import torch
import torch.nn as nn


class NNModel(nn.Module):

    def __init__(self):
        super(NNModel, self).__init__()
        self.l1 = nn.Linear(3, 16)
        self.l2 = nn.Linear(16, 64)
        self.l3 = nn.Linear(64, 128)
        self.l4 = nn.Linear(128, 2)
        self.relu = nn.ReLU()
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x):
        x = self.relu(self.l1(x))
        x = self.relu(self.l2(x))
        x = self.relu(self.l3(x))
        x = self.softmax(self.l4(x))
        return x

f456c38201470615dbf36ede76b4bb2d5fad7c06
<mask token>


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

<mask token>


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

    def forward(self, x):
        x = self.relu(self.l1(x))
        x = self.relu(self.l2(x))
        x = self.relu(self.l3(x))
        x = self.softmax(self.l4(x))
        return x

<mask token>


class NNModel(nn.Module):

    def __init__(self):
        super(NNModel, self).__init__()
        self.l1 = nn.Linear(3, 16)
        self.l2 = nn.Linear(16, 64)
        self.l3 = nn.Linear(64, 128)
        self.l4 = nn.Linear(128, 2)
        self.relu = nn.ReLU()
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x):
        x = self.relu(self.l1(x))
        x = self.relu(self.l2(x))
        x = self.relu(self.l3(x))
        x = self.softmax(self.l4(x))
        return x

import torch
import torch.nn as nn


class NNModel(nn.Module):

    def __init__(self):
        super(NNModel, self).__init__()
        self.l1 = nn.Linear(3, 16)
        self.l2 = nn.Linear(16, 64)
        self.l3 = nn.Linear(64, 128)
        self.l4 = nn.Linear(128, 2)
        self.relu = nn.ReLU()
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x):
        x = self.relu(self.l1(x))
        x = self.relu(self.l2(x))
        x = self.relu(self.l3(x))
        x = self.softmax(self.l4(x))
        return x
