Yesterday at Ignite conference .NET team has announced the Jupyter Notebook for .NET languages C# and F#. This is a huge step ahead for all data scientists who want to do data science and machine learning on the .NET platform. With C# Jupyter Notebook you can perform data exploration and transformation, training, evaluation and testing your Ml models. All operations are performed by code block and you can quickly see the result without running and debugging application every time you want to change something. In order to see how it looks like, in this blog post we are going to explore some of the basic functionalities in C# Jupyter Notebook.
How to Install .NET Jupyter Notebook
In order to install Jupyter Notebook you can see the official blog post, anyhow here I am going to present this process because it is very short and easy. Before install .NET Jupyter components, you have to install the latest version of .NET SDK and Anaconda. Once you have Anaconda installed on your machine, open Anaconda Prompt from Windows Start Menu.
To run Anaconda Prompt you have two options:
to open power shell or
to open classic command prompt.
Select Anaconda Powershell Prompt, and the powershell window will pop up. Once the powershell prompt is opened we can start with the installation of Jupyter Notebook components. The first step is to install the dotnet try global tool.
Type this to cmd:
dotnet tool install -g dotnet-try
After some time you should get the following message:
Then we need to install .NET Kernel by typing the following command:
Also note that this version of Jupyter Notebook is in preview, so not all actions will work as you expected.
Now that you have installed C# Jupyter, you can open Jupyter notebook from the Anaconda navigator, or just type Jupyter Notebook in to Anaconda Prompt. Once we did that, your default bowers pops up and shows the starting directory in the Jupyter Notebook. If you click New button, you can see option to create C# and F# notebooks. Press C#, and the new C# notebook will appeared in the browser.
Try some basic stuff in notebook.
In the next blog post we are going to explore more and see some of the coolest features in C# Jupyter Notebook.
With ANNdotNET 1.2 the user is able to create and train deep learning models for image classification. Image classification module provides minimum of GUI actions in order to fully prepare data set. In this post, we are going to create and train deep learning model for CIFAR-10 data set, and see how it easy to do that with ANNdotNET v1.2.
In order to prepare data we have to download CIFAR-10 data set from official web site . The CIFAR-10 data set is provided in 6 binary batch files that should be extracted and persisted on your local machine. Number 10 in the name means that data set is created for 10 labels.The following image shows 10 labels of CIFAR-10 data set each label with few sample images.
The data set contains 60 000 (50 000 for training and validation, and 10 000 for test) tinny colored images dimensions of 32×32. There is also bigger version of the data set CIFAR-100 with 100 labels. Our task is to create deep learning model capable of recognizing only one of 10 predefined labels from each image.
In order to prepare images, we need to do the following:
decompress the file and store each image in 10 different folders that are represent image label.
each folder contains images for one label.
The following image shows extracted data set persisted in 10 label folders. The bird folder is opened and shows all images labeled for bird. The test folder contains all images created for testing the model once the model is trained.
In order to properly save all images, we need to create simple C# Console application which should extract and save all 60 000 images. Complete C# program can be downloaded from here.
In order to successfully extract the images, we have to see how those images are stored in binary files. From the official site we can see that there are 5 for training and 1 for test binary files: data_batch_1.bin, data_batch_2.bin, …, data_batch_5.bin, as well as test_batch.bin.
Each of these files is formatted as follows so that the first byte of the array is label index, and the next 3072 bytes represent the image. Each batch contains 10 000 images.
Important to know is that images are stored in CHW format which means that 1d image array is created so that the first 1024 bytes are the red channel values, the next 1024 the green, and the final 1024 the blue. The values are stored in row-major order, so the first 32 bytes are the red channel values of the first row of the image. To end this, all those information have been carried out when implementing the Extractor application. The most important methods are reshaping the 1D byte array into [3, height, width] image tensor, and creating the image from the byte tensor. The following implementation shows how 1D byte array is transformed into 3channel bitmap tensor.
static int reshape(int channel, int height, int width, byte img)
var data = new int[channel];
int counter = 0;
for(int c = 0; c < channel; c++)
data[c] = new int[height];
for (int y = 0; y < height; y++)
data[c][y] = new int[width];
for (int x = 0; x < width; x++)
data[c][y][x] = img[counter];
Once the 1D byte array is transformed into tensor, the image can be created and persisted on disk. The following method iterates through all 10000 images in one batch file, extract them and persist on disk.
public static void extractandSave(byte batch, string destImgFolder, ref int imgCounter)
var nStep = 3073;//1 for label and 3072 for image
for (int i = 0; i < batch.Length; i += nStep)
var l = (int)batch[i];
var img = new ArraySegment<byte>(batch, i + 1, nStep - 1).ToArray();
// data in CIFAR-10 dataset is in CHW format, which means CHW: RR...R, GG..G, BB..B;
// while HWC: RGB, RGB, ... RGB
var reshaped = reshape(3, 32, 32, img);
var image = ArrayToImg(reshaped);
//check if folder exist
var currentFolder = destImgFolder + classNames[l];
//save image to specified folder
image.Save(currentFolder + "\\" + imgCounter.ToString() + ".png");
Run Cifar-Extractor console application and the process of downloading, extracting and saving images will be finished in few minutes. The most important is that CIFAR-10 data set will be stored in c://sc/datasets/cifar-10 path. This is important later, when we create image classifier.
