Specify the number of filters using the numFilters argument with The number of filters determines the number of channels in the output of a convolutional For example, if the input is a color image, the number of color channels is 3. Is the width of the filter, respectively, and c is the number of channels The number of weights in a filter is h * w * Map represents the input and the upper map represents the output. This image shows a 3-by-3 filter scanning through the input with a stride of 2. Local regions that the neurons connect to can overlap depending on the Specify the step size with the Stride name-value pair argument. The step size with which the filter moves is called a stride. Input and the upper map represents the output. This image shows a 3-by-3 filter scanning through the input. Other words, the filter convolves the input. Input image vertically and horizontally, repeating the same computation for each region. Weights and the input, and then adds a bias term. When creating a layer using the convolution2dLayer function, you can specify the size of these regions usingįor each region, the trainNetwork function computes a dot product of the The layer learns the features localized by these regions 1 Filters and StrideĪ convolutional layer consists of neurons that connect to subregions of the input images or The convolutional layer consists of various components. Create a 2-D convolutional layer using convolution2dLayer. Image corresponds to the height, width, and the number of color channels of that image.įor example, for a grayscale image, the number of channels is 1, and for a color imageĪ 2-D convolutional layer applies sliding convolutional filters Specify the image size using the inputSize argument. Images to a network and applies data normalization. To specify the architecture of a network where layersĬan have multiple inputs or outputs, use a LayerGraphĬreate an image input layer using imageInputLayer. To specify the architecture of a neural network with all layers connected sequentially,Ĭreate an array of layers directly. Layers as an input to the training function For example, to create a deep network which classifiesĢ8-by-28 grayscale images into 10 classes, specify the layer To specify the architecture of a deep network with all layers connected sequentially,Ĭreate an array of layers directly. Of colored images, you might need a more complicated network with multiple convolutional and On the other hand, for more complex data with millions Smaller network with only one or two convolutional layers might be sufficient to learn on a Whereas regression networks must have a regression layer at the end of the network. ForĮxample, classification networks typically have a softmax layer and a classification layer, The types and number of layers included depends on the particular application or data. The network architecture can vary depending on the types and numbers of layers included. Your own custom layers, see Define Custom Deep Learning Layers. Networks for sequence classification and regression, see Long Short-Term Memory Networks. For a complete list of deep learning layers and how toĬreate them, see List of Deep Learning Layers. This topic explains the details of ConvNet layers, and the The first step of creating and training a new convolutional neural network (ConvNet) is toĭefine the network architecture. Specify Layers of Convolutional Neural Network