Tensorflow without python

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https://github.com/Pigrecos/TensorFlow.Delphi

 

TensorFlow.Delphi  provides a Delphi(Pascal)Standard binding for tensorflow It aims to implement the complete Tensorflow API in Delphi which allows Pascal developers to develop, train and deploy Machine Learning models with the Pascal Delphi(porting to free pascal in the future).

 

Note: This is a work-in-progress. please treat it as such.Pull request are welcome

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In progress.....

function TMnistGAN.Make_Generator_model: Model;
begin
    var mModel := TKerasApi.keras.Sequential(nil,'GENERATOR');

    mModel.OnEpochBegin      := On_Epoch_Begin;
    mModel.OnTrainBatchBegin := On_Train_Batch_Begin;
    mModel.OnEndSummary      := On_End_Summary;
    mModel.OnTestBatchBegin  := On_Train_Batch_Begin;

    mModel.Add( layers.Input(TFShape.Create([noise_dim])).first );
    mModel.Add( layers.Dense(7*7*256, {activation}nil ,{kernel_initializer}nil, {use_bias}False) );
    mModel.Add( layers.BatchNormalization);
    mModel.Add( layers.LeakyReLU);

    mModel.Add( layers.Reshape(TFShape.Create([7, 7, 256]))) ;
    Assert(mModel.OutputShape = TFShape.Create([-1, 7, 7, 256]));

    mModel.Add( layers.Conv2DTranspose(128, TFShape.Create([5, 5]), TFShape.Create([1, 1]), 'same', {data_format}'', {dilation_rate}nil, {activation}'relu', False));
    Assert(mModel.OutputShape = TFShape.Create([-1, 7, 7, 128]));
    mModel.Add( layers.BatchNormalization);
    mModel.Add( layers.LeakyReLU);

    mModel.Add( layers.Conv2DTranspose(64, TFShape.Create([5, 5]), TFShape.Create([2, 2]), 'same', {data_format}'', {dilation_rate}nil, {activation}'relu', False));
    Assert(mModel.OutputShape = TFShape.Create([-1, 14, 14, 64]));
    mModel.Add( layers.BatchNormalization);
    mModel.Add( layers.LeakyReLU);

    mModel.Add( layers.Conv2DTranspose(1, TFShape.Create([5, 5]), TFShape.Create([2, 2]), 'same', {data_format}'', {dilation_rate}nil, {activation}'tanh', False));
    Assert(mModel.OutputShape = TFShape.Create([-1, 28, 28, 1]));

    mModel.summary;
    Result := mModel;
end;

function TMnistGAN.Make_Discriminator_model: Model;
begin
    var model := TKerasApi.keras.Sequential(nil,'DISCRIMINATOR');

    model.OnEpochBegin      := On_Epoch_Begin;
    model.OnTrainBatchBegin := On_Train_Batch_Begin;
    model.OnEndSummary      := On_End_Summary;
    model.OnTestBatchBegin  := On_Train_Batch_Begin;

    model.Add( layers.Input(img_shape).first );
    model.add(layers.Conv2D(64, TFShape.Create([5, 5]), TFShape.Create([2, 2]), 'same'));
    model.add(layers.LeakyReLU);
    model.add(layers.Dropout(0.3));

    model.add(layers.Conv2D(128, TFShape.Create([5, 5]), TFShape.Create([2, 2]), 'same'));
    model.add(layers.LeakyReLU);
    model.add(layers.Dropout(0.3)) ;

    model.add(layers.Flatten) ;
    model.add(layers.Dense(1));

    model.summary;
    Result := model;
end;

 

