Keras4Delphi is a high-level neural networks API, written in Delphi's Object Pascal with Python Binding and capable of running on top of TensorFlow. Based on Keras.NET and Keras

Use Keras if you need a deep learning library that:

Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility). Supports both convolutional networks and recurrent networks, as well as combinations of the two. Runs seamlessly on CPU and GPU.

Keras4Delphi is using:

• python4delphi (thanks @pyscripter to the great work)
• NumPy4Delphi (Partial conversion)

Prerequisites

• Python 3 https://www.python.org/downloads/
• Install keras, numpy and the TensorFlow backend
• pip install keras numpy tensorflow
• Please see on how to configure: https://keras.io/backend/

Example with 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');

Output:

MNIST CNN Example

Python example taken from: 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('./');

Output

Reached 98% accuracy within 3 epoches.

Sentiment classification LSTM

Python example taken from: //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');

Notes

welcome collaborative testing and improvement of source code. I have little free time

TODO List

• Test code
• and Much more