TensorFlow .NET（它是如何为开发人员工作的）

    // Load a pre-trained TensorFlow model
    var model = TensorFlowModel.LoadModel("model.pb");
    // Perform inference on input data
    var input = new float[,] { { 1.0f, 2.0f, 3.0f } };
    var output = model.Predict(input);

' Load a pre-trained TensorFlow model
	Dim model = TensorFlowModel.LoadModel("model.pb")
	' Perform inference on input data
	Dim input = New Single(, ) {
		{ 1.0F, 2.0F, 3.0F }
	}
	Dim output = model.Predict(input)

    // Create a neural network model using TensorFlow.NET APIs and metrics
    var input = new Input(Shape.Scalar);
    var output = new Dense(1, Activation.ReLU).Forward(input);
    // Compile the model with loss function and optimizer algorithms
    var model = new Model(input, output);
    model.Compile(optimizer: new SGD(), loss: Losses.MeanSquaredError);
    // Train the model with training data just like Python
    model.Fit(x_train, y_train, epochs: 10, batchSize: 32);

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    // Evaluate the trained model on test data
    var evaluation = model.Evaluate(x_test, y_test);
    // Export the trained model for deployment
    model.SaveModel("trained_model.pb");

IRON VB CONVERTER ERROR developers@ironsoftware.com

// static Tensorflow
using static Tensorflow.Binding;
namespace IronPdfDemos
{
    public class TensorFlow
    {
        public static void Execute()
        {
            var hello = tf.constant("Hello, TensorFlow!");
            Console.WriteLine(hello);
        }
    }
}

' static Tensorflow
Imports Tensorflow.Binding
Namespace IronPdfDemos
	Public Class TensorFlow
		Public Shared Sub Execute()
			Dim hello = tf.constant("Hello, TensorFlow!")
			Console.WriteLine(hello)
		End Sub
	End Class
End Namespace

using IronPdf;
using static Tensorflow.Binding;

namespace IronPdfDemos
{
    public class Program
    {
        public static void Main()
        {
            // Instantiate Cache and ChromePdfRenderer
            var renderer = new ChromePdfRenderer();

            // Prepare HTML
            var content = "<h1>Demonstrate TensorFlow with IronPDF</h1>";
            content += $"<p></p>";
            content += $"<h2></h2>";

            // Eager mode
            content += $"<h2>Enable Eager Execution</h2>";
            content += $"<p>tf.enable_eager_execution();</p>";

            // tf is a static TensorFlow instance
            tf.enable_eager_execution(); // Enable eager mode

            content += $"<h2>Define tensor constants.</h2>";
            // Introduce tensor constants
            var a = tf.constant(5);
            var b = tf.constant(6);
            var c = tf.constant(7);

            content += $"<p></p>";
            content += $"<p></p>";
            content += $"<p></p>";

            content += $"<h2>Various tensor operations.</h2>";
            // Various tensor operations usage
            // Note: Tensors also support operators (+, *, ...)
            var add = tf.add(a, b);
            var sub = tf.subtract(a, b);
            var mul = tf.multiply(a, b);
            var div = tf.divide(a, b);

            content += $"<p>var add = tf.add(a, b);</p>";
            content += $"<p>var sub = tf.subtract(a, b);</p>";
            content += $"<p>var mul = tf.multiply(a, b);</p>";
            content += $"<p>var div = tf.divide(a, b);</p>";

            content += $"<h2>Access tensors value.</h2>";
            // Tensors value
            print($"{(int)a} + {(int)b} = {(int)add}");
            print($"{(int)a} - {(int)b} = {(int)sub}");
            print($"{(int)a} * {(int)b} = {(int)mul}");
            print($"{(int)a} / {(int)b} = {(double)div}");

            content += $"<p>{(int)a} + {(int)b} = {(int)add}</p>";
            content += $"<p>{(int)a} - {(int)b} = {(int)sub}</p>";
            content += $"<p>{(int)a} * {(int)b} = {(int)mul}</p>";
            content += $"<p>{(int)a} / {(int)b} = {(double)div}</p>";

            // Some more operations
            var mean = tf.reduce_mean(tf.constant(new[] { a, b, c }));
            var sum = tf.reduce_sum(tf.constant(new[] { a, b, c }));

            content += $"<h2>Access tensors value.</h2>";
            // Access tensors value
            print("mean =", mean.numpy());
            print("sum =", sum.numpy());

            content += $"<p>mean = {mean.numpy()}</p>";
            content += $"<p>sum = {sum.numpy()}</p>";

            content += $"<h2>Matrix multiplications.</h2>";
            // Matrix multiplications with a single row
            var matrix1 = tf.constant(new float[,] { { 1, 2 }, { 3, 4 } });
            var matrix2 = tf.constant(new float[,] { { 5, 6 }, { 7, 8 } });
            var product = tf.matmul(matrix1, matrix2);

            content += "<p>matrix1 = tf.constant(new float[,] { { { 1, 2 }}, { { 3, 4 }} })</p>";
            content += "<p>matrix2 = tf.constant(new float[,] { { { 5, 6 }}, { { 7, 8 }} })</p>";
            content += "<p>product = tf.matmul(matrix1, matrix2)</p>";

            content += $"<h2>Convert Tensor to Numpy.</h2>";
            // Convert Tensor to Numpy
            print("product =", product.numpy());

            content += $"<p>product = {product.numpy()}</p>";

