arunmallya

import torch.nn as nn

def myModule(nn.Module):
  def __init__(self):
    # Init stuff here
    self.X = nn.Sequential(
               nn.Linear(num_input_genes, num_tfs),
               nn.ReLU(),
               nn.BatchNorm1d(num_tfs)
             )

arunmallya

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

class SimpleModel(nn.Module):
	def __init__(self):
		super(SimpleModel, self).__init__()
		self.net = nn.Linear(10, 2)

	def forward(self, inputs):

arunmallya

# Since convolution takes the most time, only do it on images with
# mask = 1. Note that masks.data.nonzero() is of size (N, 1).
# As a result, when expanding to 4 dims, we need to unsqueeze it twice.
selected_idx = Variable(masks.data.nonzero().unsqueeze(2).unsqueeze(3).repeat(
    1, images.size(1), images.size(2), images.size(3)))
selected_images = torch.gather(images, 0, selected_idx)

# Get image features from CNN and linear layer rnn_emb.
some_im_feats = self.rnn_emb(self.cnn(selected_images).squeeze())