Beispieldatensätze für flache neuronale Netze

Die Deep Learning Toolbox™ enthält eine Reihe von Beispieldatensätzen, die Sie zum Experimentieren mit flachen neuronalen Netzen verwenden können. Geben Sie zum Anzeigen der verfügbaren Datensätze den folgenden Befehl ein:

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

Beachten Sie, dass alle Datensätze Dateinamen nach dem Schema name_dataset aufweisen. In diesen Dateien befinden sich die Arrays nameInputs und nameTargets. Sie können einen Datensatz in den Arbeitsbereich laden, indem Sie beispielsweise folgenden Befehl eingeben:

load simplefit_dataset

Mit diesem Befehl werden simplefitInputs und simplefitTargets in den Arbeitsbereich geladen. Wenn Sie die Eingangs- und Zielarrays in verschiedene Namen laden möchten, können Sie beispielsweise folgenden Befehl verwenden:

[x,t] = simplefit_dataset;

Mit diesem Befehl werden die Eingänge und Ziele in die Arrays x und t geladen. Die Beschreibung eines Datensatzes können Sie beispielsweise mit folgendem Befehl abrufen:

help maglev_dataset

Siehe auch

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