tutorial_james.html

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            <h1 class="masthead-heading mb-0">James</h1>
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                    James is the name of a humanoid robot project developed by University of Genoa and Italian Institute of Technology (IIT).
                    The neural network controller for the arm of the robot consists of 8 inputs and 6 outputs: the input consists of two 
                    vectors of four elements each, one related to the velocity and the other to the angle of the shoulder and elbow joints. 
                    The outputs are the corresponding values of internal forces and torques in a Cartesian space.
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                    <p>
                        The picture below shows the formal representation of the feed-forward neural network for this example.
                        The architecture consists of 3 hidden layers with ReLU activation functions and a decreasing
                        number of neurons, namely 64, 32 and 16.
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                            NeVer2 allows to add sequential layers directly by clicking on the corresponding buttons in the left toolbar.
                            The layer block will be placed in the correct position with default parameters which can be edited directly.
                            Note that adding a layer will block the editing of previous layers to maintain consistency. It is always possible
                            to remove nodes at the end of a network with the "DEL" key. This also applies to the input block, where it is
                            possible to change the input identifier and to set the number of inputs of the network.
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                        <p>
                            The first block is a Fully Connected layer consisting of 64 neurons. To do so, it is sufficient to set the
                            <i>out_features</i> parameter to 64 and to click on <i>Save</i>.
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                            <img class="img-tutorial" src="./assets/img/tutorials/james/first_layer.png" alt="Fully Connected block"></img>
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                        <p>
                            To add the other layers we select the corresponding block in the left toolbar. ReLU activation functions have
                            no parameters and new Fully Connected layers can be added directly. In the example below it is presented the
                            complete network. In this example it is also visible how the last layer computes the output which is then
                            shown in the output block.
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                            <img class="img-tutorial-full" src="./assets/img/tutorials/james/james_net.png" alt="James network"></img>
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                        Once the network is completed, it is time to train it on the dataset. In the menu bar, selecting 
                        "Learn..." -> "Train" displays the window for the training setup. The first thing to do is to select
                        the dataset: we made directly available the MNIST and Fashion MNIST datasets, but it is possible to
                        open any dataset as a text file selecting "Custom data source...".
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                        In order to process correctly the dataset, it is required to provide information on the <i>Data type</i>
                        to expect and the <i>Delimiter</i> character, which are set by default as <i>float</i> and '<i>,</i>'.
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                            <img class="img-tutorial-wide" src="./assets/img/tutorials/james/training_setup.png" alt="Training parameters of the network"></img>
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                        The learning parameters can be set as follows:<br><br>
                        <b><i>Optimizer</i></b>: at the moment only "Adam" (adaptive moment estimation) is supported, being
                        the most popular and effective gradient based optimization algorithm. Once selected, you can edit all
                        its related parameters.<br><br>
                        <b><i>Learning Rate Scheduler</i></b>: like the Optimizer, only "ReduceLROnPlateau" is supported. It adjusts the
                        learning rate only when a plateau in model performance is detected, e.g. no change for a given number of
                        training epochs.<br><br>
                        <b><i>Loss Function</i></b>: you can choose between "Cross Entropy" and "MSE Loss", depending on the network
                        structure.<br><br>
                        <b><i>Precision Metric</i></b>: you can choose between "Inaccuracy" and "MSE loss".<br><br>
                        <b><i>Epochs</i></b>: it defines the steps that the learning algorithm will perform through the training process.
                        <br><br>
                        <b><i>Validation Percentage</i></b>: (a number between 0 and 1) indicates the percentage of the dataset to use
                        as the validation set.<br><br>
                        <b><i>Training batch size</i></b> and <b><i>Validation batch size</i></b>: it defines the dimension of the
                        training batches.<br><br>
                        <b><i>Cuda</i></b>: it allows to take advantage of NVidia GPU architecture for the computation.<br><br>
                        <b><i>Train patience</i></b> <i>(optional)</i>: the number of epochs in which the loss may not decrease
                        before the training procedure is interrupted with early stopping.<br><br>
                        <b><i>Checkpoints root</i></b> <i>(optional)</i>: where to store the checkpoints of the training strategy, by
                        default the working directory of NeVer2.<br><br>
                        <b><i>Verbosity level</i></b> <i>(optional)</i>: how many batches between training log prints (default 1).
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                            Once the parameters are set, clicking "Train Network" runs the training. Logs are printed in a box
                            below the buttons.
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                                <img class="img-tutorial" src="./assets/img/tutorials/james/training_loss.png" alt="Training Results"></img>
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                        <p>
                          The <b>Best Loss Score</b> label indicates the smallest training loss; in this case,
                          with a 10 epoch long training the best loss is 0.00048, corresponding to 0.048%. Looking
                          at the following diagram shows how increasing the training epochs improves slightly
                          the training loss.
                        </p>
                        <img class="center" src="./assets/img/tutorials/james/james_loss.png" width="60%" alt="Training Loss"></img>
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                    <h1 class="titles-tutorial">In progress...</h1>
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