            // Create a PDF from an HTML string using C#
            var pdf = renderer.RenderHtmlAsPdf(content);
            // Export to a file or Stream
            pdf.SaveAs("tensorflow.pdf");
        }
    }
}

Imports IronPdf
Imports Tensorflow.Binding

Namespace IronPdfDemos
	Public Class Program
		Public Shared Sub Main()
			' Instantiate Cache and ChromePdfRenderer
			Dim renderer = New ChromePdfRenderer()

			' Prepare HTML
			Dim content = "<h1>Demonstrate TensorFlow with IronPDF</h1>"
			content &= $"<p></p>"
			content &= $"<h2></h2>"

			' Eager mode
			content &= $"<h2>Enable Eager Execution</h2>"
			content &= $"<p>tf.enable_eager_execution();</p>"

			' tf is a static TensorFlow instance
			tf.enable_eager_execution() ' Enable eager mode

			content &= $"<h2>Define tensor constants.</h2>"
			' Introduce tensor constants
			Dim a = tf.constant(5)
			Dim b = tf.constant(6)
			Dim c = tf.constant(7)

			content &= $"<p></p>"
			content &= $"<p></p>"
			content &= $"<p></p>"

			content &= $"<h2>Various tensor operations.</h2>"
			' Various tensor operations usage
			' Note: Tensors also support operators (+, *, ...)
			Dim add = tf.add(a, b)
			Dim [sub] = tf.subtract(a, b)
			Dim mul = tf.multiply(a, b)
			Dim div = tf.divide(a, b)

			content &= $"<p>var add = tf.add(a, b);</p>"
			content &= $"<p>var sub = tf.subtract(a, b);</p>"
			content &= $"<p>var mul = tf.multiply(a, b);</p>"
			content &= $"<p>var div = tf.divide(a, b);</p>"

			content &= $"<h2>Access tensors value.</h2>"
			' Tensors value
			print($"{CInt(a)} + {CInt(b)} = {CInt(add)}")
			print($"{CInt(a)} - {CInt(b)} = {CInt([sub])}")
			print($"{CInt(a)} * {CInt(b)} = {CInt(mul)}")
			print($"{CInt(a)} / {CInt(b)} = {CDbl(div)}")

			content &= $"<p>{CInt(a)} + {CInt(b)} = {CInt(add)}</p>"
			content &= $"<p>{CInt(a)} - {CInt(b)} = {CInt([sub])}</p>"
			content &= $"<p>{CInt(a)} * {CInt(b)} = {CInt(mul)}</p>"
			content &= $"<p>{CInt(a)} / {CInt(b)} = {CDbl(div)}</p>"

			' Some more operations
			Dim mean = tf.reduce_mean(tf.constant( { a, b, c }))
			Dim sum = tf.reduce_sum(tf.constant( { a, b, c }))

			content &= $"<h2>Access tensors value.</h2>"
			' Access tensors value
			print("mean =", mean.numpy())
			print("sum =", sum.numpy())

			content &= $"<p>mean = {mean.numpy()}</p>"
			content &= $"<p>sum = {sum.numpy()}</p>"

			content &= $"<h2>Matrix multiplications.</h2>"
			' Matrix multiplications with a single row
			Dim matrix1 = tf.constant(New Single(, ) {
				{ 1, 2 },
				{ 3, 4 }
			})
			Dim matrix2 = tf.constant(New Single(, ) {
				{ 5, 6 },
				{ 7, 8 }
			})
			Dim product = tf.matmul(matrix1, matrix2)

			content &= "<p>matrix1 = tf.constant(new float[,] { { { 1, 2 }}, { { 3, 4 }} })</p>"
			content &= "<p>matrix2 = tf.constant(new float[,] { { { 5, 6 }}, { { 7, 8 }} })</p>"
			content &= "<p>product = tf.matmul(matrix1, matrix2)</p>"

			content &= $"<h2>Convert Tensor to Numpy.</h2>"
			' Convert Tensor to Numpy
			print("product =", product.numpy())

			content &= $"<p>product = {product.numpy()}</p>"

			' Create a PDF from an HTML string using C#
			Dim pdf = renderer.RenderHtmlAsPdf(content)
			' Export to a file or Stream
			pdf.SaveAs("tensorflow.pdf")
		End Sub
	End Class
End Namespace

    using IronPdf; // This imports the IronPDF package, which is used for working with PDF files.
    using static Tensorflow.Binding; // This imports the TensorFlow library, specifically the .NET standard binding.

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    var add = tf.add(a, b); // Adds a and b.
    var sub = tf.subtract(a, b); // Subtracts b from a.
    var mul = tf.multiply(a, b); // Multiplies a and b.
    var div = tf.divide(a, b); // Divides a by b.

IRON VB CONVERTER ERROR developers@ironsoftware.com

    print($"{(int)a} + {(int)b} = {(int)add}");
    print($"{(int)a} - {(int)b} = {(int)sub}");
    print($"{(int)a} * {(int)b} = {(int)mul}");
    print($"{(int)a} / {(int)b} = {(double)div}");

IRON VB CONVERTER ERROR developers@ironsoftware.com