TensorFlow.NET/test/TensorFlowNET.Keras.UnitTest/Layers/Layers.Reshaping.Test.cs

using Microsoft.VisualStudio.TestTools.UnitTesting;
using Tensorflow.NumPy;
using static Tensorflow.Binding;
using static Tensorflow.KerasApi;

namespace Tensorflow.Keras.UnitTest.Layers
{
    [TestClass]
    public class LayersReshapingTest : EagerModeTestBase
    {
        [TestMethod]
        public void ZeroPadding2D()
        {
            Shape input_shape = (1, 1, 2, 2);
            var x = np.arange(input_shape.size).reshape(input_shape);
            var zero_padding_2d = keras.layers.ZeroPadding2D(new[,] { { 1, 0 }, { 1, 0 } });
            var y = zero_padding_2d.Apply(x);
            Assert.AreEqual((1, 2, 3, 2), y.shape);
        }

        [TestMethod]
        public void UpSampling2D()
        {
            Shape input_shape = (2, 2, 1, 3);
            var x = np.arange(input_shape.size).reshape(input_shape);
            var y = keras.layers.UpSampling2D(size: (1, 2)).Apply(x);
            Assert.AreEqual((2, 2, 2, 3), y.shape);
        }

        [TestMethod]
        public void Reshape()
        {
            var inputs = tf.zeros((10, 5, 20));
            var outputs = keras.layers.LeakyReLU().Apply(inputs);
            outputs = keras.layers.Reshape((20, 5)).Apply(outputs);
            Assert.AreEqual((10, 20, 5), outputs.shape);
        }

        [TestMethod]
        public void Permute()
        {
            var inputs = tf.zeros((2, 3, 4, 5));
            var outputs = keras.layers.Permute(new int[] { 3, 2, 1 }).Apply(inputs);
            Assert.AreEqual((2, 5, 4, 3), outputs.shape);
        }

    }
}