thecaffeinedev

version: '3.1'

services:
  zookeeper:
    platform: linux/amd64
    container_name: zookeeper
    image: zookeeper:3.4
    restart: on-failure
    volumes:
      - "./zookeeper/data:/data"

thecaffeinedev

# parameters
nc: 7  # number of classes  # CHANGED HERE
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple

# anchors
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

thecaffeinedev

<h1>SignUp</h1>
<form method="POST">
{% csrf_token %}

{{ form.as_p }}

<button type="submit">Sign Up</button>

</form>

thecaffeinedev

absl-py==0.8.1
astor==0.8.0
certifi==2019.11.28
chardet==3.0.4
cogapp==3.0.0
Cython
decorator==4.4.1
defusedxml==0.6.0
gast==0.3.2
google-pasta==0.1.8

thecaffeinedev

for c in classes:
    print(c)
    verify_images(path/c, delete=True, max_size=500)

thecaffeinedev

urls = Array.from(document.querySelectorAll('.rg_di .rg_meta')).map(el=>JSON.parse(el.textContent).ou);
window.open('data:text/csv;charset=utf-8,' + escape(urls.join('\n')));

thecaffeinedev

import glob, os

# change the path of your images folder
dataset_path = '/home/thecaffeinedev/yolov3/training/images'

# Percentage of images to be used for the test set
percentage_test = 30;

# Create and/or truncate train.txt and test.txt
file_train = open('train.txt', 'w')  

thecaffeinedev

import numpy as np

def nonlin(x,deriv=False):
	if(deriv==True):
	    return x*(1-x)

	return 1/(1+np.exp(-x))
    
X = np.array([[0,0,1],
            [0,1,1],

thecaffeinedev

# Here, about 1.2 MB of data produced a pickle of about 120MB Of data 100 times
    # training might take a lot of time on bigger data sets
    # saving the model as you train it helps here

import tensorflow as tf
from TF_own_data_model import create_feature_sets_and_labels
import numpy as np

# can load the data from the pickle
    # or you could just write it down

thecaffeinedev

from numpy import exp, array, random, dot


class NeuralNetwork():
    def __init__(self):
        # Seed the random number generator, so it generates the same numbers
        # every time the program runs.
        random.seed(1)

        # We model a single neuron, with 3 input connections and 1 output connection.