ikhlestov

import torch
from torch.autograd import Variable

# new way with `init` module
w = torch.Tensor(3, 5)
torch.nn.init.normal(w)
# work for Variables also
w2 = Variable(w)
torch.nn.init.normal(w2)
# old styled direct access to tensors data attribute

ikhlestov

import torch
from torch.autograd import Variable

# requires grad
# If there’s a single input to an operation that requires gradient,
# its output will also require gradient.
x = Variable(torch.randn(5, 5))
y = Variable(torch.randn(5, 5))
z = Variable(torch.randn(5, 5), requires_grad=True)
a = x + y

ikhlestov

# scheduler example
from torch.optim import lr_scheduler

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

for epoch in range(100):
    scheduler.step()
    train()
    validate()

ikhlestov

from collections import OrderedDict

import torch.nn as nn

model = nn.Sequential(OrderedDict([
    ('conv1', nn.Conv2d(1, 20, 5)),
    ('relu1', nn.ReLU()),
    ('conv2', nn.Conv2d(20, 64, 5)),
    ('relu2', nn.ReLU())
]))

ikhlestov

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

ikhlestov

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()

ikhlestov

from itertools import combinations, permutations
import numpy as np

def check_line(line):
    start = False
    for i in line:
        if i != 0 and not start:
            start = True
            counter = 0
        elif i == 0 and start: