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CAI NEURAL API - Pascal based deep learning neural network API optimized for AVX, AVX2 and AVX512 instruction sets plus OpenCL capable devices including AMD, Intel and NVIDIA.

Project README


CAI NEURAL API is a pascal based deep learning neural network API optimized for AVX, AVX2 and AVX512 instruction sets plus OpenCL capable devices including AMD, Intel and NVIDIA. This API has been tested under Windows and Linux.

This project is a subproject from a bigger and older project called CAI and is sister to Keras based K-CAI NEURAL API. You can find trained neural network models in the pre-trained-neural-api-networks repository.

2 Minutes Promo Video

Watch the video

Why Pascal?

  • Compiled pascal code is super fast! This API can outperform some major APIs in some architectures.
  • Pascal is easy to learn and easy to make a readable and understandable source code. You'll be able to make super fast native code and at the same time have a readable code.


You'll need Lazarus development environment. If you have an OpenCL capable device, you'll need its OpenCL drivers. Many examples use the CIFAR-10 dataset. You'll also find examples for the CIFAR-100, MNIST, Fashion MNIST and the Places365-Standard Small images 256x256 dataset.

Will It Work with Delphi?

This project is Lazarus based. That said, as of release v0.98, a number of units do compile with Delphi and you can create and run neural networks with Delphi. You'll be able to compile these units with Delphi: neuralvolume, neuralnetwork, neuralab, neuralabfun, neuralbit, neuralbyteprediction, neuralcache, neuraldatasets, neuralgeneric, neuralplanbuilder, Neural OpenCL, Neural Threading and neuralfit.


Clone this project, add the neural folder to your Lazarus unit search path and you'll be ready to go!

A.I. Powered Support

You can get A.I. powered help from these tools:


The documentation is composed by:

  • Easy Examples.
  • Simple Image Classification Examples.
  • Youtube Videos.
  • Advanced Examples.

In this readme file, you’ll find information about:

  • Data structures (Volumes).
  • Available neural network layers.
  • Dataset support.
  • Training (fitting) your neural network.
  • Parallel computing.
  • Other scientific publications from the same author.

Easy Examples First Please!

Assuming that you would like to train a neural network to learn a function that has 2 inputs and one output, you could start with something like this:


The example above has 2 inputs (TNNetInput), 2 dense layers (TNNetFullConnectReLU) with 32 neurons each and one output (TNNetFullConnectLinear).

You can learn more about how to build and train simple neural networks at the following source code examples:

Loading and Saving Neural Networks

Loading is very easy:

    NN := TNNet.Create;

Saving is as easy:


NLP - Training a Simple Neural Network Model for Text Generation

This NLP source code example shows a (hello world) small neural network trained on the Tiny Stories dataset. This code

    WriteLn(GenerateStringFromChars(NFit.NN, 'once', FSampler),'.');
    WriteLn(GenerateStringFromChars(NFit.NN, 'one ', FSampler),'.');

produces this output:

once upon a time, there was a little girl named lily. she loved to play outside i.
one day, a little girl named lily was playing in her garden. she saw a big car wi.

You can open on colab the raw training file and run it by yourself at:

Creating Your Own Chat Bot

Once your neural network is trained, you can run your own chat bot with:

  S: string;
  oSampler: TNNetSamplerBase;
  NN: TNNet;
  oSampler := TNNetSamplerTopP.Create(0.6);
  NN := TNNet.Create();
  WriteLn('Loading neural network.');
  WriteLn('Write something and I will reply.');
    Write('User: ');
    WriteLn('Neural network: ',GenerateStringFromChars(NN, LowerCase(S), oSampler),'.');
  until S = 'exit';

Simple Image Classification Examples

CIFAR-10 Image Classification Example

The CIFAR-10 dataset is a well-known collection of images commonly used to train machine learning and computer vision algorithms. It was created by the Canadian Institute for Advanced Research (CIFAR). It contains 60K 32x32 color images. The images are classified into 10 different classes, with 6,000 images per class. The classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. Despite its relatively low resolution and small size, CIFAR-10 can be challenging for models to achieve high accuracy, making it a good dataset for testing advancements in machine learning techniques.

