datakop

require 'nn';
net = nn.Sequential()

conv1 = nn.SpatialConvolution(1,64,3,3,2,2,1,1)
conv2 = nn.SpatialConvolution(64,128,3,3,1,1,1,1)
conv3 = nn.SpatialConvolution(128,128,3,3,2,2,1,1)
conv4 = nn.SpatialConvolution(128,256,3,3,1,1,1,1)
conv5 = nn.SpatialConvolution(256,256,3,3,2,2,1,1)
conv6 = nn.SpatialConvolution(256,512,3,3,1,1,1,1)
conv11 = nn.SpatialConvolution(512,512,3,3,1,1,1,1)

datakop

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

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=64,  kernel_size=3,  stride=2,  padding=1)
        self.conv2 = nn.Conv2d(in_channels=64, out_channels=128,  kernel_size=3,  stride=1,  padding=1)