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()

JonathanLoscalzo

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

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

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

{
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

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

#!/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

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

Create a new project

poetry new <project-name>

Add a new lib

potry add <library>

Remove a lib