Follows a source code example for the CIFAR-10 image classification:

NN := TNNet.Create();
  TNNetInput.Create(32, 32, 3), //32x32x3 Input Image
  TNNetConvolutionReLU.Create({Features=}16, {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0),
  TNNetConvolutionReLU.Create({Features=}32, {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0),
  TNNetConvolutionReLU.Create({Features=}32, {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0),

CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes);

WriteLn('Neural Network will minimize error with:');
WriteLn(' Layers: ', NN.CountLayers());
WriteLn(' Neurons:', NN.CountNeurons());
WriteLn(' Weights:', NN.CountWeights());

NeuralFit := TNeuralImageFit.Create;
NeuralFit.InitialLearningRate := fLearningRate;
NeuralFit.Inertia := fInertia;
NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, NumClasses, {batchsize}128, {epochs}100);

These examples train a neural network to classify images in classes such as: image has a cat, image has a dog, image has an airplane...

You can save and load trained models (neural networks) with TNNet.SaveToFile and TNNet.LoadFromFile. The file format is portable meaning that you can train on CPU and run on GPU or train in AMD and run on ARM as examples. The following code shows a simple example for image classification loading a pre-trained model:

  procedure ClassifyOneImageSimple;
    NN: TNNet;
    ImageFileName: string;
    NeuralFit: TNeuralImageFit;
    WriteLn('Loading Neural Network...');
    NN := TNNet.Create;
    NeuralFit := TNeuralImageFit.Create;
    ImageFileName := 'plant/Apple___Black_rot/image (1).JPG';
    WriteLn('Processing image: ', ImageFileName);
      'The class of the image is: ',
      NeuralFit.ClassifyImageFromFile(NN, ImageFileName)

Youtube Videos

Watch the video Watch the video Watch the video
Increasing Image Resolution with Neural Networks Ultra Fast Single Precision Floating Point Computing AVX and AVX2 Code Optimization

Some videos make referrence to uvolume unit. The current neuralvolume unit used to be called uvolume. This is why it's mentioned.

Advanced Examples

Although these examples require deeper understanding about neural networks, they are very interesting:

There are also some older code examples that you can look at.


Volumes behave like dynamically created arrays. They are the main array like structure used by this API. TNNetVolume class allows you to create volumes that can be accessed as 1D, 2D or 3D arrays and be operated with Advanced Vector Extensions (AVX) - Single Instruction Multiple Data (SIMD) instruction set. The usual way to create a volume is:

constructor Create(pSizeX, pSizeY, pDepth: integer; c: T = 0);

You can access the data as 1D or 3D with:

property Raw[x: integer]: T read GetRaw write SetRaw;
property Data[x, y, d: integer]: T read Get write Store; default;

Your code will look like this:

// Usage Examples
vInput := TNNetVolume.Create(32, 32, 3);
vInput[1, 1, 1] := 1;
vInput[2, 2, 2] := vInput[1, 1, 1] + 1;
vInput.Raw[10] := 5;

WriteLn('Avg: ', vInput.GetAvg());
WriteLn('Variance: ', vInput.GetVariance());
WriteLn('Std Dev: ', vInput.GetStdDeviation());

WriteLn('Multiplying by 10');
WriteLn('Avg: ', vInput.GetAvg());
WriteLn('Variance: ', vInput.GetVariance());
WriteLn('Std Dev: ', vInput.GetStdDeviation());

As examples, you can add, subtract, multiply and calculate dot products with:

procedure Add(Original: TNNetVolume); overload;
procedure Sub(Original: TNNetVolume); overload;
procedure Mul(Value: Single); overload;
function DotProduct(Original: TNNetVolume): TNeuralFloat; overload;

In the case that you need the raw position or raw pointer to an element of the volume, you can get with:

function GetRawPos(x, y, d: integer): integer; overload;
function GetRawPos(x, y: integer): integer; overload;
function GetRawPtr(x, y, d: integer): pointer; overload;
function GetRawPtr(x, y: integer): pointer; overload;
function GetRawPtr(x: integer): pointer; overload;

You can easily operate volumes with OpenCL via TEasyOpenCLV:

