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# Several basic machine learning models
import torch
from torch import nn
class LogisticRegression(nn.Module):
"""A simple implementation of Logistic regression model"""
def __init__(self, num_feature, output_size):
super(LogisticRegression, self).__init__()
self.linear = nn.Linear(num_feature, output_size)
def forward(self, x):
return self.linear(x)
class NeuralNet(nn.Module):
def __init__(self):
super(NeuralNet, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
#self.fc1 = nn.Linear(16*5*5, 120)
self.fc1 = nn.Linear(16*4*4, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2, 2)
#x = x.view(-1, 4*4*50)
x = x.view(-1, 16*4*4)
x = F.relu(self.fc1(x))
x= F.relu(self.fc2(x))
#x = self.fc2(x)
x = self.fc3(x)
return F.log_softmax(x, dim=1)