Delete NN_Arch.py

NN_Arch.py

@@ -1,77 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.Functional as F
-
-
-# MLP Arch with 1 Hidden layer
-
-
-class MLP(nn.Module):
-    def __init__(self, input_dim, hidden, out_dim):
-
-        super(MLP, self).__init__()
-        self.linear1 = nn.Linear(input_dim, hidden)
-        self.linear2 = nn.Linear(hidden, out_dim)
-        self.relu = nn.ReLU()
-        self.dropout = nn.Dropout()
-        self.softmax = nn.Softmax(dim=1)
-
-    def forward(self, x):
-        x = x.view(-1, x.shape[1]*x.shape[-2]*x.shape[-1])
-        x = self.linear1(x)
-        x = self.dropout(x)
-        x = self.relu(x)
-        x = self.linear2(x)
-        return self.softmax(x)
-
-
-# CNN Arch for MNIST
-
-
-class CNN_Mnist(nn.Module):
-    def __init__(self, args):
-
-        super(CNN_Mnist, self).__init__()
-        self.conv1 = nn.Conv2d(args.num_channels, 10, kernel_size=5)
-        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
-        self.dropout_2d = nn.Dropout2d()
-        self.fc1 = nn.Linear(320, 50)
-        self.fc2 = nn.Linear(50, args.num_classes)
-
-    def forward(self, x):
-        x = F.max_pool2d(self.conv1(x), 2)
-        x = F.relu(x)
-        x = F.max_pool2d(nn.Dropout2d(self.conv2(x)), 2)
-        x = F.relu(x)
-        x = x.view(-1, x.shape[1]*x.shape[2]*x.shape[3])
-        x = F.relu(self.fc1(x))
-        x = F.dropout(x, training=self.training)
-        x = self.fc2(x)
-        return F.log_softmax(x, dim=1)
-
-
-# CNN Arch -- CIFAR
-
-
-class CNN_Cifar(nn.Module):
-
-    def __init__(self, args):
-
-        super(CNN_Cifar, self).__init__()
-        self.conv1 = nn.Conv2d(3, 6, 5)
-        self.pool = nn.MaxPool2d(2, 2)
-        self.conv2 = nn.Conv2d(6, 16, 5)
-        self.fc1 = nn.Linear(16*5*5, 120)
-        self.fc2 = nn.Linear(120, 84)
-        self.fc3 = nn.Linear(84, args.num_classes)
-
-    def forward(self, x):
-        x = F.relu(self.conv1(x))
-        x = self.pool(x)
-        x = F.relu(self.conv2(x))
-        x = self.pool(x)
-        x = x.view(-1, 16*5*5)  # Dim of fc1
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        x = self.fc3(x)
-        return F.log_softmax(x, dim=1)