public void perceptron_should_learn_all_except_xor_nor(
[ValueSource("Backends")] string backend,
[ValueSource("Targets")] float[] y,
[Values(false, true)] bool useBias)
{
KerasSharp.Backends.Current.Switch(backend);
var model = new Sequential();
model.Add(new Dense(1, input_dim: 2,
kernel_initializer: new GlorotUniform(),
bias_initializer: new GlorotUniform(),
use_bias: useBias,
activation: new Sigmoid()));
model.Compile(loss: new MeanSquareError(), optimizer: new SGD(lr: 1), metrics: new[] { new Accuracy() });
model.fit(x, y, epochs: 1000, batch_size: y.Length);
Array yy = model.predict(x, batch_size: y.Length)[0];
float[] pred = MatrixEx.Round(yy.To <float[, ]>()).GetColumn(0);
if ((useBias && (y == xor)) ||
(!useBias && (y == xor || y == nor || y == and)))
{
Assert.AreNotEqual(y, pred);
}
else
{
Assert.AreEqual(y, pred);
}
}