  TEasyOpenCLV = class (TEasyOpenCL)
      function CreateBuffer(flags: cl_mem_flags; V: TNNetVolume): cl_mem; overload;
      function CreateInputBuffer(V: TNNetVolume): cl_mem; overload;
      function CreateHostInputBuffer(V: TNNetVolume): cl_mem; overload;
      function CreateOutputBuffer(V: TNNetVolume): cl_mem; overload;
      function CreateBuffer(V: TNNetVolume): cl_mem;  overload;

      function WriteBuffer(buffer: cl_mem; V: TNNetVolume; blocking: cl_bool = CL_FALSE): integer;
      function ReadBuffer(buffer: cl_mem; V: TNNetVolume; blocking: cl_bool = CL_TRUE): integer;

      function CreateAndWriteBuffer(V: TNNetVolume; var buffer: cl_mem): integer; overload;
      function CreateAndWriteBuffer(V: TNNetVolume): cl_mem; overload;
      function CreateWriteSetArgument(V: TNNetVolume; kernel:cl_kernel; arg_index: cl_uint): cl_mem;
      function CreateOutputSetArgument(V: TNNetVolume; kernel:cl_kernel; arg_index: cl_uint): cl_mem;

Volume Pairs, Volume Lists and Volume Pair Lists

Volumes can be organized in pairs:

  /// Implements a pair of volumes
  TNNetVolumePair = class(TObject)
      FA: TNNetVolume;
      FB: TNNetVolume;
      constructor Create(); overload;
      constructor Create(pA, pB: TNNetVolume); overload;
      constructor CreateCopying(pA, pB: TNNetVolume); overload;

      destructor Destroy(); override;

      property A:TNNetVolume read FA;
      property B:TNNetVolume read FB;
      property I:TNNetVolume read FA;
      property O:TNNetVolume read FB;

Depending on the problem that you are trying to solve, modelling the training with pairs or pair lists might be helpful. Typically, a pair will be (input, desired output). This is how volume lists and volume pair lists have been implemented:

TNNetVolumeList = class (specialize TFPGObjectList<TNNetVolume>
TNNetVolumePairList = class (specialize TFPGObjectList<TNNetVolumePair>)

Neural Network Layers

This API is really big. The following list gives a general idea about this API but it doesn't contain everything.

Input Layer

  • TNNetInput (input/output: 1D, 2D or 3D).

Convolutional Layers

  • TNNetConvolution (input/output: 1D, 2D or 3D - feature size: 1D or 2D). This layer has tanh as default activation function.
  • TNNetConvolutionReLU (input/output: 1D, 2D or 3D - feature size: 1D or 2D).
  • TNNetConvolutionLinear (input/output: 1D, 2D or 3D - feature size: 1D or 2D).
  • TNNetPointwiseConvReLU (input/output: 1D, 2D or 3D).
  • TNNetPointwiseConvLinear (input/output: 1D, 2D or 3D).
  • TNNetDepthwiseConv (input/output: 1D, 2D or 3D). This layer has tanh as default activation function.
  • TNNetDepthwiseConvReLU (input/output: 1D, 2D or 3D).
  • TNNetDepthwiseConvLinear (input/output: 1D, 2D or 3D).
  • TNNet.AddSeparableConvReLU (input/output: 1D, 2D or 3D). Adds a separable convolution.
  • TNNet.AddSeparableConvLinear (input/output: 1D, 2D or 3D). Adds a separable convolution.
  • TNNet.AddConvOrSeparableConv (input/output: 1D, 2D or 3D). Adds a convolution or a separable convolution with/without ReLU and normalization.
  • TNNet.AddGroupedConvolution (input/output: 1D, 2D or 3D). Adds a grouped convolution.

Fully Connected (Dense) Layers

  • TNNetFullConnect (input/output: 1D, 2D or 3D). This layer has tanh as default activation function.
  • TNNetFullConnectReLU (input/output: 1D, 2D or 3D).
  • TNNetFullConnectLinear (input/output: 1D, 2D or 3D).
  • TNNetFullConnectSigmoid (input/output: 1D, 2D or 3D).
  • TNNet.AddGroupedFullConnect: inspired on TNNet.AddGroupedConvolution, adds a grouped fully connected layer.

Locally Connected Layers

  • TNNetLocalConnect (input/output: 1D, 2D or 3D - feature size: 1D or 2D). This layer has htan as default activation function.
  • TNNetLocalConnectReLU (input/output: 1D, 2D or 3D - feature size: 1D or 2D).

