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MATLAB has a deep network designer app that allows users to interactively build and edit deep learning networks. You can open this app by going to the Apps tab and under Machine Learning & Deep Learning, click on the icon of the application. Alternatively, if you are excellent at coding, you can open the deep network design app from the command line. Just type deepNetworkDesigner. To copy or modify a network that is already existing, simply select it from the start page. You can also load a network from the workspace by clicking new on the designer tab. The same process also applies if you want to select a pretrained network or want to build one from scratch.

The app allows you to build a network using any of the built-in layers. Moreover, you can create custom layers at the command line and then import the network into the app. The app's designer pane is where you construct, edit, and analyze your network. We are specialists in network design using MATLAB. If you feel that the task assigned to you is too complicated, opt for our network design homework help. We guarantee unique designs that will convince your professor to award you a decent grade.

First, you have to drag and connect blocks from the layer library to assemble a network. It is recommended that you work with blocks of layers at a time. Next, you should highlight multiple layers, then copy and paste or delete. You can also view and edit the properties of a layer by selecting it and clicking the help icon next to it. This will provide you with additional information about the layer.

On the list of deep learning layers page, you can select the layer name on the table for detailed information on the layer's properties. Sometimes it is useful to create a block of layers to connect and copy repeated units. A perfect example is using blocks of layers to develop multiple copies of batch normalization, groups of convolution, and ReLU layers. To make pre-trained networks deeper, you can add layers at their end. When working with small input images, you can alternatively edit a pre-trained network to simplify it. For example, a simple network can be created by deleting units of layers (like inception modules from a GoogLeNet network).

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● Checking your network

On the designer tab, there is the Analyze option. Click on it to check your network and evaluate the layers in finer detail. You can resolve problems related to size mismatches in the network by examining and investigating the layer properties. You can edit layers by going back to the app and clicking on analyze again to examine the results. Your network will only be ready for training if the analyzer returns zero errors.

● Exporting a network

You can also export an untrained network to the workspace for training. Go to the designer tab and click export. This action will export your network of choice to a new variable that has the edited network layers. After you are done with exporting, you can train the layer variable by supplying it to the trainNetwork function.

● Using deep network designer to train a network

The deep network designer app supports training a network for image classification hurdles. To do this go the data tab and select import data. This will allow you to choose the image data that you want to train the network on. After you are done with the data importation, click on Train on the training tab. The app will copy the network you have constructed in the designer pane and train it.

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