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

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

namespace TensorFlowNET.Keras.UnitTest {
      [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);
            }

      }
}