JonathanLoscalzo

Create a new project

poetry new <project-name>

Add a new lib

potry add <library>

Remove a lib

JonathanLoscalzo

version: '3'
services:
 influxdb:
  image: 'influxdb:latest'
  container_name: influxDB
  networks:
    - tick-network
  volumes:
    - '/home/tick/influxdb/data:/var/lib/influxdb'
    - '/home/tick/influxdb/config/:/etc/influxdb/'

JonathanLoscalzo

#!/bin/sh

if [ "$PYENV_VERSION" -ne "" ]
then
    name=`pyenv version-name`
    python=`pyenv which python`
else
    name=`basename "$VIRTUAL_ENV"`
    python="$VIRTUALENV/bin/python"
fi

JonathanLoscalzo

def make_weights_for_balanced_classes(images, nclasses):                        
    count = [0] * nclasses                                                      
    for item in images:                                                         
        count[item[1]] += 1                                                     
    weight_per_class = [0.] * nclasses                                      
    N = float(sum(count))                                                   
    for i in range(nclasses):                                                   
        weight_per_class[i] = N/float(count[i])                                 
    weight = [0] * len(images)                                              
    for idx, val in enumerate(images):                                          

JonathanLoscalzo

{
0: "tench, Tinca tinca",
1: "goldfish, Carassius auratus",
2: "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
3: "tiger shark, Galeocerdo cuvieri",
4: "hammerhead, hammerhead shark",
5: "electric ray, crampfish, numbfish, torpedo",
6: "stingray",
7: "cock",
8: "hen",

JonathanLoscalzo

class ImagesDataset(torch.utils.data.Dataset):
    pass

class Net(nn.Module):
    pass

model = Net()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
scheduler = lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
criterion = torch.nn.MSELoss()

JonathanLoscalzo

import torch
import torchvision as tv


class ImagesDataset(torch.utils.data.Dataset):
    def __init__(self, df, transform=None,
                 loader=tv.datasets.folder.default_loader):
        self.df = df
        self.transform = transform
        self.loader = loader

JonathanLoscalzo

Directory structure for JavaScript/Node Projects

While the following structure is not an absolute requirement or enforced by the tools, it is a recommendation based on what the JavaScript and in particular Node community at large have been following by convention.

Beyond a suggested structure, no tooling recommendations, or sub-module structure is outlined here.

Directories

• lib/ is intended for code that can run as-is
• src/ is intended for code that needs to be manipulated before it can be used

JonathanLoscalzo

JonathanLoscalzo

import numpy as np
import tensorflow as tf
from keras.callbacks import TensorBoard
from keras.layers import Input, Dense
from keras.models import Model


def write_log(callback, names, logs, batch_no):
    for name, value in zip(names, logs):
        summary = tf.Summary()