Keras4Delphi 다운로드 Keras4Delphi 소스 코드 다운로드

Keras4Delphi

델파이 소스 코드

1.0.0

다운로드

Keras4delphi는 Python 바인딩이있는 Pascal (Delphi Rio 10.3)으로 작성되고 Tensorflow, CNTK 또는 Theano 위에서 실행할 수있는 고급 신경 네트워크 API입니다. Keras.net 및 Keras를 기반으로합니다

딥 러닝 라이브러리가 필요한 경우 keras를 사용하십시오.

쉽고 빠른 프로토 타이핑을 허용합니다 (사용자 친선 성, 모듈성 및 확장 성을 통해). Convolutional Network와 Reburrent Network와 두 가지 조합을 지원합니다. CPU 및 GPU에서 완벽하게 실행됩니다.

keras4delphi를 사용하고 있습니다.

Python4delphi (위대한 작품에 감사합니다)
Numpy4delphi (부분 변환)

전제 조건

Python 2.7-3.7, 링크 : https://www.python.org/downloads/
Keras, Numpy 및 백엔드 중 하나를 설치하십시오 (Tensorflow/CNTK/Theano). 구성 방법을 참조하십시오 : https://keras.io/backend/

XOR 샘플과 함께 예제

 // 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 ' );

산출:

MNIST CNN 예제

Python 예제 : 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( ' ./ ' );

산출

3 개의 에포크 내에서 98% 정확도에 도달했습니다.

감정 분류 lstm

python 예제 : //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 ' );