Keras4Delphi Download - Keras4Delphi Quellcode Download

Keras4Delphi

Delphi-Quellcode

1.0.0

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Keras4Delphi ist eine hochrangige neuronale Netzwerke-API, die in Pascal (Delphi Rio 10.3) mit Python-Bindung geschrieben und in der Lage ist, auf Tensorflow, CNTK oder Theano zu laufen. Basierend auf Keras.net und Keras

Verwenden Sie Keras, wenn Sie eine Deep -Learning -Bibliothek benötigen, die:

Ermöglicht einfache und schnelle Prototypen (durch Benutzerfreundlichkeit, Modularität und Erweiterbarkeit). Unterstützt sowohl Faltungsnetzwerke als auch wiederkehrende Netzwerke sowie Kombinationen der beiden. Läuft nahtlos auf CPU und GPU.

Keras4Delphi verwendet:

python4delphi (danke @pyscripter an die großartige Arbeit)
Numpy4Delphi (Teilumwandlung)

Voraussetzung

Python 2.7 - 3.7, Link: https://www.python.org/downloads/
Installieren Sie Keras, Numpy und einen der Backend (TensorFlow/CNTK/Theano). Weitere Informationen finden Sie unter: https://keras.io/backend/

Beispiel mit xor sample

 // Load train data
var x : TNDarray := TNumPy.npArray<Double>( [ [ 0 , 0 ], [ 0 , 1 ], [ 1 , 0 ], [ 1 , 1 ] ] );
var y : TNDarray := TNumPy.npArray<Double>( [ 0 , 1 , 1 , 0 ] );

// Build functional model
var input  : TKInput := TKInput.Create(tnp_shape.Create([ 2 ]));
var hidden1: TBaseLayer  := TDense.Create( 32 , ' relu ' ).& Set ([input]);
var hidden2: TBaseLayer  := TDense.Create( 64 , ' relu ' ).& Set ([hidden1]);
var output : TBaseLayer  := TDense.Create( 1 ,  ' sigmoid ' ).& Set ([hidden2]);

var model : TModel := TModel.Create ( [ input ] , [ output ]);

// Compile and train
model.Compile(TStringOrInstance.Create( TAdam.Create ), ' binary_crossentropy ' ,[ ' accuracy ' ]);

var batch_size : Integer := 2 ;
var history: THistory := model.Fit(x, y, @batch_size, 10 , 1 );

model.Summary;

var logs := history.HistoryLogs;

// Save model and weights
var json : string := model.ToJson;
TFile.WriteAllText( ' model.json ' , json);
model.SaveWeight( ' model.h5 ' );

// Load model and weight
var loaded_model : TBaseModel := TSequential.ModelFromJson(TFile.ReadAllText( ' model.json ' ));
loaded_model.LoadWeight( ' model.h5 ' );

Ausgabe:

MNIST CNN Beispiel

Python -Beispiel aus: https://keras.io/examples/mnist_cnn/

 var
  res      : TArray<TNDArray>;
begin
    var batch_size : Integer := 128 ; // Training batch size
    var num_classes: Integer := 10 ;  // No. of classes
    var epochs     : Integer := 12 ;  // No. of epoches we will train

    // input image dimensions
    var img_rows: Integer := 28 ;
    var img_cols: Integer := 28 ;

    // Declare the input shape for the network
    var input_shape : Tnp_shape := default(Tnp_shape);

    // Load the MNIST dataset into Numpy array
    res := TMNIST.load_data;
    var x_train, y_train ,x_test, y_test : TNDArray;

    x_train := res[ 0 ];
    y_train := res[ 1 ];
    x_test  := res[ 2 ];
    y_test  := res[ 3 ];

    // Check if its channel fist or last and rearrange the dataset accordingly
    var K: TBackend := TBackend.Create;
    if (K.ImageDataFormat = ' channels_first ' ) then
    begin
        x_train := x_train.reshape([x_train.shape[ 0 ], 1 , img_rows, img_cols]);
        x_test  := x_test.reshape ([x_test.shape[ 0 ] , 1 , img_rows, img_cols]);
        input_shape := Tnp_shape.Create([ 1 , img_rows, img_cols]);
    end else
    begin
        x_train := x_train.reshape([x_train.shape[ 0 ], img_rows, img_cols, 1 ]);
        x_test  := x_test.reshape ([x_test.shape[ 0 ] , img_rows, img_cols, 1 ]);
        input_shape := Tnp_shape.Create([img_rows, img_cols, 1 ]);
    end ;

