Hello
Sorry for being away for so long.
Given that everyone is talking about chatGPT and language models in general, I'll join the trend.
I've been doing experiments with chatGPT 3.5, chatGPT 4 and Hugging Face. For some questions, they all fail terribly. For other questions, one or another gives a good reply.
Anyway, follows a very interesting experiment about Free Pascal.
Schuler:Hello. I hope that you are doing well. I'm trying to understand the following free pascal source code. Do you know what it does please?
NN := TNNet.Create();
NN.AddLayer([
TNNetInput.Create(32, 32, 3), //32x32x3 Input Image
TNNetConvolutionReLU.Create({Features=}16, {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0),
TNNetMaxPool.Create({Size=}2),
TNNetConvolutionReLU.Create({Features=}32, {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0),
TNNetMaxPool.Create({Size=}2),
TNNetConvolutionReLU.Create({Features=}32, {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0),
TNNetFullConnectReLU.Create({Neurons=}32),
TNNetFullConnectLinear.Create(NumClasses),
TNNetSoftMax.Create()
]);
:
The given source code is creating a neural network model using the Free Pascal programming language. Here's a breakdown of what the code does:
NN := TNNet.Create(); - This line creates an instance of a neural network model called NN using the TNNet class.
NN.AddLayer([...]); - This line adds layers to the neural network model. Each layer is specified as an array of objects representing different types of layers in the network.
a. TNNetInput.Create(32, 32, 3) - This layer represents the input layer of the network and expects 32x32x3 input images (32 pixels width, 32 pixels height, and 3 color channels for RGB).
b. TNNetConvolutionReLU.Create({Features=}16, {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0) - This layer is a convolutional layer with Rectified Linear Unit (ReLU) activation. It performs 2D convolutions on the input images with 16 features (output channels), a feature size of 5x5, no padding, a stride of 1, and bias not suppressed.
c. TNNetMaxPool.Create({Size=}2) - This layer is a max pooling layer that downsamples the input by selecting the maximum value within a 2x2 window.
d. The above two layers (convolution + max pooling) are repeated twice with different feature sizes (32 features) but with the same configuration.
e. TNNetFullConnectReLU.Create({Neurons=}32) - This layer is a fully connected layer with ReLU activation. It connects all the neurons from the previous layer to the current layer with 32 neurons.
f. TNNetFullConnectLinear.Create(NumClasses) - This layer is a fully connected layer with linear activation. It connects the previous layer to the output layer, where NumClasses represents the number of classes in the classification task.
g. TNNetSoftMax.Create() - This layer is the output layer that applies the softmax function to produce probability scores for each class.
By adding these layers to the neural network model, the code creates a convolutional neural network (CNN) architecture commonly used for image classification tasks. The specific architecture details (number of layers, feature sizes, etc.) may vary depending on the specific problem domain and requirements.
Schuler :
Regarding the provided source code above, can you rewrite it adding more and better source code comments please?
GPT-3.5 :
Certainly! Here's the rewritten source code with more comprehensive comments:
NN := TNNet.Create(); // Create a new neural network model
NN.AddLayer([
TNNetInput.Create(32, 32, 3), // Input layer: Expects 32x32x3 input images (32 pixels width, 32 pixels height, and 3 color channels for RGB)
TNNetConvolutionReLU.Create({Features=}16, {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0),
// Convolutional layer with ReLU activation:
// - Features: 16 output channels
// - FeatureSize: 5x5 filter size
// - Padding: 0 (no additional padding)
// - Stride: 1 (movement of the filter by 1 pixel at a time)
// - SuppressBias: 0 (include bias parameters)
TNNetMaxPool.Create({Size=}2), // Max pooling layer: Downsampling the input by selecting the maximum value within a 2x2 window
TNNetConvolutionReLU.Create({Features=}32, {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0),
// Another Convolutional layer with ReLU activation:
// - Features: 32 output channels
// - FeatureSize: 5x5 filter size
// - Padding: 0
// - Stride: 1
// - SuppressBias: 0
TNNetMaxPool.Create({Size=}2), // Another Max pooling layer
TNNetConvolutionReLU.Create({Features=}32, {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0),
// Another Convolutional layer with ReLU activation:
// - Features: 32 output channels
// - FeatureSize: 5x5 filter size
// - Padding: 0
// - Stride: 1
// - SuppressBias: 0
TNNetFullConnectReLU.Create({Neurons=}32), // Fully connected layer with ReLU activation: Connects all neurons from the previous layer to the current layer with 32 neurons
TNNetFullConnectLinear.Create(NumClasses), // Fully connected layer with linear activation: Connects the previous layer to the output layer with 'NumClasses' number of neurons/classes
TNNetSoftMax.Create() // Softmax layer: Produces probability scores for each class
]);
--
Additional comments from Schuler:
GPT-3.5 was able to vastly go beyond the original source code comments. The only mistake I can see is "repeated twice". I would say "repeated" or "repeated once".