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procedure LayersTest.TensorFlowOpLayer;
var
  mean   : TTensor;
  adv    : TTensor;
  value  : TFTensor;
  inputs : TFTensors;
  x      : TFTensors;
begin

    var l_layers := tf.keras.layers;
    inputs   := l_layers.Input( TFShape.Create([24]) );
    x        := l_layers.Dense(128, 'relu').Apply(inputs);
    value    := l_layers.Dense(24).Apply(x).first;
    adv      := l_layers.Dense(1).Apply(x).First;

    var aAxis : TAxis := 1;
    mean         := adv - tf.reduce_mean(adv, @aAxis, true);;
    adv          := l_layers.Subtract.Apply(TFTensors.Create([adv, mean])).first;
    var outputs  := l_layers.Add.Apply(TFTensors.Create([value, adv]));
    var model    := tf.keras.Model(inputs, outputs);

    model.OnEpochBegin      :=  On_Epoch_Begin;
    model.OnTrainBatchBegin :=  On_Train_Batch_Begin;
    model.OnEndSummary      :=  On_End_Summary;

    model.compile(tf.keras.optimizers.RMSprop(Single(0.001)), tf.keras.losses.MeanSquaredError, [ 'acc' ]);
    model.summary;

    Assert.AreEqual(model.Layers.Count, 8);

    var res := model.predict(TFTensors.Create( tf.constant(np.arange(24).astype(np.np_float32)[ [np.newaxis, Slice.All] ]) ));

    Assert.Istrue(res.shape= TFShape.Create([1, 24]));
    model.fit(np.arange(24).astype(np.np_float32)[[np.newaxis, Slice.All]], np.arange(24).astype(np.np_float32)[[np.newaxis, Slice.All]],{Batch_Size} -1,{Epochs} 1,{Verbose} 0);
end;

procedure TestXor;
var
  mModel : Sequential;
begin
    var x := np.np_array<Single>([ [ 0, 0 ], [ 0, 1 ], [ 1, 0 ], [ 1, 1 ] ],np.np_float32);
    var y := np.np_array<Single>([ [ 0 ],    [ 1 ],    [ 1 ],    [ 0 ] ],np.np_float32);

    mModel := TKerasApi.keras.Sequential;
    try
      mModel.OnEpochBegin      :=  On_Epoch_Begin;
      mModel.OnTrainBatchBegin :=  On_Train_Batch_Begin;
      mModel.OnEndSummary      :=  On_End_Summary;
      mModel.OnTestBatchEnd    :=  On_Epoch_Begin;

      mModel.add(tf.keras.Input(2));
      mModel.add(tf.keras.layers.Dense(32, tf.keras.activations.Relu));
      mModel.add(tf.keras.layers.Dense(64, tf.keras.activations.Relu));
      mModel.add(tf.keras.layers.Dense(1, tf.keras.activations.Sigmoid));
      mModel.compile(tf.keras.optimizers.Adam, tf.keras.losses.MeanSquaredError, ['accuracy']);
      mModel.fit(x, y, {batch_size}-1, {epochs}50, {verbose}1);
      var s := mModel.predict(TFTensors.Create(x), 4).tostring;
      frmMain.mmo1.Lines.Add(s);
    finally
     mModel.free;
    end;
end;

procedure TUnitTest_Basic.TFRandomSeedTest;
begin
    var initValue := np.arange(6).reshape(TFShape.create([3, 2]));
    tf.set_random_seed(1234);
    var a1 := tf.random_uniform(1);
    var b1 := tf.random_shuffle(tf.constant(initValue));

    // This part we consider to be a refresh
    tf.set_random_seed(10);
    tf.random_uniform(1);
    tf.random_shuffle(tf.constant(initValue));

    tf.set_random_seed(1234);
    var a2 := tf.random_uniform(1);
    var b2 := tf.random_shuffle(tf.constant(initValue));
    Assert.IsTrue(a1.numpy.Equals(a2.numpy));
    Assert.IsTrue(b1.numpy.Equals(b2.numpy));
end;

And Other...

[Reidinga 0]

Very nice! I have build several models with yolov5 for object detection. Would it be possible to use these models with this unit? 

[Kryvich 163]

I wonder if it is possible to create a similar binding for the PyTorch library? To use it directly from Delphi without installing Python.