import torch.nn as nn
import torch.nn.functional as F


class LogisticClassifier(nn.Module):
    def __init__(self, params):
        super(LogisticClassifier, self).__init__()

        self.params= params
        self.feature_num = int(self.params["feature_num"]) # vocab size
        self.num_class= int(self.params["num_class"])

        # Architecture
        self.linear= nn.Linear(self.feature_num, self.num_class)
        pass

    def forward(self, x):
        x= x.view(x.size(0), -1)
        x= self.linear(x)
        x= F.log_softmax(x, dim=1)
        return x
837d51b18f33038d4295936a31a2358582c19d80
<mask token>


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

<mask token>


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

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

<mask token>


class LogisticClassifier(nn.Module):

    def __init__(self, params):
        super(LogisticClassifier, self).__init__()
        self.params = params
        self.feature_num = int(self.params['feature_num'])
        self.num_class = int(self.params['num_class'])
        self.linear = nn.Linear(self.feature_num, self.num_class)
        pass

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

import torch.nn as nn
import torch.nn.functional as F


class LogisticClassifier(nn.Module):

    def __init__(self, params):
        super(LogisticClassifier, self).__init__()
        self.params = params
        self.feature_num = int(self.params['feature_num'])
        self.num_class = int(self.params['num_class'])
        self.linear = nn.Linear(self.feature_num, self.num_class)
        pass

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

import torch.nn as nn
import torch.nn.functional as F


class LogisticClassifier(nn.Module):
    def __init__(self, params):
        super(LogisticClassifier, self).__init__()

        self.params= params
        self.feature_num = int(self.params["feature_num"]) # vocab size
        self.num_class= int(self.params["num_class"])

        # Architecture
        self.linear= nn.Linear(self.feature_num, self.num_class)
        pass

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