Min / Max / Avg Pools

  • TNNetAvgPool (input/output: 1D, 2D or 3D).
  • TNNetMaxPool (input/output: 1D, 2D or 3D).
  • TNNetMinPool (input/output: 1D, 2D or 3D).
  • TNNet.AddMinMaxPool (input/output: 1D, 2D or 3D). Does both min and max pools and then concatenates results.
  • TNNet.AddAvgMaxPool (input/output: 1D, 2D or 3D ). Does both average and max pools and then concatenates results.

Min / Max / Avg layers that Operate an Entire Channel and Produce Only One Result per Channel

  • TNNetAvgChannel (input: 2D or 3D - output: 1D). Calculates the channel average.
  • TNNetMaxChannel (input: 2D or 3D - output: 1D). Calculates the channel max.
  • TNNetMinChannel (input: 2D or 3D - output: 1D). Calculates the channel min.
  • TNNet.AddMinMaxChannel (input/output: 1D, 2D or 3D). Does both min and max channel and then concatenates results.
  • TNNet.AddAvgMaxChannel (input/output: 1D, 2D or 3D). Does both average and max channel and then concatenates results.

Trainable Normalization Layers Allowing Faster Learning/Convergence

  • TNNetChannelZeroCenter (input/output: 1D, 2D or 3D). Trainable zero centering.
  • TNNetMovingStdNormalization (input/output: 1D, 2D or 3D). Trainable std. normalization.
  • TNNetChannelStdNormalization (input/output: 1D, 2D or 3D). Trainable per channel std. normalization.
  • TNNet.AddMovingNorm (input/output: 1D, 2D or 3D). Possible replacement for batch normalization.
  • TNNet.AddChannelMovingNorm (input/output: 1D, 2D or 3D). Possible replacement for per batch normalization.

Non Trainable and per Sample Normalization Layers

  • TNNetLayerMaxNormalization (input/output: 1D, 2D or 3D).
  • TNNetLayerStdNormalization (input/output: 1D, 2D or 3D).
  • TNNetLocalResponseNorm2D (input/output: 2D or 3D).
  • TNNetLocalResponseNormDepth (input/output: 2D or 3D).
  • TNNetRandomMulAdd (input/output: 1D, 2D or 3D). Adds a random multiplication (scale) and a random bias (shift).
  • TNNetChannelRandomMulAdd (input/output: 1D, 2D or 3D). Adds a random multiplication (scale) and random bias (shift) per channel.

Concatenation, Summation and Reshaping Layers

  • TNNetConcat (input/output: 1D, 2D or 3D). Allows concatenating previous layers into a single layer.
  • TNNetDeepConcat (input/output: 1D, 2D or 3D). Concatenates previous layers into the depth axis. This is useful with DenseNet like architectures. Use TNNetDeepConcat instead of TNNetConcat if you need to add convolutions after concating layers.
  • TNNetIdentity (input/output: 1D, 2D or 3D).
  • TNNetIdentityWithoutBackprop (input/output: 1D, 2D or 3D). Allows the forward pass to proceed but prevents backpropagation.
  • TNNetReshape (input/output: 1D, 2D or 3D).
  • TNNetSplitChannels (input: 1D, 2D or 3D / output: 1D, 2D or 3D). Splits (or copies) channels from the input. This layer allows getting a subset of the input channels.
  • TNNetSplitChannelEvery (input: 1D, 2D or 3D / output: 1D, 2D or 3D). Splits (or copies) channels from the input every few channels. As example, this layer allows getting half (GetChannelEvery=2) or a third (GetChannelEvery=3) of the input channels.
  • TNNetSum (input/output: 1D, 2D or 3D). Sums outputs from previous layers allowing ResNet style networks.
  • TNNetUpsample (input/output: 3D). Converts channels (depth) into spatial data. For example, a 128x128x256 activation map will be converted to 256x256x64. The number of channels is always divided by 4 while the resolution increases.

