//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');

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('./');

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');