Append() for nn.Sequential or directly converting nn.ModuleList to nn.Sequential

QuantScientist · September 6, 2017, 1:24pm

Great example here:

vinhkhuc/PyTorch-Mini-Tutorials/blob/master/5_convolutional_net.py

import numpy as np

import torch
from torch.autograd import Variable
from torch import optim

from data_util import load_mnist


# We need to create two sequential models here since PyTorch doesn't have nn.View()
class ConvNet(torch.nn.Module):
    def __init__(self, output_dim):
        super(ConvNet, self).__init__()

        self.conv = torch.nn.Sequential()
        self.conv.add_module("conv_1", torch.nn.Conv2d(1, 10, kernel_size=5))
        self.conv.add_module("maxpool_1", torch.nn.MaxPool2d(kernel_size=2))
        self.conv.add_module("relu_1", torch.nn.ReLU())
        self.conv.add_module("conv_2", torch.nn.Conv2d(10, 20, kernel_size=5))
        self.conv.add_module("dropout_2", torch.nn.Dropout())

This file has been truncated. show original