Layers with Activation Functions and no Trainable Parameter

  • TNNetReLU (input/output: 1D, 2D or 3D).
  • TNNetReLU6 (input/output: 1D, 2D or 3D).
  • TNNetReLUL (input/output: 1D, 2D or 3D).
  • TNNetLeakyReLU (input/output: 1D, 2D or 3D).
  • TNNetVeryLeakyReLU (input/output: 1D, 2D or 3D).
  • TNNetReLUSqrt (input/output: 1D, 2D or 3D).
  • TNNetSELU (input/output: 1D, 2D or 3D).
  • TNNetSigmoid (input/output: 1D, 2D or 3D).
  • TNNetSoftMax (input/output: 1D, 2D or 3D).
  • TNNetSwish (input/output: 1D, 2D or 3D).
  • TNNetSwish6 (input/output: 1D, 2D or 3D).
  • TNNetHardSwish (input/output: 1D, 2D or 3D).
  • TNNetHyperbolicTangent (input/output: 1D, 2D or 3D).
  • TNNetPower (input/output: 1D, 2D or 3D).

Trainable Bias (Shift) and Multiplication (Scaling) per Cell or Channel Allowing Faster Learning and Convergence

  • TNNetCellBias (input/output: 1D, 2D or 3D).
  • TNNetCellMul (input/output: 1D, 2D or 3D).
  • TNNetChannelBias (input/output: 1D, 2D or 3D).
  • TNNetChannelMul (input/output: 1D, 2D or 3D).

Opposing Operations

  • TNNetDeLocalConnect (input/output: 1D, 2D or 3D - feature size: 1D or 2D).
  • TNNetDeLocalConnectReLU (input/output: 1D, 2D or 3D - feature size: 1D or 2D).
  • TNNetDeconvolution (input/output: 1D, 2D or 3D - feature size: 1D or 2D).
  • TNNetDeconvolutionReLU (input/output: 1D, 2D or 3D - feature size: 1D or 2D).
  • TNNetDeMaxPool (input/output: 1D, 2D or 3D - max is done on a single layer).

Weight Initializers

This API implements popular weight initialization methods including He (Kaiming) and Glorot/Bengio (Xavier):

  • InitUniform(Value: TNeuralFloat = 1).
  • InitLeCunUniform(Value: TNeuralFloat = 1).
  • InitHeUniform(Value: TNeuralFloat = 1).
  • InitHeUniformDepthwise(Value: TNeuralFloat = 1).
  • InitHeGaussian(Value: TNeuralFloat = 0.5).
  • InitHeGaussianDepthwise(Value: TNeuralFloat = 0.5).
  • InitGlorotBengioUniform(Value: TNeuralFloat = 1).
  • InitSELU(Value: TNeuralFloat = 1).

Data Augmentation Methods Implemented at TVolume

  • procedure FlipX();
  • procedure FlipY();
  • procedure CopyCropping(Original: TVolume; StartX, StartY, pSizeX, pSizeY: integer);
  • procedure CopyResizing(Original: TVolume; NewSizeX, NewSizeY: integer);
  • procedure AddGaussianNoise(pMul: TNeuralFloat);
  • procedure AddSaltAndPepper(pNum: integer; pSalt: integer = 2; pPepper: integer = -2);

Closest Layer Types to Other APIs (work in progress)

NEURAL Keras PyTorch
TNNetFullConnect layers.Dense(activation='tanh') nn.Linear nn.Tanh()
TNNetFullConnectReLU layers.Dense(activation='relu') nn.Linear nn.ReLU()
TNNetFullConnectLinear layers.Dense(activation=None) nn.Linear
TNNetFullConnectSigmoid layers.Dense(activation='sigmoid') nn.Linear nn.Sigmoid()
TNNetReLU activations.relu nn.ReLU()
TNNetLeakyReLU activations.relu(alpha=0.01) nn.LeakyReLU(0.01)
TNNetVeryLeakyReLU activations.relu(alpha=1/3) nn.LeakyReLU(1/3)
TNNetSELU activations.selu nn.SELU
TNNetSigmoid activations.sigmoid nn.Sigmoid
TNNetSoftMax activations.softmax nn.Softmax
TNNetHyperbolicTangent activations.tanh nn.Tanh
TNNetAvgPool layers.AveragePooling2D nn.AvgPool2d
TNNetMaxPool layers.MaxPool2D nn.MaxPool2d
TNNetMaxPoolPortable layers.MaxPool2D nn.MaxPool2d
TNNetAvgChannel layers.GlobalAveragePooling2D nn.AvgPool2d
TNNetMaxChannel layers.GlobalMaxPool2D nn.MaxPool2d
TNNet.AddAvgMaxChannel cai.layers.GlobalAverageMaxPooling2D
TNNetConcat layers.Concatenate(axis=1)
TNNetDeepConcat layers.Concatenate(axis=3)
TNNetIdentity nn.Identity
TNNetReshape layers.Reshape torch.reshape
TNNetSplitChannels cai.layers.CopyChannels
TNNetSum layers.Add torch.add
TNNetCellMulByCell layers.Multiply
TNNetChannelMulByLayer layers.Multiply
TNNetUpsample tf.nn.depth_to_space

