neural_nets.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models

import numpy as np

# new comment

class logistic(nn.Module):
    def __init__(self, in_size=32*32*1, num_classes=10):
        super(logistic, self).__init__()
        self.linear = nn.Linear(in_size, num_classes)

    def forward(self, x):
        out = x.view(x.size(0), -1)
        out = self.linear(out)
        return out


class lstm(nn.Module):
    def __init__(self, input_size=32, hidden_size=128, num_layers=2, num_classes=10):
        super(lstm, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.input_size = input_size
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)
    
    def forward(self, x):
        x = x.reshape(-1, self.input_size, self.input_size)
        # Set initial hidden and cell states 
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).cuda()
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).cuda()
        # Forward propagate LSTM
        out, _ = self.lstm(x, (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size)
        # Decode the hidden state of the last time step
        out = self.fc(out[:, -1, :])
        return out


class cnn(nn.Module):
    def __init__(self):
        super(fedlearnCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(32, 32, kernel_size=5)
        self.fc1 = nn.Linear(800, 256)
        self.fc2 = nn.Linear(256, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(x.size(0), -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x


class VGG(nn.Module):
    '''
    VGG model 
    '''
    def __init__(self, features, size=512, out=10):
        super(VGG, self).__init__()
        self.features = features
        self.classifier = nn.Sequential(
            #nn.Dropout(),
            nn.Linear(size, size),
            nn.ReLU(True),
            #nn.Dropout(),
            nn.Linear(size, size),
            nn.ReLU(True),
            nn.Linear(size, out),
        )
         # Initialize weights
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, np.sqrt(2. / n))
                m.bias.data.zero_()


    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x


def make_layers(cfg):
    layers = []
    in_channels = 3
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)


def vgg11s():
    return VGG(make_layers([32, 'M', 64, 'M', 128, 128, 'M', 128, 128, 'M', 128, 128, 'M']), size=128)

def vgg11():
    return VGG(make_layers([64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']))