An example for using the [TensorFlow.NET](https://github.com/SciSharp/TensorFlow.NET) and [NumSharp](https://github.com/SciSharp/NumSharp) for image recognition, it will use a pre-trained inception model to predict a image which outputs the categories sorted by probability. The original paper is [here](https://arxiv.org/pdf/1512.00567.pdf). The Inception architecture of GoogLeNet was designed to perform well even under strict constraints on memory and computational budget. The computational cost of Inception is also much lower than other performing successors. This has made it feasible to utilize Inception networks in big-data scenarios, where huge amount of data needed to be processed at reasonable cost or scenarios where memory or computational capacity is inherently limited, for example in mobile vision settings.

The GoogLeNet architecture conforms to below design principles:

* Avoid representational bottlenecks, especially early in the network.

* Higher dimensional representations are easier to process locally within a network.

* Spatial aggregation can be done over lower dimensional embeddings without much or any loss in representational power.

* Balance the width and depth of the network.

This example will download the dataset and uncompress it automatically. Some external paths are omitted, please refer to the source code for the real path.

private void PrepareData()

{

Directory.CreateDirectory(dir);

string url = "models/inception_v3_2016_08_28_frozen.pb.tar.gz";

string zipFile = Path.Join(dir, $"{pbFile}.tar.gz");

Utility.Web.Download(url, zipFile);

Utility.Compress.ExtractTGZ(zipFile, dir);

string pic = "grace_hopper.jpg";

Utility.Web.Download($"data/{pic}", Path.Join(dir, pic));

}

We need to load a sample image to test our pre-trained inception model. Convert it into tensor and normalized the input image. The pre-trained model takes input in the form of a 4-dimensional tensor with shape [BATCH_SIZE, INPUT_HEIGHT, INPUT_WEIGHT, 3] where:

- BATCH_SIZE allows for inference of multiple images in one pass through the graph

- INPUT_HEIGHT is the height of the images on which the model was trained

- INPUT_WEIGHT is the width of the images on which the model was trained

- 3 is the (R, G, B) values of the pixel colors represented as a float.

private NDArray ReadTensorFromImageFile(string file_name,

int input_height = 299,

int input_width = 299,

int input_mean = 0,

int input_std = 255)

{

return with<Graph, NDArray>(tf.Graph().as_default(), graph =>

{

var file_reader = tf.read_file(file_name, "file_reader");

var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");

var caster = tf.cast(image_reader, tf.float32);

var dims_expander = tf.expand_dims(caster, 0);

var resize = tf.constant(new int[] { input_height, input_width });

var bilinear = tf.image.resize_bilinear(dims_expander, resize);

var sub = tf.subtract(bilinear, new float[] { input_mean });

var normalized = tf.divide(sub, new float[] { input_std });

return with<Session, NDArray>(tf.Session(graph), sess => sess.run(normalized));

});

}

Load the pre-trained inception model which is saved as Google's protobuf file format. Construct a new graph then set input and output operations in a new session. After run the session, you will get a numpy-like ndarray which is provided by NumSharp. With NumSharp, you can easily perform various operations on multiple dimensional arrays in the .NET environment.

public void Run()

{

PrepareData();

var labels = File.ReadAllLines(Path.Join(dir, labelFile));

var nd = ReadTensorFromImageFile(Path.Join(dir, picFile),

input_height: input_height,

input_width: input_width,

input_mean: input_mean,

input_std: input_std);

var graph = Graph.ImportFromPB(Path.Join(dir, pbFile));

var input_operation = graph.get_operation_by_name(input_name);

var output_operation = graph.get_operation_by_name(output_name);

var results = with<Session, NDArray>(tf.Session(graph),

sess => sess.run(output_operation.outputs[0],

new FeedItem(input_operation.outputs[0], nd)));

results = np.squeeze(results);

var argsort = results.argsort<float>();

var top_k = argsort.Data<float>()

.Skip(results.size - 5)

.Reverse()

.ToArray();

foreach (float idx in top_k)

Console.WriteLine($"{picFile}: {idx} {labels[(int)idx]}, {results[(int)idx]}");

}

The best probability is `military uniform` which is 0.8343058. It's the correct classification.

You can find the full source code from [github](https://github.com/SciSharp/TensorFlow.NET/tree/master/test/TensorFlowNET.Examples).

ControlDependency

Gradient

Train

EagerMode

ImageRecognition

c_api.TF_GraphImportGraphDef(_handle, graph_def, opts, Status);

return Status;

}

public static Graph ImportFromPB(string file_path)

{

var graph = tf.Graph().as_default();

var graph_def = GraphDef.Parser.ParseFrom(File.ReadAllBytes(file_path));

importer.import_graph_def(graph_def);

return graph;

}

}

case float floatVal:

feed_dict_tensor[subfeed_t] = (NDArray)floatVal;

break;

case double doubleVal:

feed_dict_tensor[subfeed_t] = (NDArray)doubleVal;

break;

case int intVal:

feed_dict_tensor[subfeed_t] = (NDArray)intVal;

break;

feed_dict_tensor[subfeed_t] = (NDArray)bytes;

break;

default:

Console.WriteLine($"can't handle data type of subfeed_val");

throw new NotImplementedException("_run subfeed");

}

feed_map[subfeed_t.name] = (subfeed_t, subfeed_val);

<Description>Google's TensorFlow binding in .NET Standard.

Docs: https://tensorflownet.readthedocs.io</Description>

<AssemblyVersion>0.3.0.0</AssemblyVersion>

<PackageReleaseNotes>Added image prediction example.

Upgraded to TensorFlow 1.13 RC2.

</PackageReleaseNotes>

<LangVersion>7.2</LangVersion>

<FileVersion>0.3.0.0</FileVersion>

namespace TensorFlowNET.Examples

/// <summary>

/// Inception Architecture for Computer Vision

/// Port from tensorflow\examples\label_image\label_image.py

/// </summary>

public class InceptionArchGoogLeNet : Python, IExample

{

string dir = "label_image_data";

string pbFile = "inception_v3_2016_08_28_frozen.pb";

{

PrepareData();

var labels = File.ReadAllLines(Path.Join(dir, labelFile));

var nd = ReadTensorFromImageFile(Path.Join(dir, picFile),

input_height: input_height,

input_width: input_width,

input_mean: input_mean,

input_std: input_std);

var graph = Graph.ImportFromPB(Path.Join(dir, pbFile));

var input_operation = graph.get_operation_by_name(input_name);

var output_operation = graph.get_operation_by_name(output_name);

Console.WriteLine($"{picFile}: {idx} {labels[(int)idx]}, {results[(int)idx]}");

}

private NDArray ReadTensorFromImageFile(string file_name,

int input_height = 299,

int input_width = 299,

private void PrepareData()

{

Directory.CreateDirectory(dir);

// get model file