Adding Layers

You can add layers one by one or you can add an array of layers in one go. Follows an example adding layers one by one:

NN.AddLayer(TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}5, {Padding=}2, {Stride=}1));

The next example shows how to add an array of layers that is equivalent to the above example:

  TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}5, {Padding=}2, {Stride=}1),

Multi-path Architectures Support

Since 2017, this API supports multi-paths architectures. You can create multi-paths with AddLayerAfter method. For concatenating (merging) paths, you can call either TNNetConcat or TNNetDeepConcat. Follows an example:

// Creates The Neural Network
NN := TNNet.Create();
// This network splits into 2 paths and then is later concatenated
InputLayer := NN.AddLayer(TNNetInput.Create(32, 32, 3));
// First branch starting from InputLayer (5x5 features)
NN.AddLayerAfter(TNNetConvolutionReLU.Create({Features=}16, {FeatureSize=}5, {Padding=}2, {Stride=}1), InputLayer);
NN.AddLayer(TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}5, {Padding=}2, {Stride=}1));
EndOfFirstPath := NN.AddLayer(TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}5, {Padding=}2, {Stride=}1));
// Another branch starting from InputLayer (3x3 features)
NN.AddLayerAfter(TNNetConvolutionReLU.Create({Features=}16, {FeatureSize=}3, {Padding=}1, {Stride=}1), InputLayer);
NN.AddLayer(TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1));
EndOfSecondPath := NN.AddLayer(TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1));
// Concats both branches into one branch.
NN.AddLayer(TNNetDeepConcat.Create([EndOfFirstPath, EndOfSecondPath]));
NN.AddLayer(TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1));

These source code examples show AddLayerAfter:

You can find more about multi-path architectures at:

Dataset Support

These datasets can be easily loaded:


procedure CreateCifar10Volumes(out ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList);

Source code example: Simple CIFAR-10 Image Classifier


procedure CreateCifar100Volumes(out ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList);

Source code example: CAI Optimized DenseNet CIFAR-100 Image Classifier

MNIST and Fashion MNIST

procedure CreateMNISTVolumes(out ImgTrainingVolumes, ImgValidationVolumes,
  ImgTestVolumes: TNNetVolumeList;
  TrainFileName, TestFileName: string;
  Verbose:boolean = true;
  IsFashion:boolean = false);

Source code examples:

One Class per Folder with Image Classification

In the case that your dataset has one class per folder, you can call CreateVolumesFromImagesFromFolder for loading your data into RAM:

// change ProportionToLoad to a smaller number if you don't have enough RAM.
ProportionToLoad := 1;
WriteLn('Loading ', Round(ProportionToLoad*100), '% of the Plant leave disease dataset into memory.');
  ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes,
  {FolderName=}'plant', {pImageSubFolder=}'',
  {NewSizeX=}128, {NewSizeY=}128

The example above shows how to load the dataset with 90% loaded into training and 5% loaded for each validation and testing. Images are being resized to 128x128.

Source code examples:

Is your Dataset too Big for RAM? You should use TNeuralImageLoadingFit.

