diff --git a/test/TensorFlowNET.Keras.UnitTest/Layers/ActivationTest.cs b/test/TensorFlowNET.Keras.UnitTest/Layers/ActivationTest.cs
index 75fcc023..cc99f4a0 100644
--- a/test/TensorFlowNET.Keras.UnitTest/Layers/ActivationTest.cs
+++ b/test/TensorFlowNET.Keras.UnitTest/Layers/ActivationTest.cs
@@ -49,7 +49,7 @@ namespace Tensorflow.Keras.UnitTest.Layers
             Tensor input = tf.constant(new float[] { -3f, -2f, -1f, 0f, 1f, 2f });
             Tensor output = keras.layers.Softplus().Apply(input);
             NDArray expected = new NDArray(new float[] { 0.04858733f, 0.12692805f, 0.31326166f, 0.6931472f, 1.3132616f, 2.126928f });
-            Assert.AreEqual(expected, output.numpy());
+            Assert.IsTrue(expected == output.numpy());
         }
 
         [TestMethod]
diff --git a/test/TensorFlowNET.Keras.UnitTest/Layers/AttentionTest.cs b/test/TensorFlowNET.Keras.UnitTest/Layers/AttentionTest.cs
index 162a10d2..95ef923e 100644
--- a/test/TensorFlowNET.Keras.UnitTest/Layers/AttentionTest.cs
+++ b/test/TensorFlowNET.Keras.UnitTest/Layers/AttentionTest.cs
@@ -94,7 +94,7 @@ namespace Tensorflow.Keras.UnitTest.Layers
                 { 7.6400003f, 12.24f, 16.84f },
                 { 14.24f, 22.84f, 31.439999f }
             } }, dtype: np.float32);
-            Assert.AreEqual(expected, actual.numpy());
+            Assert.IsTrue(expected == actual.numpy());
         }
 
         [TestMethod]
diff --git a/test/TensorFlowNET.Keras.UnitTest/Losses/LossesTest.cs b/test/TensorFlowNET.Keras.UnitTest/Losses/LossesTest.cs
index 3bec2f17..0bb1d011 100644
--- a/test/TensorFlowNET.Keras.UnitTest/Losses/LossesTest.cs
+++ b/test/TensorFlowNET.Keras.UnitTest/Losses/LossesTest.cs
@@ -39,7 +39,7 @@ public class LossesTest : EagerModeTestBase
         // Using 'none' reduction type.
         bce = tf.keras.losses.BinaryCrossentropy(from_logits: true, reduction: Reduction.NONE);
         loss = bce.Call(y_true, y_pred);
-        Assert.AreEqual(new float[] { 0.23515666f, 1.4957594f }, loss.numpy());
+        Assert.IsTrue(new NDArray(new float[] { 0.23515666f, 1.4957594f }) == loss.numpy());
     }
 
     /// <summary>