Now that we have 60000 tiny images on disk arranged by labels we can start creating deep learning model.
Create new image classification project file in ANNdotNET
Open the latest ANNdotNET v1.2 and select New-> Image Classification project. Enter CIFAR project name and press save button. The following image shows CIFAR new ann-project:
Once we have new project, we can start defining image labels by pressing Add button. For each 10 labels we need to add new label item in the list. In each item the following fields should be defined:
Path to images with the label.
Query – in case we need to get all images within the specified path with certain part of the name. In case all images withing the specified path are images that indicate one label, query should be empty string.
Beside Label item, image transformation should be defined in order to define the size of the images, as well as how many images create validation/test data set.
Assuming the CIFAR-10 data set is extracted at c:/sc/datasets/cifar-10 folder, the following image shows how label items should be defined:
In case label item should be removed from the list, this is done by selecting the item, and then pressing Remove button. Beside image properties, we should defined how many images belong to validation data set. As can be seen 20% of all extracted images will be created validation data set. Notice that images from the test folder are not part of those two data set. they will be used for testing phase once the model is trained. Now that we done with data preparation we can move to the next step: creating mlconifg file.
Create mlconfig in ANNdotNET
By selecting New MLConfig command the new mlconfig file is created within the project explorer. Moreover by pressing F2 key on selected mlconfig tree item, we can easily change the name into “CIRAF-10-ConvNet”. The reason why we gave such name is because we are going to use convolution neural networks.
In order to define mlconfig file we need to define the following:
Network configuration using Visual Network Designer
Define Learning parameters
Define training parameters
Create Network configuration
By using Visual Network Designer (VND) we can quickly create network model. For this CIFAR-10 data set we are going to create 11 layers model with 4 Constitutional, 2 Pooling, 1 DropOut and 3 Dense layer, all followed by Scale layer:
This network can be created so that we select appropriate layer from the VND combo box and click on Add button. The first layer is Scale layer, since we need to normalize the input values to be in interval (0,1). Then we created two sequence of Convolution, Pooling layers. Once we done with that, we can add two Dense layers with 64 and 32 neurons with TanH activation function. The last layer is output layer that must follow the output dimension, and Softmax activation function.
Once network model is defined, we can move to the next step: Setting learning and training parameters.
Learning parameters can be defined through the Learning parameters interface: For this model we can select:
AdamLearner with 0.005 rate and 0.9 momentum value. Loss function is Classification Error, and the evaluation function is Classification Accuracy
In order to define the training parameters we switch to Training tab page and setup:
Number of epoch
Randomize minibatch during training
Now we have enough information to start model training. The training process is started by selecting Run command from the application ribbon. In order to get good model we need to train the model at least few thousands epoch. The following image shows trained model with training history charts.
The model is trained with exactly of 4071 epochs, with network parameters mentioned above. As can be seen from the upper chart, mini-batch loss function was CrossEntropyWithSoftmax, while the evaluation function was classification accuracy. The bottom chart shows performance of the training and validation data sets for each 4071 epoch. We can also recognize that validation data set has roughly the same accuracy as training data set which indicates the model is trained well. More details about model performance can be seen on the next image:
Upper charts of the image above show actual and predicted values for training (left) and validation (right). Most of the point values are blue and overlap the orange which indicates that most of value are correctly predicted. The charts can be zoomed and view details of each value.The bottom part of the evaluation show performance parameters of the model for corresponded data set. As can be seen the trained model has 0.91 overall accuracy for training data set and 0.826 overall accuracy for validation data set, which indicate pretty good accuracy of the model. Moreover, the next two images shows confusion matrix for the both data sets, which in details shows how model predict all 10 labels.
The last part of the post is testing model for test data set. For that purpose we selected 10 random images from each label of the test set, and evaluate the model. The following images shows the model correctly predicted all 10 images.
ANNdotNET v1.2 image classification module offers complete data preparation and model development for image classification. The user can prepare data for training, create network model with Neural Network Designer, and perform set of statistical tools against trained model in order to validate and evaluate model. The important note is that the data set of images must be stored on specific location in order to use this trained model shown in the blog post. The trained model, as well as mlcofig files, can be load directly into ANNdotNET project explorer by doublick on CIFAR-10.zip feed example.
ANNdotNET as open source project provides outstanding way in complete development of deep learning model.