In the case that your image classification dataset is too big to be stored in RAM, you can follow this example:

    FTrainingFileNames, FValidationFileNames, FTestFileNames: TFileNameList;
    ProportionToLoad := 1;
      FTrainingFileNames, FValidationFileNames, FTestFileNames,
      {FolderName=}'places_folder/train', {pImageSubFolder=}'',

Then, you can call a fitting method made specific for this:

NeuralFit := TNeuralImageLoadingFit.Create;
NeuralFit.FitLoading({NeuralNetworkModel}NN, {ImageSizeX}256, {ImageSizeY}256, FTrainingFileNames, FValidationFileNames, FTestFileNames, {BatchSize}256, {Epochs}100);

TNeuralImageLoadingFit.FitLoading has been tested with Places365-Standard Small images 256x256 with easy directory structure. You can follow this example:

Loading and Saving Images with Volumes

When loading an image from a file, the easiest and fastest method is calling LoadImageFromFileIntoVolume(ImageFileName:string; V:TNNetVolume). When loading from an TFPMemoryImage, you can load with LoadImageIntoVolume(M: TFPMemoryImage; Vol:TNNetVolume). For saving an image, the fastest method is SaveImageFromVolumeIntoFile(V: TNNetVolume; ImageFileName: string).

Fitting your Neural Network

The easiest way to train your neural network is utilizing unit neuralfit.pas. Inside this unit, you’ll find the class TNeuralImageFit that is used by many examples.

Image Classification

TNeuralImageFit has been designed for image classification tasks and can be called as follows:

procedure Fit(pNN: TNNet;
  pImgVolumes, pImgValidationVolumes, pImgTestVolumes: TNNetVolumeList;
  pNumClasses, pBatchSize, Epochs: integer);

Each volume should be provided with property tag that contains the corresponding class. TNeuralImageFit internally implements data augmentation techniques: flipping, making gray, cropping and resizing. These techniques can be controlled with:

property HasImgCrop: boolean read FHasImgCrop write FHasImgCrop;
property HasMakeGray: boolean read FHasMakeGray write FHasMakeGray;
property HasFlipX: boolean read FHasFlipX write FHasFlipX;
property HasFlipY: boolean read FHasFlipY write FHasFlipY;
property MaxCropSize: integer read FMaxCropSize write FMaxCropSize; 

Once you have a trained neural network, you can use an advanced classification procedure that will average the classification probability of the input image with its flipped and cropped versions. This process frequently gives a higher classification accuracy at the expense of internally running the very same neural network a number of times. This is how you can classify images:

procedure ClassifyImage(pNN: TNNet; pImgInput, pOutput: TNNetVolume);

In the case that you would like to look into TNeuralImageFit in more detail, the Simple CIFAR-10 Image Classifier example is a good starting point.

Training with Volume Pair Lists - TNeuralFit

In the case that your training, validation and testing data can be defined as volume pairs from input volume to output volume, the easiest way to train your neural network will be calling TNeuralFit. This class has the following fitting method:

procedure Fit(pNN: TNNet;
  pTrainingVolumes, pValidationVolumes, pTestVolumes: TNNetVolumePairList;
  pBatchSize, Epochs: integer);

Both AND, OR and XOR with neuralfit unit and hypotenuse function examples load volume pair lists for training.

Training with Volume Pairs - TNeuralDataLoadingFit

The TNeuralFit implementation has a limitation: your dataset needs to be placed into RAM. In the case that your dataset is too large for RAM, you can call TNeuralDataLoadingFit:

TNNetGetPairFn = function(Idx: integer; ThreadId: integer): TNNetVolumePair of object;
TNNetGet2VolumesProc = procedure(Idx: integer; ThreadId: integer; pInput, pOutput: TNNetVolume) of object;
TNeuralDataLoadingFit = class(TNeuralFitBase)
    procedure FitLoading(pNN: TNNet;
      TrainingCnt, ValidationCnt, TestCnt, pBatchSize, Epochs: integer;
      pGetTrainingPair, pGetValidationPair, pGetTestPair: TNNetGetPairFn); overload;
    procedure FitLoading(pNN: TNNet;
      TrainingCnt, ValidationCnt, TestCnt, pBatchSize, Epochs: integer;
      pGetTrainingProc, pGetValidationProc, pGetTestProc: TNNetGet2VolumesProc); overload;

The Hypotenuse with FitLoading example uses TNeuralDataLoadingFit so it creates training pairs on the fly.


