import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch import optim
import numpy as np

class MLP(nn.Module):
	def __init__(self, input_dim, hidden_dim, num_classes):
		super(MLP, self).__init__()
		self.fc = nn.Sequential(
			nn.Linear(input_dim, hidden_dim),
			nn.ReLU(inplace=True),
			nn.Linear(hidden_dim, num_classes)
		)

	def forward(self, x):
		x = x.view(x.size()[0], -1) # flatten
		out = self.fc(x)
		return out
cc7925180f9aa2853f6b3ea4501f266423387979
<mask token>


class MLP(nn.Module):
    <mask token>
    <mask token>

<mask token>


class MLP(nn.Module):
    <mask token>

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

<mask token>


class MLP(nn.Module):

    def __init__(self, input_dim, hidden_dim, num_classes):
        super(MLP, self).__init__()
        self.fc = nn.Sequential(nn.Linear(input_dim, hidden_dim), nn.ReLU(
            inplace=True), nn.Linear(hidden_dim, num_classes))

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

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch import optim
import numpy as np


class MLP(nn.Module):

    def __init__(self, input_dim, hidden_dim, num_classes):
        super(MLP, self).__init__()
        self.fc = nn.Sequential(nn.Linear(input_dim, hidden_dim), nn.ReLU(
            inplace=True), nn.Linear(hidden_dim, num_classes))

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

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch import optim
import numpy as np

class MLP(nn.Module):
	def __init__(self, input_dim, hidden_dim, num_classes):
		super(MLP, self).__init__()
		self.fc = nn.Sequential(
			nn.Linear(input_dim, hidden_dim),
			nn.ReLU(inplace=True),
			nn.Linear(hidden_dim, num_classes)
		)

	def forward(self, x):
		x = x.view(x.size()[0], -1) # flatten
		out = self.fc(x)
		return out