# Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection
## 2 Preliminary Concepts
### Convolutional Neural Networks  
In general, an artificial neural network is constructed from the weighted sum of many simple functions (each executed by a single neuron), whose output feeds a nonlinear function or activation function (e.g., ReLU). The function of each neuron is determined by adjustable parameters, which can be iteratively updated (from initial values and examples of inputs and outputs) until the trainable function performs the desired task. The training process refers to the adjustment of the parameters (e.g., weights and biases) that relate the units of the network in order to reduce the error between the actual value and the value predicted by the model. Based on this concept, recent models have been developed such as CNN, recurrent neural networks, and transformers that allow pattern recognition in images, text, speech, or video.  
Convolutional neural networks are a type of neural network that allow the extraction of features in images without the need to elaborate complex rules; consequently, they have been mainly used in computer vision applications for object classification and detection tasks in images. They are mainly composed of convolutional layers, pooling layers, and dense layers [22]. Convolutional layers are the main component of these models, which involve a set of spatial filters that apply a cross-correlation operation on a neighborhood. The pooling layers are used to synthesize the information in each neighborhood, either by calculating the average value (average pooling) or by extracting the maximum value (max pooling). In addition, there are layers that operate on the entire input feature map, either through a global pooling operation that extracts a single value or through the use of dense layers or fully connected (FC) layers that are usually applied at the end of the network for classification purposes [23].  
In addition, hyperparameters are those values that are not learned by the model but adjusted by the designer in relation to the architecture (e.g., type of architecture, network depth, number of filters or neurons), or the training (e.g., type of optimizer, learning rate, number of epochs, batch size) [24].  
In terms of architecture, there are different typologies of CNN. Sequential networks (e.g., VGG11, VGG16, and VGG19) have different depths based on sequential blocks and pooling layers [25]. Residual networks (e.g., ResNet50, ResNet101, ResNet152) use additional connections between convolutional layers of different depth levels [26]. Parallel networks (e.g, InceptionV3) use concatenation connections between convolutional layers of the same depth level [27]. InceptionResNet is a combination of architectures within Inception and ResNet blocks [28]. Separable convolution networks (e.g., MobileNet, Xception) use a depth-wise convolution followed by a point-wise convolution [29; 30]. Dense networks exploit the potential of the network by reusing features and connecting layers in a feed-forward manner to ensure maximum information flow between them [31]. Table 1 presents a summary of the number of parameters and layers of some representative state-of-the-art networks for image classification tasks.