TNeuralImageFit and TNeuralDataLoadingFit both descend from TNeuralFitBase. From TNeuralFitBase, you can define training properties:

property Inertia: single read FInertia write FInertia;
property InitialEpoch: integer read FInitialEpoch write FInitialEpoch;
property InitialLearningRate: single read FInitialLearningRate write FInitialLearningRate;
property LearningRateDecay: single read FLearningRateDecay write FLearningRateDecay;
property CyclicalLearningRateLen: integer read FCyclicalLearningRateLen write FCyclicalLearningRateLen;
property Momentum: single read FInertia write FInertia;
property L2Decay: single read FL2Decay write FL2Decay;
property FileNameBase: string read FFileNameBase write FFileNameBase;

You can also collect current statistics:

property CurrentEpoch: integer read FCurrentEpoch;
property CurrentStep: integer read FCurrentStep;
property CurrentLearningRate: single read FCurrentLearningRate;
property TestAccuracy: TNeuralFloat read FTestAccuracy;
property TrainingAccuracy: TNeuralFloat read FTrainingAccuracy;
property Running: boolean read FRunning;

Some events are available:

property OnStart: TNotifyEvent read FOnStart write FOnStart;
property OnAfterStep: TNotifyEvent read FOnAfterStep write FOnAfterStep;
property OnAfterEpoch: TNotifyEvent read FOnAfterEpoch write FOnAfterEpoch;

You can define your own learning rate schedule:

property CustomLearningRateScheduleFn: TCustomLearningRateScheduleFn read FCustomLearningRateScheduleFn write FCustomLearningRateScheduleFn;
property CustomLearningRateScheduleObjFn: TCustomLearningRateScheduleObjFn read FCustomLearningRateScheduleObjFn write FCustomLearningRateScheduleObjFn;

Got Too Many Console Messages?

TNeuralFitBase descends from TMObject that allows you to code your own message treatment:

property MessageProc: TGetStrProc read FMessageProc write FMessageProc;
property ErrorProc: TGetStrProc read FErrorProc write FErrorProc;

On your own code, you could something is:

MyFit.MessageProc := {$IFDEF FPC}@{$ENDIF}Self.MessageProc;
MyFit.ErrorProc := {$IFDEF FPC}@{$ENDIF}Self.ErrorProc;

If you don’t need any message at all, you can hide messages by calling:

procedure HideMessages();

You can also disable fitting verbosity with:

property Verbose: boolean read FVerbose write FVerbose;

Your code will look like this:

NeuralFit := TNeuralImageFit.Create;
NeuralFit.Verbose := false;

Parallel Computing - The neuralthread.pas

This API has easy to use, lightweight and platform independent parallel processing API methods.

As an example, assuming that you need to run a procedure 10 times in parallel, you can create 10 thread workers as follows:

FProcs := TNeuralThreadList.Create( 10 );

As an example, this is the procedure that we intend to run in parallel:

procedure MyClassName.RunNNThread(index, threadnum: integer);
  WriteLn('This is thread ',index,' out of ',threadnum,' threads.');

Then, to run the procedure RunNNThread passed as parameter 10 times in parallel, do this:

FProcs.StartProc({$IFDEF FPC}@RunNNThread{$ELSE}RunNNThread{$ENDIF});

You can control the blocking mode (waiting threads to finish before the program continues) as per declaration:

procedure StartProc(pProc: TNeuralProc; pBlock: boolean = true);

Or, if you prefer, you can specifically say when to wait for threads to finish as per this example:

FProcs.StartProc({$IFDEF FPC}@RunNNThread{$ELSE}RunNNThread{$ENDIF}, false);
// insert your code here
FProcs.WaitForProc(); // waits until all threads are finished.

When you are done, you should call:


Publications from the Author

In the case that you would like to know more about what the CAI's author is working at, here we go.

Optimizing the first layers of a convolutional neural network:

Optimizing deep layers of a convolutional neural network:

Publicações em Português:


Pull requests are welcome. Having requests accepted might be hard.

Citing this API

You can cite this API in BibTeX format with:

  author       = {Joao Paulo Schwarz Schuler},
  title        = {CAI NEURAL API},
  month        = dec,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v1.0.6},
  doi          = {10.5281/zenodo.5810077},
  url          = {}
Open Source Agenda is not affiliated with "Neural Api" Project. README Source: joaopauloschuler/neural-api