I’m new in pytorch. Sorry if my question is stupid.
I have a multiclass classification problem and for it I have a convolutional neural network that has Linear layer in its last layer.
I get predictions from this model so it gives me a tensor that has n_class elements. For example:
tensor([class_1, class_2, class_3])
class CNN(nn.Module):
    
    # Constructor
    def __init__(self):
        
        super(CNN, self).__init__()
        
        
        # cnn and maxpool layers
        self.cnn_ = nn.Conv2d(in_channels=1, 
                         out_channels=conv_kernel_output_channel, 
                         kernel_size=(filter_size, embedding_size), 
                         stride=1, 
                         padding=0)
        self.maxpool_ = nn.MaxPool2d(kernel_size=(longest_sentence_length - filter_size + 1, 1), 
                                stride=1, 
                                padding=0)
        
        # droput layer
        self.drop = nn.Dropout(p = dropout_keep_prob)
        
        # fully connected layer
        self.fc1 = nn.Linear(conv_kernel_output_channel * len(filter_sizes), num_classes)
    
    
    # Prediction
    def forward(self, x):
        # convolution layer
        x1 = self.cnn_(x)

        # apply activation function
        x1 = torch.relu(x1)

        # maxpooling layer
        x1 = self.maxpool_(x1)

        # flatten output
        x1 = x1.view(x1.size(0), -1)
        
        # dropout
        x1 = self.drop(x1)
        
        # fully connected layer
        x1 = self.fc1(x1)
        
        # no softmax at the end, because cross entropy loss have it implicitly
        
        return x1

pred = CNN(x)
_, label_1 = torch.max(pred)

pred_soft = F.softmax(pred)
_, label_2 = torch.max(pred_soft )

Questions:
1- Why getting the torch.max() from this prediction will give us the label, I mean why for desired label our model produce bigger values? What is the logic behind this?
2- why getting the torch.max() from this prediction and from F.softmax() will give use same results and why we can interpret them as same and is enough to use one of them for getting the predicted label?