pigrecos / tensorflow.delphi Goto Github PK

function LinearRegression.Run(mmo1: TMemo): Boolean;
begin
    tf.compat.v1.disable_eager_execution;

    PrepareData;

    // tf Graph Input
    var X : TTensor := tf.placeholder(tf.float32_t);
    var Y : TTensor := tf.placeholder(tf.float32_t);

    // Set model weights
    // We can set a fixed init value in order to debug
    // var rnd1 = rng.randn<float>();
    // var rnd2 = rng.randn<float>();
    var W  := tf.Variable(Single(-0.06), 'weight');
    var b  := tf.Variable(Single(-0.73), 'bias');

    // Construct a linear model
    var pred : TTensor := tf.add(tf.multiply(X, W), b);
    //var pred1 := (X * W) + b;  OK

    // Mean squared error
    var cost := TTensor(tf.reduce_sum(tf.pow(pred - Y, 2.0)))  / (2.0 * n_samples) ;

    // Gradient descent
    // Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
    var optimizer := tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);

    // Initialize the variables (i.e. assign their default value)
    var init := tf.global_variables_initializer;

    // Start training
    var sess := tf.Session;
    // Run the initializer
    sess.run(init);

    // Fit all training data
    var epoch: Integer ;
    for epoch := 0 to training_epochs -1 do
    begin
        for var zItem in TUtils.zip<Single>(train_X, train_Y) do
        begin
            var v_x : Single := zItem.Value1 ;
            var v_y : Single := zItem.Value2 ;
            sess.run(optimizer, [ Tuple<TValue,TValue>.Create(X, v_x), Tuple<TValue,TValue>.Create(Y, v_y) ]);
        end;

        // Display logs per epoch step
        if ((epoch + 1) mod display_step) = 0 then
        begin
            var fc : Single := NDArray(sess.run(cost, [ Tuple<TValue,TValue>.Create(X, train_X), Tuple<TValue,TValue>.Create(Y, train_Y) ]));
            var fW : Single := NDArray(sess.run( TResourceVariable(W) ));
            var fb : Single := NDArray(sess.run( TResourceVariable(b) ));
            mmo1.Lines.Add( Format('Epoch: %d cost=%.9f + "W=%.9f b=%.9f"',[epoch + 1,fc, fW,fb]) );
        end;
    end;

    mmo1.Lines.Add('Optimization Finished!');
    var training_cost : Single := NDArray(sess.run(cost, [ Tuple<TValue,TValue>.Create(X, train_X), Tuple<TValue,TValue>.Create(Y, train_Y) ]));
    var fW            : Single := NDArray(sess.run(  TResourceVariable(W) ));
    var fb            : Single := NDArray(sess.run(  TResourceVariable(b) ));

    mmo1.Lines.Add('');
    mmo1.Lines.Add(Format('Training cost=%.9f W=%.9f b=%.9f',[training_cost, fW, fb]));

    // Testing example
    var test_X : NDArray := np.np_array( TArray<Single>.Create(6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1) );
    var test_Y : NDArray := np.np_array( TArray<Single>.Create(1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03) );

    mmo1.Lines.Add('Testing... (Mean square loss Comparison)');

    var t_cost                 := TTensor(tf.reduce_sum(tf.pow(pred - Y, 2.0)))  / (2.0 * test_X.shape[0]) ;
    var testing_cost : Single  := NDArray(sess.run(t_cost, [ Tuple<TValue,TValue>.Create(X, test_X), Tuple<TValue,TValue>.Create(Y, test_Y) ]));

    mmo1.Lines.Add('');
    mmo1.Lines.Add( Format('Testing cost=%.9f',[testing_cost]) );
    var diff := Abs( training_cost - testing_cost);
    mmo1.Lines.Add( Format('Absolute mean square loss difference: %.9f',[diff]) );
    mmo1.Lines.Add('');

    Result := diff < 0.01;

end;