TensorFlow.NET/test/TensorflowNET.Hub.Unittest/KerasLayerTest.cs

using static Tensorflow.Binding;
using static Tensorflow.HubAPI;

namespace Tensorflow.Hub.Unittest
{
    [TestClass]
    public class KerasLayerTest
    {
        [TestMethod]
        public void SmallBert()
        {
            var layer = hub.KerasLayer("https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-6_H-256_A-4/1");

            var input_type_ids = tf.convert_to_tensor(new int[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, dtype: tf.int32);
            input_type_ids = tf.reshape(input_type_ids, (1, 128));
            var input_word_ids = tf.convert_to_tensor(new int[] { 101, 2129, 2024, 2017,  102,    0,    0,    0,    0,    0,    0,
               0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
               0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
               0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
               0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
               0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
               0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
               0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
               0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
               0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
               0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,
               0,    0,    0,    0,    0,    0,    0 }, dtype: tf.int32);
            input_word_ids = tf.reshape(input_word_ids, (1, 128));
            var input_mask = tf.convert_to_tensor(new int[] { 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }, dtype: dtypes.int32);
            input_mask = tf.reshape(input_mask, (1, 128));

            var result = layer.Apply(new Tensors(input_type_ids, input_word_ids, input_mask));
        }

    }
}