What is now clear to me: GPT-3.5 can help at explaining existing free pascal source code and can help at adding documentation.
Another topic: I'll be at
https://www.pascalcongress.com/ ...
--
I then asked the very first question to GPT-4 and this is what I've got:
GPT-4 :
This Pascal code is defining a Convolutional Neural Network (CNN) using the TNNet library. CNNs are a class of deep neural networks, most commonly used to analyze visual images.
Let's break down the code:
TNNet.Create(): This line is creating a new instance of a neural network object.
NN.AddLayer(): This method is adding layers to the neural network. In this case, the network contains the following layers:
TNNetInput.Create(32, 32, 3): This is the input layer of the neural network. It's expecting input images of size 32x32 with 3 channels (probably RGB color images).
TNNetConvolutionReLU.Create({Features=}16, {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0): This is a convolutional layer with a Rectified Linear Unit (ReLU) activation function. It uses 16 filters, each with a size of 5x5. There's no padding and the stride is 1. SuppressBias is set to 0, which means bias is not suppressed (bias is a constant value added to the weighted inputs and the bias before passing through the activation function).
TNNetMaxPool.Create({Size=}2): This is a max pooling layer, which reduces the spatial size of the representation to reduce the amount of parameters and computation in the network. This layer is using a 2x2 pooling window.
Another convolutional ReLU layer and max pooling layer are added, this time with 32 filters.
TNNetFullConnectReLU.Create({Neurons=}32): This is a fully connected layer with a ReLU activation function. The layer has 32 neurons.
TNNetFullConnectLinear.Create(NumClasses): This is a fully connected linear layer. The number of neurons equals to the number of classes in the problem at hand, which is defined elsewhere in the code as NumClasses.
TNNetSoftMax.Create(): The final layer is a SoftMax layer, which outputs a vector that represents the probability distributions of a list of potential outcomes. It's most commonly used in applied machine learning, where it can be used for multi-class classification.
So, the code is building a Convolutional Neural Network with specific architecture to be trained and used for image classification tasks. The specific task would be defined by the training data and the NumClasses variable.
--
More comments from Schuler: GPT-4 hasn't made a single error explaining this code. Users could also ask about "please clarify further this line or that line", "please teach me the concept of ...", "please improve this portion of code" (would not make sense as my source code is perfect...
).
--
HuggingChat OpenAssistant/oasst-sft-6-llama-30b:
This Free Pascal source code appears to define an object called NN, which is an instance of class TNNet from some package. From looking at this code snippet alone, we can deduce that this code creates layers in NN using methods from different classes defined within the TNNet module (or other dependent packages). These classes include TNNetInput for input data layer, TNNetConvolutionReLU/TNNetMaxPool sequence for convolutional layers, TNNetConvolutionReLU/TNNetMaxPool again and then FullConnect layers before finishing off with a softmax output activation function (SMO) layer, ultimately producing outputs for multiple different classes NumClasses. But without knowing more details about how these components work together it’s impossible to tell precisely what these weights compute specifically.
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