When building deep learning models, it is often required to check the model for consistency and proper parameters definition. In ANNdotNET, ml network models are designed using Visual Network Designer (VND), so it is easy to see the network configuration. Beside VND, in ANNdotNET there are several visualization features on different level: network preparation, model training phase, post training evaluation, performance analysis, and export results. In this blog post we will learn how to use those features when working with deep learning models
Visualization during network preparation and model training
When preparing network and training parameters, we need information about data sets, input format and output type. This information is relevant for selecting what type of network model to configure, what types of layers we will use, and what learner to select. For example the flowing image shows network configuration containing of 2 embedding layers, 3 dense layers and 2 dropout layers. This network configuration is used to train CNTK model for mushroom data set. As can be seen network layers are arranged as listbox items, and the user has possibility to see, on the highest level, how neural networks looks like, which layers are included in the network, and how many dimensions each layer is defined. This is very helpful, since it provides the way of building network very quickly and accurately, and it requires much less times in comparisons to use traditional way of coding the network in python, or other programming language.
ANNdotNET Network Settings page provides pretty much information about the network, input and output layers, what data set are defined, as well as whole network configuration arranged in layers. Beside network related information, the Network Settings tab page also provides the learning parameters for the network training. More about Visual Network Designer the ready can find on one of the previous blog post.
Since ANNdotNET implements MLEngine which is based on CNTK, so all CNTK related visualization features could be used. The CNTK library provides rich set of visualizations. For example you can use Tensorboard in CNTK for visualization not just computational graph, but also training history, model evaluation etc. Beside Tensorboard, CNTK provides logger module which uses Graphviz tool for visualizing network graph. The bad news of this is that all above features cannot be run on C#, since those implementation are available only in python.
This is one of the main reason why ANNdotNET provides rich set of visualizations for .NET platform. This includes: training history, model evaluation for training and validation data set, as well as model performance analysis. The following image show some of the visualization features: the training history (loss and evaluation) of minibatches during training of mushroom model:
Moreover, the following image shows evaluation of training and validation set for each iteration during training:
Those graphs are generated during training phase, so the user can see what is happening with the model. This is of tremendous help, when deciding when to stop the training process, or are training parameters produce good model at all, or this can be helpful in case when can stop and change parameters values. In case we need to stop the training process immediately, ANNdotNET provides Stop command which stops training process at any time.
Model performance visualization
Once the model is trained, ANNdotNET provides performance analysis tool for all three types of ML problems: regression, binary and multi class classification.
Since the mushrooms project is binary ML problem the following image shows the performance of the trained model:
Using Graphviz to visualize CNTK network graph in C#
We have seen that ANNdotNET provides all types of visualizations CNTK models, and those features are provided by mouse click through the GUI interfaces. One more feature are coming to ANNdotNET v1.1 which uses Grpahviz to visualize CNTK network graph. The feature is implemented based on original CNTK python implementation with some modification and style.
In order to use Graphviz to visualize network computation graph the following requirements must be met:
ANNdotNET v1.0 has been release a few weeks ago, and the feedback is very positive. Also up to now there is no any blocking or serious bug in the release which makes me very happy. For this blog post we are going through Export options in ANNdotNET.
The ANNdotNET supposed to be an application which can offer whole life-cycle for machine learning project: from the defining raw data set, cleaning and features engineering, to training and evaluation of the model. Also with different mlconfig files within the same project, the user has ability to create as many ml configurations as wants. Once the user select the best ml configuration, and the training and evaluation process completes, the next step in ML project life-cycle is the model deployment/export.
Currently, ANNdotNET defines three export options:
Export model result to CSV file,
Export model and model result to Excel, and
Export model in CNTK file format.
With those three export option, we can achieve many ML scenarios.
Export to CSV
Export to CSV provides exporting actual and predicted values of testing data set to comma separated txt file. In case the testing data set is not provided, the result of validation data set will exported. In case nor testing nor validation dataset are not provided the export process is terminated.
The export process starts by selecting appropriate mlconfig file. The network model must be trained prior to be exported.
Once the export process completes, the csv file is created on disk. We can import the exported result in Excel, and similar content will be shows as image below:
Exported result is shows in two columns. The actual and predicted values. In case the classification result is exported, in the header the information about class values are exported.
Export to Excel
Export to Excel option is more than just exporting the result. In fact, it is deployment of the model into Excel environment. Beside exporting all defined data sets (training, Validation, and Test) the model is also exported. Predicted values are calculated by using ANNdotNET Excel Add-in, which the model evaluation looks like calling ordinary Excel formula. More information how it works can be found here.
Exported xlsx file can be opened, and the further analysis for the model and related data sets can be continued. The following image shows exported model for Concrete Slum Test example. Since only two data sets are defined (training and validation) those data sets are exported. As can be seen the predicted column is not filled, only the row is filled with the formula that must be evaluated by inserting equal sign “=” in front of the formula.
Once the formula is evaluated for the first row, we can use Excel trick to copy it on other rows.
The same situation is for other data sets separated in Excel Worksheets.
Export to CNTK
The last option allows to export CNTK trained model in CNTK format. Also ONNX format will be supported as soon as being available on CNTK for C# library. This option is handy in situation where trained CNTK model being evaluated in other solutions.
For this blog post, there is a short video which the reader can see all three options in actions.