pigrecos / keras4delphi

Keras4Delphi is a high-level neural networks API, written in Pascal with Python Binding

Keras4Delphi is a high-level neural networks API, written in Pascal(Delphi Rio 10.3) with Python Binding and capable of running on top of TensorFlow, CNTK, or Theano. 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)

Prerequisite

• Python 2.7 - 3.7, Link: https://www.python.org/downloads/
• Install keras, numpy and one of the backend (Tensorflow/CNTK/Theano). 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

keras4delphi's Issues

Problem compiling Keras4Delphi.exe

Hello everyone.

I think I have done everthing correct, but I dont know why, when I try to compile Keras4Delphi.exe I receive this:

[dcc32 Error] Python.Utils.pas(836): E2003 Undeclared identifier: 'PyDict_Update'
[dcc32 Error] Python.Utils.pas(933): E2003 Undeclared identifier: 'PyLong_FromUnsignedLongLong'
[dcc32 Error] Python.Utils.pas(1132): E2003 Undeclared identifier: 'PyFloat_FromString'
[dcc32 Error] Python.Utils.pas(1362): E2003 Undeclared identifier: 'PySequence_List'
[dcc32 Fatal Error] untMain.pas(9): F2063 Could not compile used unit 'Python.Utils.pas'

Every undeclared identifier are supossed to be part of TPythonEngine, but I cannot find any of this in PythonEngine.pas, where its defined.

Anyone can help me?

Thank you!

Keras4Delphi on GPU?

Hi,

You mentioned that Keras4Delphi works also on GPU. I've been busy for several days to get it working for my GPU (GeForce GTX 4070), but I don't succeed. What procedure must one follow to make it work? In the best case my GPU is listed in cmd.exe: python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))". But running Keras4Delphi in Delphi 10.4 results in a program halt.

Thanks.

Python.Utils.pas: error build with new Python4Dephi

Hello,
It's not possible to build with last Python4Delphi.
Reply by pyscripter Mar 27/04/2021 11:23
PythonEngine.pas dropped support for python 2.x. Many methods have been removed and others replaced. All the PyString_ and PyInt_ methods have been removed. Use PyUnicode_ and PyLong_ instead.
Could you update the file python.utils.pas ?
Thanks