ptrblck

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch._C import _infer_size

# Setup
x = Variable(torch.randn(4, 2, 2))
y = Variable(torch.Tensor(4, 2, 2).random_(2))
output = F.sigmoid(x)

ptrblck

import torch
import numpy as np

# numpy
a = np.random.rand(10, 20)
tmp0 = np.split(a, indices_or_sections=5, axis=0) # split into 5 sections
for t in tmp0:
    print(t.shape)

np.split(a, indices_or_sections=7, axis=0) # error, since no equal division

ptrblck

import torch
import torch.nn as nn
from torch.autograd import Variable

# batch_size = 2
x1 = Variable(torch.ones(2, 1))
w1 = Variable(torch.ones(1, 1), requires_grad=True)
y1 = Variable(torch.ones(2, 1) * 2)

ptrblck

#Load packages
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as dsets
from torch.autograd import Variable
import torch.nn.functional as F

torch.manual_seed(2809)

ptrblck

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

import matplotlib.pyplot as plt

import numpy as np

ptrblck

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

class MyDataset(Dataset):
    def __init__(self, data, target):
        self.data = data
        self.target = target

ptrblck

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

from torchvision.utils import make_grid

import matplotlib.pyplot as plt

ptrblck

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim


use_adam = False

class MyModel(nn.Module):
    def __init__(self):

ptrblck

import torch
import torch.nn as nn
import torch.nn.functional as F

import matplotlib.pyplot as plt


output = F.log_softmax(torch.randn(1, 3, 24, 24), 1)
target = torch.zeros(1, 24, 24, dtype=torch.long)
target[0, 4:12, 4:12] = 1

ptrblck

import time

import torch
import torch.nn as nn


device = 'cuda:0'
batch_size = 10
channels = 64
h, w = 128, 128