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Conjuntos de datos de muestra para redes neuronales superficiales

Deep Learning Toolbox™ contiene varios conjuntos de datos de muestra que puede utilizar para experimentar con redes neuronales superficiales. Para ver los conjuntos de datos disponibles, utilice el siguiente comando:

help nndatasets
  Neural Network Datasets
  Function Fitting, Function approximation and Curve fitting.
  Function fitting is the process of training a neural network on a
  set of inputs in order to produce an associated set of target outputs.
  Once the neural network has fit the data, it forms a generalization of
  the input-output relationship and can be used to generate outputs for
  inputs it was not trained on.
   simplefit_dataset     - Simple fitting dataset.
   abalone_dataset       - Abalone shell rings dataset.
   bodyfat_dataset       - Body fat percentage dataset.
   building_dataset      - Building energy dataset.
   chemical_dataset      - Chemical sensor dataset.
   cho_dataset           - Cholesterol dataset.
   engine_dataset        - Engine behavior dataset.
   vinyl_dataset         - Vinyl bromide dataset.
  Pattern Recognition and Classification
  Pattern recognition is the process of training a neural network to assign
  the correct target classes to a set of input patterns.  Once trained the
  network can be used to classify patterns it has not seen before.
   simpleclass_dataset     - Simple pattern recognition dataset.
   cancer_dataset          - Breast cancer dataset.
   crab_dataset            - Crab gender dataset.
   glass_dataset           - Glass chemical dataset.
   iris_dataset            - Iris flower dataset.
   ovarian_dataset         - Ovarian cancer dataset.
   thyroid_dataset         - Thyroid function dataset.
   wine_dataset            - Italian wines dataset.
   digitTrain4DArrayData   - Synthetic handwritten digit dataset for
                             training in form of 4-D array.
   digitTrainCellArrayData - Synthetic handwritten digit dataset for
                             training in form of cell array.
   digitTest4DArrayData    - Synthetic handwritten digit dataset for
                             testing in form of 4-D array.
   digitTestCellArrayData  - Synthetic handwritten digit dataset for
                             testing in form of cell array.
   digitSmallCellArrayData - Subset of the synthetic handwritten digit 
                             dataset for training in form of cell array.
  Clustering, Feature extraction and Data dimension reduction
  Clustering is the process of training a neural network on patterns
  so that the network comes up with its own classifications according
  to pattern similarity and relative topology.  This is useful for gaining
  insight into data, or simplifying it before further processing.
   simplecluster_dataset - Simple clustering dataset.
  The inputs of fitting or pattern recognition datasets may also clustered.
  Input-Output Time-Series Prediction, Forecasting, Dynamic modeling
  Nonlinear autoregression, System identification and Filtering
  Input-output time series problems consist of predicting the next value
  of one time series given another time series. Past values of both series
  (for best accuracy), or only one of the series (for a simpler system)
  may be used to predict the target series.
   simpleseries_dataset  - Simple time series prediction dataset.
   simplenarx_dataset    - Simple time series prediction dataset.
   exchanger_dataset     - Heat exchanger dataset.
   maglev_dataset        - Magnetic levitation dataset.
   ph_dataset            - Solution PH dataset.
   pollution_dataset     - Pollution mortality dataset.
   refmodel_dataset      - Reference model dataset
   robotarm_dataset      - Robot arm dataset
   valve_dataset         - Valve fluid flow dataset.
  Single Time-Series Prediction, Forecasting, Dynamic modeling,
  Nonlinear autoregression, System identification, and Filtering
  Single time series prediction involves predicting the next value of
  a time series given its past values.
   simplenar_dataset     - Simple single series prediction dataset.
   chickenpox_dataset    - Monthly chickenpox instances dataset.
   ice_dataset           - Global ice volume dataset.
   laser_dataset         - Chaotic far-infrared laser dataset.
   oil_dataset           - Monthly oil price dataset.
   river_dataset         - River flow dataset.
   solar_dataset         - Sunspot activity dataset

Observe que todos los conjuntos de datos tienen nombres de archivo con el formato name_dataset. Dentro de estos archivos, estarán los arreglos nameInputs y nameTargets. Puede cargar un conjunto de datos en el espacio de trabajo con un comando como

load simplefit_dataset

De este modo, se cargarán simplefitInputs y simplefitTargets en el espacio de trabajo. Si desea cargar los arreglos de entrada y objetivo en distintos nombres, puede utilizar un comando como

[x,t] = simplefit_dataset;

De este modo, se cargarán las entradas y objetivos en los arreglos x y t. Puede obtener una descripción de un conjunto de datos con un comando como

help maglev_dataset

Consulte también

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