AI News, Designing (and Learning From) a Teachable Machine

Designing (and Learning From) a Teachable Machine

In addition to working towards the principles above, we wrestled with a few key decisions while prototyping.

You might train the model on a ton of pictures of oranges (as shown before) and switch to inference mode—asking the model to “infer,” or make a guess, about a new picture.

Plus, the words “training” and “inference”—while useful to know if you work in the field—aren’t crucial for someone just getting a feel for machine learning.

Should an output be triggered by the confidence level reaching a certain threshold (say, play a GIF when the confidence of a class reaches 90%?) Or does it interpolate between different states based on the confidence of each class (e.g.

shifting between red and blue so that if the model is 50% confident in both classes, it shows purple?) One could imagine an interface that interpolates between two different sounds based on how high your hand is, or one that just switches between the two sounds when your hand crosses the confidence barrier.

Having a dedicated neutral state might help you build a mental model for how to control a specific game, but it’s not quite an honest representation of how the model works.

torch/nn

Module is an abstract class which defines fundamental methods necessary for

Takes an input object, and computes the corresponding output of the module.

backpropagation step consist in computing two kind of gradients at

This function simply performs this task using two

function calls: It is not advised to override this function call in custom classes.

Computes the output using the current parameter set of the class and input.

Keep in mind that, this function uses a simple trick to achieve its goal

after creating it, hence making a deep copy of this module with some

If tensors (or their storages) are shared between multiple modules in a network,

To preserve sharing between multiple modules and/or tensors, use nn.utils.recursiveType:

These state variables are useful objects if one wants to check the guts of a

This contains the output of the module, computed with the last call of forward(input).

This contains the gradients with respect to the inputs of the module, computed with the last call of updateGradInput(input,

Some modules contain parameters (the ones that we actually want to train!).

They should instead override parameters(...) which is, in turn, called by the present function.

This function will go over all the weights and gradWeights and make them view into a single tensor (one for weights and one for gradWeights).

Since the storage of every weight and gradWeight is changed, this function should be called only once on a given network.

This is useful for modules like Dropout or BatchNormalization that have a different behaviour during training vs evaluation.

This is useful for modules like Dropout or BatchNormalization that have a different behaviour during training vs evaluation.

It returns a flattened list of the matching nodes, as well as a flattened list of the container modules for each matching node.

Modules that do not have a parent container (ie, a top level nn.Sequential for instance) will return their self as the container.

This function is very helpful for navigating complicated nested networks.

if you wanted to print the output size of all nn.SpatialConvolution instances: Another use might be to replace all nodes of a certain typename with another.

For example : Which will result in the following output : Clears intermediate module states as output, gradInput and others. Useful

to operate on as a first argument: In the example above train will be set to to true in all modules of model. This

Similar to apply takes a function which applied to all modules of a model, but

Introduction to PyTorch Code Examples

Once you get the high-level idea, depending on your task and dataset, you might want to modify Once you get something working for your dataset, feel free to edit any part of the code to suit your own needs.

The five lines below pass a batch of inputs through the model, calculate the loss, perform backpropagation and update the parameters.

All the other code that we write is built around this- the exact specification of the model, how to fetch a batch of data and labels, computation of the loss and the details of the optimizer.

For operations that do not involve trainable parameters (activation functions such as ReLU, operations like maxpool), we generally use the torch.nn.functional module.

Here’s an example of a single hidden layer neural network borrowed from here: The __init__ function initialises the two linear layers of the model.

In the forward function, we first apply the first linear layer, apply ReLU activation and then apply the second linear layer.

If the input to the network is simply a vector of dimension 100, and the batch size is 32, then the dimension of x would be 32,100.

Let’s see an example of how to define a model and compute a forward pass: More complex models follow the same layout, and we’ll see two of them in the subsequent posts.

You can define the loss function and compute the loss as follows: PyTorch makes it very easy to extend this and write your own custom loss function.

Once gradients have been computed using loss.backward(), calling optimizer.step() updates the parameters as defined by the optimization algorithm.

By this stage you should be able to understand most of the code in train.py and evaluate.py (except how we fetch the data, which we’ll come to in the subsequent posts).

For a multi-class classification problem as set up in the section on Loss Function, we can write a function to compute accuracy using NumPy as: You can add your own metrics in the model/net.py file.

To save your model, call: utils.py internally uses the torch.save(state, filepath) method to save the state dictionary that is defined above.

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