    // Normalize the input data
    x_train := x_train.astype(vNumpy.float32_);
    x_test  := x_test.astype(vNumpy.float32_);
    x_train := TNDArray.opDiv(x_train, 255 );
    x_test  := TNDArray.opDiv(x_test, 255 );

    redtOutput.Lines.Add( ' x_train shape: ' + x_train.shape.ToString);
    redtOutput.Lines.Add( IntToStr(x_train.shape[ 0 ]) + ' train samples ' );
    redtOutput.Lines.Add( IntToStr(x_test.shape[ 0 ]) + '  test  samples ' );

    // Convert class vectors to binary class matrices
    var Util : TUtil := TUtil.Create;
    y_train := Util.ToCategorical(y_train, @num_classes);
    y_test  := Util.ToCategorical(y_test, @num_classes);

    // Build CNN model
    var model : TSequential := TSequential.Create;

    model.Add( TConv2D.Create( 32 , [ 3 , 3 ], ' relu ' , @input_shape) );
    model.Add( TConv2D.Create( 64 , [ 3 , 3 ], ' relu ' ));
    model.Add( TMaxPooling2D.Create([ 2 , 2 ]));
    model.Add( TDropout.Create( 0.25 ));
    model.Add( TFlatten.Create);
    model.Add( TDense.Create( 128 , ' relu ' ));
    model.Add( TDropout.Create( 0.5 ));
    model.Add( TDense.Create(num_classes, ' softmax ' ));

    // Compile with loss, metrics and optimizer
    model.Compile(TStringOrInstance.Create(TAdadelta.Create), ' categorical_crossentropy ' , [ ' accuracy ' ]);

    // Train the model
    model.Fit(x_train, y_train, @batch_size, epochs, 1 , nil , 0 ,[ x_test, y_test ]);

    // Score the model for performance
    var score : TArray<Double> := model.Evaluate(x_test, y_test, nil , 0 );

    redtOutput.Lines.Add( ' Test loss: '   + FloatToStr(score[ 0 ]));
    redtOutput.Lines.Add( ' Test accuracy: ' + FloatToStr(score[ 1 ]));

    // Save the model to HDF5 format which can be loaded later or ported to other application
    model.Save( ' model.h5 ' );
    // Save it to Tensorflow JS format and we will test it in browser.
    model.SaveTensorflowJSFormat( ' ./ ' );

Ausgabe

Erreichte 98% Genauigkeit innerhalb von 3 Epochen.

Stimmungsklassifizierung LSTM

Python -Beispiel von: //https://keras.io/examples/imdb_lstm/

 var
  res      : TArray<TNDArray>;
begin
    var max_features: Integer := 20000 ;
    // cut texts after this number of words (among top max_features most common words)
    var maxlen     : Integer := 80 ;
    var batch_size : Integer := 32 ;

    redtOutput.Lines.Add( ' Loading data... ' );
    res := TIMDB.load_data(@max_features);
    var x_train, y_train ,x_test, y_test,X,Y,tmp : TNDArray;

    x_train := res[ 0 ];
    y_train := res[ 1 ];
    x_test  := res[ 2 ];
    y_test  := res[ 3 ];

    redtOutput.Lines.Add( ' train sequences: ' + x_train.shape.ToString);
    redtOutput.Lines.Add( ' test sequences: '  + x_test.shape.ToString);

    redtOutput.Lines.Add( ' Pad sequences (samples x time) ' );
    var tseq : TSequenceUtil := TSequenceUtil.Create;
    x_train := tseq.PadSequences(x_train, @maxlen);
    x_test  := tseq.PadSequences(x_test,  @maxlen);
    redtOutput.Lines.Add( ' x_train shape: ' + x_train.shape.ToString);
    redtOutput.Lines.Add( ' x_test shape: '  + x_test.shape.ToString);

    redtOutput.Lines.Add( ' Build model... ' );
    var model : TSequential := TSequential.Create;
    model.Add( TEmbedding.Create(max_features, 128 ));
    model.Add( TLSTM.Create( 128 , 0.2 , 0.2 ));
    model.Add( TDense.Create( 1 , ' sigmoid ' ));

    // try using different optimizers and different optimizer configs
    model.Compile(TStringOrInstance.Create( ' adam ' ), ' binary_crossentropy ' , [ ' accuracy ' ]);
    model.Summary;

    redtOutput.Lines.Add( ' Train... ' );
    model.Fit(x_train, y_train, @batch_size, 15 , 1 ,[ x_test, y_test ]);

    // Score the model for performance
    var score : TArray<Double> := model.Evaluate(x_test, y_test, @batch_size);

    redtOutput.Lines.Add( ' Test score: '   + FloatToStr(score[ 0 ]));
    redtOutput.Lines.Add( ' Test accuracy: ' + FloatToStr(score[ 1 ]));

    // Save the model to HDF5 format which can be loaded later or ported to other application
    model.Save( ' model.h5 ' );