# Multilabel Genre Prediction Using Deep-Learning Frameworks
## 3 Methodology
### The Architecture of Pretrained Models  
The use of a transfer-learning method has made a great contribution to developments in the field of deep learning. Transfer learning allows us to redesign models that were previously trained on very large datasets to solve our own problem. Training a model from scratch takes a lot of time and resources. Thanks to pretrained models, the training cost and time problems are overcome, and the problem is solved quickly and efficiently. In the transfer-learning method, a pretrained model is used as a feature extractor. The top layers of the chosen pretrained model are removed to add new layers to adapt the model to the new task. According to this, the pretrained models mentioned above have been imported to the environment separately without the top layers. In order to provide a fair environment, identical dense layers have been appended to the top of the models. Six of the modern pretrained models were selected for this task which were VGG16 [31], ResNet50 [32], InceptionV3 [33], DenseNet [34], MobileNet [35], and ConvNeXt [36]. The general structures of the used architectures are explained below. VGG16 architecture, which was created with the principle of a small filter and deeper network, is a computer vision model that classifies 1000 different images in 1000 different categories in the field of image recognition and classification with 92.7% accuracy in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) [37] held in 2014. The main motivation of the model is to examine the effect of increasing depth on classification accuracy. The successful results of the model prove that the motivation is correct. The architecture, which takes RGB images of 224 \(\times\) 224 size as the input, consists of 16 convolutional layers, 3 fully connected layers, and the following softmax layer. Filters that are 3 \(\times\) 3 filters  
Figure 4: The distribution of movies which have multiple labels. with a stride of 2 are used in the convolutional layer. Filters that are 2 \(\times\) 2 with a stride of 2 are used in the max pooling layers following some convolutional layers. The ReLU activation function is used after each convolutional and fully connected layer. In order to solve more complex problems, the networks are made deeper. However, contrary to expectations, as the network depth increased, the training and test error rates also increased and the vanishing/gradient exploding problem occurred. The ResNet architecture, which proposes the residual block concept to overcome this problem, came first in the ILSVRC'15 competition held in 2015 with an error rate of 3.57%. Based on the VGG architecture, ResNet is eight times deeper than the VGG architecture but has lower complexity. The ResNet architecture consists of residual block stacks consisting of two convolutional layers with a 3x3 filter, batch normalization layer, and ReLU activation function. Residual block stacks facilitate the model to learn more complex and abstract features. The \"skip connection\" or \"shortcut connection,\" which lets the network skip one or more layers, is the main idea that these blocks explain. Thanks to this technique, the activations of one layer are linked to other layers by skipping some intermediates. The InceptionV3 architecture is based on the main idea of wider networks rather than deepening networks for efficient feature extraction. It consists of repeating Inception modules with different dimensions. Each module consists of 1 \(\times\) 1, 3 \(\times\) 3, and 5 \(\times\) 5 filtered convolutional layers. The primary concept involves the utilization of multiple filters with different sizes and the subsequent concatenation of their respective outputs. This facilitates the model in capturing features at varying spatial resolutions and acquiring diverse representations of the input. The InceptionV3 model employs global average pooling layers. This reduces overfitting and improves the generalizability of the model.