using Tensorflow;

Add two constants:

var a = tf.constant(4.0f);

}

Feed placeholder:

var a = tf.placeholder(tf.float32);

using(var sess = tf.Session())

{

var o = sess.run(c, new FeedItem(a, 3.0f), new FeedItem(b, 2.0f));

}

Linear Regression:

var W = tf.Variable(-0.06f, name: "weight");

var b = tf.Variable(-0.73f, name: "bias");

var pred = tf.add(tf.multiply(X, W), b);

var cost = tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * n_samples);

var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);

var init = tf.global_variables_initializer();

with(tf.Session(), sess =>

{

sess.run(init);

for (int epoch = 0; epoch < training_epochs; epoch++)

{

foreach (var (x, y) in zip<float>(train_X, train_Y))

sess.run(optimizer, new FeedItem(X, x), new FeedItem(Y, y));

if ((epoch + 1) % display_step == 0)

{

var c = sess.run(cost, new FeedItem(X, train_X), new FeedItem(Y, train_Y));

Console.WriteLine($"Epoch: {epoch + 1} cost={c} " + $"W={sess.run(W)} b={sess.run(b)}");

}

Console.WriteLine("Optimization Finished!");

var training_cost = sess.run(cost, new FeedItem(X, train_X), new FeedItem(Y, train_Y));

Console.WriteLine($"Training cost={training_cost} W={sess.run(W)} b={sess.run(b)}");

var test_X = np.array(6.83f, 4.668f, 8.9f, 7.91f, 5.7f, 8.7f, 3.1f, 2.1f);

var test_Y = np.array(1.84f, 2.273f, 3.2f, 2.831f, 2.92f, 3.24f, 1.35f, 1.03f);

Console.WriteLine("Testing... (Mean square loss Comparison)");

var testing_cost = sess.run(tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * test_X.shape[0]), new FeedItem(X, test_X), new FeedItem(Y, test_Y));

Console.WriteLine($"Testing cost={testing_cost}");

var diff = Math.Abs((float)training_cost - (float)testing_cost);

Console.WriteLine($"Absolute mean square loss difference: {diff}");

}

});

Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflownet.readthedocs.io/en/latest/FrontCover.html).

* [Hello World](test/TensorFlowNET.Examples/HelloWorld.cs)

* [Basic Operations](test/TensorFlowNET.Examples/BasicOperations.cs)

* [Linear Regression](test/TensorFlowNET.Examples/LinearRegression.cs)

* [Logistic Regression](test/TensorFlowNET.Examples/LogisticRegression.cs)

* [Nearest Neighbor](test/TensorFlowNET.Examples/NearestNeighbor.cs)

* [Naive Bayes Classification](test/TensorFlowNET.Examples/NaiveBayesClassifier.cs)

* [Image Recognition](test/TensorFlowNET.Examples/ImageRecognition.cs)

* [K-means Clustering](test/TensorFlowNET.Examples/KMeansClustering.cs)

* [NN XOR](test/TensorFlowNET.Examples/NeuralNetXor.cs)

* [Object Detection](test/TensorFlowNET.Examples/ObjectDetection.cs)

* [Text Classification](test/TensorFlowNET.Examples/TextClassificationWithMovieReviews.cs)

* [CNN Text Classification](test/TensorFlowNET.Examples/CnnTextClassification.cs)

* [Named Entity Recognition](test/TensorFlowNET.Examples/NamedEntityRecognition.cs)

* Debug one of the unit tests that is marked as Ignored to get it to work (can be challenging)

* Debug one of the not yet working examples and get it to work (hard)

The best way to find out why a unit test is failing is to single step it in C# and its pendant Python at the same time to see where the flow of execution digresses or where variables exhibit different values. Good Python IDEs like PyCharm let you single step into the tensorflow library code.