# Real-Time Advanced Computational Intelligence for Deep Fake Video Detection
## 5 Experiments
### Experimental Settings  
1. Drop Out: This parameter is used to reduce over fitting. To train all nodes equally, we drop out some neurons for a particular epoch. To do so, we randomly drop neurons in our model during training. This forces the network to share information between weights, which leads to an increase in its ability to generalize to new data. Some nodes having more weight may get turned off multiple times, and some nodes having less weight may not turn off even once. The chance is between 0-1. This determines the fraction of neurons that should be turned off from the previous layer. In our proposed model, we used a dropout value of 0.45.
2. Learning Rate: This parameter is tuned to control the change in the model after each time the weights are updated. The learning rate generally decides the time taken by the model to converge. It is the step that is taken by the model each time to move close to convergence. If the learning rate is too large, the model takes larger steps, and it will converge fast; as a result, there is the chance that the model may not converge exactly on the minimum, and the minimum may get missed. If the learning rate is too small, then the model will take a very large time to converge. The best learning rate is one that decreases as the model gets closer to the solution. We used Reduce LR on the plateau learning rate scheduler in Pytorch. It reduces the learning by itself if accuracy does not improve in 2-3 epochs. In our proposed model, we used a learning rate of 0.001.
3. Epochs: This parameter is the count of iterations. In each iteration, the model is trained on the full training dataset. A greater number of iterations leads to high-performance gains of the model to some extent, as in each iteration the weights are adjusted based on error calculation on the previous iteration. This reduces the error in  
Figure 7: Data visualization.  
each following iteration. To maintain the trade-off between the time taken in training the model and performance gains, the number of epochs must be selected wisely. In our proposed model, we used 30 epochs.
4. Batch Size: This parameter defines the number of input samples that will be sent to the model at once for training. The complete training dataset is divided into batches, each with a size equal to that of batch size. The model is trained on one batch at a time. A large batch size trains the model fast and takes less memory, but the model has less learning, whereas too small a size may lead to large memory requirements and more training time for the model. Thus, the batch size must be selected, maintaining the tradeoff between accuracy and training time of the model. In our proposed model, we used 128 as the batch size.
5. Optimizer: We used a loss function to calculate the loss or the wrong predictions made by our model. Then we tried to reduce the loss function by tuning the hyper parameters. Optimizers tie loss function with the parameters of the model. They update the model in response to the outcome of the loss function. We used Adam Optimizer to optimize our model. It is an adaptive optimizer. It is the combination of AdaGrad and RMSProp, and hence combines the advantages of both.
6. Activation Function: Activation functions are an integral part of the artificial neural network. They help the model to learn complex patterns, and decide what will be fired as an input to the next neuron. The Swish activation function is a multiplication of linear and sigmoid activation functions. It has solved the problem of ReLu where the negative values are nullified to zero. We found in our model architecture that Swish performs better than the ReLu activation function.  
\[\text{Swish(x)}=\text{x}\times\text{sigmoid(x)} \tag{4}\]  
Table 4 shows various parameters used in our proposed model. We used these parameters after experimenting with our model on various other values and have concluded that our model performs best with these parameters.