aidevnn

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
from keras.callbacks import Callback
import numpy as np
import time
from tabulate import tabulate

X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([[0],[1],[1],[0]])

aidevnn

import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()

aidevnn

Issue-driven Development

Principle of issue-driven development is simple:

• Always associate development to GitHub issue.

Issue-driven development achieves:

• Modularity: Commits and branches are self-contained.
• Granularity: Commits and branches intend a single Issue.
• Transparency: Discussion on each edition can be seen on Issue.