Multi-layer Perceptron classifier #493
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| { | ||
| "id": "ml_fit_class_mlp", | ||
| "summary": "Train a Multilayer Perceptron classification model", | ||
| "description": "Fit a Multilayer Perceptron (MLP) classification model to training data. MLP is a class of feedforward artificial neural network (ANN) that consists of at least three layers of nodes: an input layer, a hidden layer, and an output layer. MLP utilizes a supervised learning technique called backpropagation for training.", | ||
| "categories": [ | ||
| "machine learning", | ||
| "deep learning" | ||
| ], | ||
| "experimental": true, | ||
| "parameters": [ | ||
| { | ||
| "name": "predictors", | ||
| "description": "The predictors for the MLP classification model as a vector data cube. These are the independent variables that the MLP algorithm analyses to learn patterns and relationships within the data.", | ||
| "schema": [ | ||
| { | ||
| "type": "object", | ||
| "subtype": "datacube", | ||
| "dimensions": [ | ||
| { | ||
| "type": "geometry" | ||
| }, | ||
| { | ||
| "type": "bands" | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "type": "object", | ||
| "subtype": "datacube", | ||
| "dimensions": [ | ||
| { | ||
| "type": "geometry" | ||
| }, | ||
| { | ||
| "type": "other" | ||
| } | ||
| ] | ||
| } | ||
| ] | ||
| }, | ||
| { | ||
| "name": "target", | ||
| "description": "The dependent variable for MLP classification. These are the labeled data, aligning with predictor values based on a shared geometry dimension. This ensures a clear connection between predictor rows and labels.", | ||
| "schema": { | ||
| "type": "object", | ||
| "subtype": "datacube", | ||
| "dimensions": [ | ||
| { | ||
| "type": "geometry" | ||
| } | ||
| ] | ||
| } | ||
| }, | ||
| { | ||
| "name": "hidden_layers", | ||
|
There was a problem hiding this comment. We define the shape of hidden layers here. But where do we define the shape of input and output layers? |
||
| "description": "The number and size of hidden layers in the MLP.", | ||
| "schema": { | ||
| "type": "array", | ||
| "items": { | ||
| "type": "integer", | ||
| "minimum": 1 | ||
| }, | ||
| "default": [ | ||
| 512, | ||
| 512, | ||
| 512 | ||
| ] | ||
| } | ||
| }, | ||
| { | ||
| "name": "dropout_rates", | ||
| "description": "Dropout rates for the hidden layers. Each value corresponds to the dropout rate for a specific layer.", | ||
| "schema": { | ||
| "type": "array", | ||
| "items": { | ||
| "type": "number", | ||
| "minimum": 0, | ||
| "maximum": 1 | ||
| }, | ||
| "default": [ | ||
| 0.4, | ||
| 0.3, | ||
| 0.2 | ||
| ] | ||
| } | ||
| }, | ||
| { | ||
| "name": "epochs", | ||
| "description": "The number of epochs for training the model.", | ||
| "schema": { | ||
| "type": "integer", | ||
| "minimum": 1, | ||
| "default": 100 | ||
| } | ||
| }, | ||
| { | ||
| "name": "batch_size", | ||
| "description": "Size of minibatches for stochastic optimizers.", | ||
| "schema": { | ||
| "type": "integer", | ||
| "minimum": 1, | ||
| "default": 64 | ||
| } | ||
| }, | ||
| { | ||
| "name": "activation_function", | ||
| "description": "Activation function for the hidden layers.", | ||
| "schema": { | ||
| "type": "string", | ||
| "enum": [ | ||
| "relu", | ||
| "tanh", | ||
| "sigmoid" | ||
| ], | ||
| "default": "relu" | ||
| } | ||
| }, | ||
|
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There was a problem hiding this comment. This might need some minor corrections. We cannot specify a specific activation function per layer. A choice might be more appropriate
It might also be helpful to include This could be the |
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| { | ||
| "name": "optimizer", | ||
| "description": "The gradient descent algorithm for weight optimization.", | ||
| "schema": { | ||
| "type": "string", | ||
| "enum": [ | ||
| "adam", | ||
| "sgd", | ||
| "lbfgs" | ||
| ], | ||
| "default": "adam" | ||
| } | ||
| }, | ||
| { | ||
| "name": "random_state", | ||
| "description": "Sets the seed of the algorithm's internal random number generator for initializing weights, biases, and data splitting in 'sgd' or 'adam' optimizers. Use an integer for consistent results across function calls.", | ||
| "optional": true, | ||
| "default": null, | ||
| "schema": { | ||
| "type": [ | ||
| "integer", | ||
| "null" | ||
| ] | ||
| } | ||
| } | ||
| ], | ||
| "returns": { | ||
| "description": "A model object that can be saved with ``save_ml_model()`` and restored with ``load_ml_model()``.", | ||
| "schema": { | ||
| "type": "object", | ||
| "subtype": "ml-model" | ||
| } | ||
| }, | ||
| "links": [ | ||
| { | ||
| "href": "https://arxiv.org/abs/1611.06455", | ||
| "title": "Z. Wang, W. Yan, and T. Oates (2017), Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline", | ||
| "type": "text/html", | ||
| "rel": "about" | ||
| } | ||
| ] | ||
| } | ||
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| Original file line number | Diff line number | Diff line change |
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@@ -47,3 +47,12 @@ Hyndman | |
| date1 | ||
| date2 | ||
| favor | ||
| Wang | ||
| Yan | ||
| Oates | ||
| adam | ||
| sgd | ||
| minibatches | ||
| Perceptron | ||
| feedforward | ||
| backpropagation | ||
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Could be helpful to somehow divide training data into train/test to get training statistics independently from the training data, e.g.