models.py

# -*- coding: utf-8 -*-
"""Models.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1E0U11I6Ncs8fm7oqmR7mKRgLKLQm2oXp
"""

import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
# can use the below import should you choose to initialize the weights of your Net
import torch.nn.init as I

class Net(nn.Module):
  def __init__(self):
    super().__init__()
    self.conv1=nn.Conv2d(1,32,5)
    self.elu=nn.ELU()
    self.pool1=nn.MaxPool2d(2,2)
    self.conv2=nn.Conv2d(32,64,3)
    self.conv3=nn.Conv2d(64,128,3)
    self.conv4=nn.Conv2d(128,256,3)
    self.conv5=nn.Conv2d(256,512,1)
    self.drop1=nn.Dropout(p=0.1)
    self.drop2=nn.Dropout(p=0.15)
    self.drop3=nn.Dropout(p=0.25)
    self.drop4=nn.Dropout(p=0.35)
    self.drop5=nn.Dropout(p=0.4)
    self.drop6=nn.Dropout(p=0.45)
    self.drop7=nn.Dropout(p=0.5)
    self.dense1=nn.Linear(512*6*6,2048)
    self.dense2=nn.Linear(2096,1024)
    self.dense3=nn.Linear(1024,512)
    self.output=nn.Linear(512,136)

  def forward(self,x):
    x=self.drop1(self.pool1(F.elu(self.conv1(x))))
    x=self.drop2(self.pool1(F.elu(self.conv2(x))))
    x=self.drop3(self.pool1(F.elu(self.conv3(x))))
    x=self.drop4(self.pool1(F.elu(self.conv4(x))))
    x=self.drop5(self.pool1(F.elu(self.conv5(x))))
    x=x.view(x.size(0),-1)
    x=self.drop4(self.elu(self.dense1(x)))
    x=self.drop6(self.elu(self.dense2(x)))
    x=self.drop6(self.elu(self.dense3(x)))
    x=self.output(x)
    return x