/// <summary>

        /// 构建张量

        /// </summary>

        private void Base_Constant()

        {

            //通过基本类型构建张量

            var c1 = tf.constant(3); // int

            var c2 = tf.constant(1.0f); // float

            var c3 = tf.constant(2.0); // double

            var c4 = tf.constant("Hello Tensorflow.Net!"); // string

            Console.WriteLine(c1);

            Console.WriteLine(c2);

            Console.WriteLine(c3);

            Console.WriteLine(c4);

            //通过多维数值构建张量

            int[,] arr = new int[,] { { 1, 2, 3 }, { 4, 5, 6 } };

            var nd = np.array(arr);

            var tensor = tf.constant(nd);

            Console.WriteLine(tensor);

            //构建全0或全1张量

            var tensor0 = tf.constant(np.zeros(new Shape(2, 3)));

            var tensor1 = tf.constant(np.ones(new Shape(2, 3)));

            Console.WriteLine(tensor0);

            Console.WriteLine(tensor1);

            var tensor_0 = tf.zeros(new Shape(2, 3));

            var tensor_1 = tf.ones(new Shape(2, 3));

            Console.WriteLine(tensor_0);

            Console.WriteLine(tensor_1);

        }

        /// <summary>

        /// 张量运算

        /// </summary>

        private void Base_Operator()

        {

            var a = tf.constant(2.0f);

            var b = tf.constant(3.0f);

            var c = tf.constant(5.0f);

            // 基本运算，可以采+ - * / 等运算符

            var add = tf.add(a, b);

            var sub = tf.subtract(a, b);

            var mul = tf.multiply(a, b);

            var div = tf.divide(a, b);

            print($"{(float)a} + {(float)b} = {(float)add}");

            print($"{(float)a} - {(float)b} = {(float)sub}");

            print($"{(float)a} * {(float)b} = {(float)mul}");

            print($"{(float)a} / {(float)b} = {(float)div}");

            // 求平均、求和

            var mean = tf.reduce_mean(tf.constant(new[] { a, b, c }));

            var sum = tf.reduce_sum(tf.constant(new[] { a, b, c }));

            print("mean =", mean.numpy());

            print("sum =", sum.numpy());

            // 矩阵相乘

            var matrix1 = tf.constant(new float[,] { { 1, 2, 3 }, { 3, 4, 5 } });

            var matrix2 = tf.constant(new float[,] { { 3, 4 }, { 5, 6 }, { 7, 8 } });

            var product1 = tf.matmul(matrix1, matrix2);

            print("product1 =", product1.numpy());

        }

        /// <summary>

        /// 生成随机数张量

        /// </summary>

        private void Base_Random()

        {

            var t1 = tf.random.normal(new Shape(10));

            var t2 = tf.random.uniform(new Shape(2, 5));

            var t3 = tf.random.uniform(new Shape(2, 5), 1, 100);

            Console.WriteLine($"t1={t1.numpy()}");

            Console.WriteLine($"t2={t2.numpy()}");

            Console.WriteLine($"t3={t3.numpy()}");

            t1 = tf.random.normal(new Shape(100), mean: 0.5f, stddev: 2);

            var mean = tf.reduce_mean(t1);

            var max = tf.reduce_max(t1);

            var min = tf.reduce_min(t1);

            Console.WriteLine($"mean={mean.numpy()},max={max.numpy()},min={min.numpy()}");

        }