


def extract_text(json_data):
    ru_text = list(filter(lambda x: any(1040 <= ord(y) <= 1103 for y in x), json_data.split('"')))
    return ' '.join(ru_text)


def embed_text(text):
    ids = bpemb_ru.encode_ids(text)

Puzer

import cv2
from tqdm import tqdm
import numpy as np
import tensorflow as tf

VIDEO_PATH = 'ch03_20181228181500.avi'
MODEL = 'ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03/frozen_inference_graph.pb'
OUTPUT_TENSORS = ['num_detections:0', 'detection_boxes:0', 'detection_scores:0', 'detection_classes:0']
BATCH_SIZE = 15

Puzer

from run_something import main as run_something

run_something({'input':'a.txt',
               'output':'b.txt'})
# Output: default 5


run_something({'input':'a.txt',
               'default':10,
               'output':'b.txt'})

Puzer

setfacl -dm g:shared_group:rwx some_dir

Puzer

# https://www.tensorflow.org/programmers_guide/using_gpu#allowing_gpu_memory_growth

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.5
sess = tf.Session(config=config)