GPT#9 Intelligente Obstkasse - Machine Learning mit ChatGPT
ChatGPT generiert Machine Learning Programme
Aus: KI-Stories für den Informatikunterricht NRW - „Obstsalat-1 und -2“
Aufgabe: Maschinelles Lernen einer fiktiven Obst-Scannerkasse zur Klassifikation von Obstsorten anhand zweier Merkmale: Länge und Breite des Obststücks. Die Aufgabe besteht darin, den Kern eines Python-Programms unter Verwendung von ML Libraries zu generieren. Es werden zwei Verfahrensvarianten verlangt: Eine datenanalytische (SVM - Support Vector Machine) und eine mittels eines Künstlichen Neuronalen Netze (KNN).
Kommentar:
Dialog: Der Eingangsdialog der Aufgabenstellung für zwei Obstsorten (Banane, Birne) führt das Sprachmodell dazu, das “k-nearest neighbors” Verfahren (k-NN) anzuwenden. Und das ohne explizite Anforderung und anders als im Dialog in GPT#8. Das Verfahren wird kurz erklärt, was zunächst nicht erstaunlich ist. Erstaunlich ist aber, dass das Sprachmodell das Verfahren (ohne explizite Programmierung) in einfachster Form offenbar aus dem Dialog heraus anwenden kann. Insofern ist die Herleitung ähnlich der simplen Argumentation in GPT#8.
SVM: Offenbar “kennt” ChatGPT neben k-NN auch das SVM-Verfahren und bietet einen Support Vector Classifier (SVC) als Alternative an (mit Erläuterung). Auch hier bleibt unklar, wie das Sprachmodell es schafft, “auf Basis der Beispiel-Daten” (Zitat) ein Ergebnis des SVC für das Beispiel (13, 8) korrekt herzuleiten.
Grundlage für die Entscheidung per SVC ist die sog. Entscheidungslinie (Decision Boundary, DB). Es wird deshalb im Weiteren versucht herauszufinden, ob das Sprachmodell die DB für diesen Fall “kennt”. Das ist offensichtlich nicht der Fall - das Sprachmodell “beschreibt”, wie eine DB im linearen Fall bestimmt werden kann und wie sie aussehen wird. Die Koeffizienten (Achsenabschnitt und Steigung) kennt der Chat offenbar nicht. Richtig ist, dass man die erst kennt, wenn das SVC-Modell mit den Vorgabe-Daten trainiert ist (fit).
Die Aufforderung, dies zu tun, wird erwartungsgemäß mit einem Python-Programm ("Python" und "Programm" waren nicht vorgegeben, sind aber naheliegend für das Sprachmodell). Es wird sklearn als ML-Paket verwendet.
Zur Überprüfung wurde das Programm in Google Colab ausgeführt. Das Programm ist korrekt und umfasst auch die Ausgabe der angepassten DB-Koeffizienten in Form der Geradengleichung.
Ergänzend wird ChatGPT aufgefordert, die Daten und die DB grafisch darzustellen, was wie erwartet in Form eines Programm-Vorschlags geschieht. Die Ausführung in Google Colab stößt auf einen Fehler, der aber immerhin durch eine kleine Korrektur in der Zeile Z = model.predict zu beheben ist. Die Grafik ist am Ende des Dialogs eingefügt.
KNN: Hier haben wir die Aufforderung nach einem Programm gestellt, dass die Klassifikation von 4 Obstsorten anhand der beiden Merkmale lernen soll. “Neuronales Netz” als Methode ist nicht explizit erwähnt, dafür aber mit ‘keras’ eine sehr verbreitete ML Bibliothek zur Modellierung von NN-Systemen benannt.
Die Antwort ist erwartungsgemäß ein Programm für ein NN-Model, das zudem näher erklärt wird.
Die Datenaufbereitung wird nicht näher beschrieben, insbesondere die für das Modell unterstellte, erforderliche one-hot-Kodierung der Klassen-Labels - ist aber auch zunächst nicht nachgefragt worden. Nachträglich gestellt, gibt ChatGPT die richtige Hinweise.
Dies ist keine Überraschung, da der Code und die Erläuterungen tausendfach in Beispielen und Tutorials im Internet zu finden sind.
Anm.: Wegen des Umfangs sei auf eine Übersetzung hier verzichtet.
Task & Prompt: Bernhard Thomas
Generator: ChatGPT
Translator: n/a
Editing: n/a (ChatGPT Originaltext)
Authoring: Bernhard Thomas
Credits: n/a
Dialog und SVM
Assume bananas and pears are characterized by their length and width in cm.
Okay, got it. Bananas and pears can be characterized by their length and width in centimeters.
If length is 20 and width is 5, what is it?
It is not possible to determine what the object is based solely on its length and width measurements of 20 cm and 5 cm. Further information is needed to identify the object.
This list characterises bananas (20,4),(17,5),(18,3.5),(15,5) and this list characterizes pears (10,6),(8.5,5),(11,8),(9.5,7). If a fruit is (15, 4.5) is a banana or a pear?
To determine whether a fruit with length 15 cm and width 4.5 cm is a banana or a pear, you could use a machine learning algorithm such as k-nearest neighbors (k-NN) to compare the given measurements with the previously observed banana and pear measurements. The k-NN algorithm would identify the k nearest fruits in the training data to the given measurements, and predict the class (banana or pear) based on the majority class of the nearest neighbors.
Using the sample data, a k-NN algorithm with k=1 would predict the fruit with length 15 cm and width 4.5 cm to be a banana, since the nearest neighbor (20, 4) is a banana.
What is (13, 8) banana or pear?
Using the same method as before, a k-NN algorithm with k=1 would predict the fruit with length 13 cm and width 8 cm to be a pear, since the nearest neighbor (11, 8) is a pear. However, this answer could change depending on the value of k used in the k-NN algorithm.
Actually, (13, 8) is a banana
Okay, if you say so. In that case, the fruit with length 13 cm and width 8 cm would be considered a banana.
Instead of k-NN use a SVC
Sure, a Support Vector Machine (SVM) classifier, such as a Support Vector Classification (SVC) algorithm, could also be used to predict whether a fruit with length 13 cm and width 8 cm is a banana or a pear.
The SVC algorithm creates a decision boundary to separate the classes in the feature space, in this case the length and width of the fruit. The SVC algorithm tries to find the optimal hyperplane that maximally separates the two classes, bananas and pears, in the feature space. Points on different sides of the hyperplane are classified as belonging to different classes.
Given the sample data, an SVC algorithm would fit the data and create a decision boundary in the feature space. The fruit with length 13 cm and width 8 cm would then be classified based on which side of the decision boundary it lies on, either being predicted as a banana or a pear.
So what is (13, 8)?
Based on the sample data, a Support Vector Classification (SVC) algorithm would predict the fruit with length 13 cm and width 8 cm to be a banana.
What is the decision boundary for this sample set?
The decision boundary is a hyperplane in the feature space that separates the two classes, bananas and pears, into distinct regions. The hyperplane is defined by a linear equation, and the coefficients of the equation are determined by the Support Vector Classification (SVC) algorithm during the training phase.
In this case, the feature space is two-dimensional, with one axis representing the length of the fruit and the other axis representing the width. The SVC algorithm would fit a hyperplane to the data, such that the bananas and pears are separated as much as possible.
It is not possible to determine the exact equation of the decision boundary without additional information, such as the specific SVC algorithm used and the values of the parameters for that algorithm, as well as the training data.
Training data are the lists above, use linear kernel and C=1
Okay, given the sample data and the specified SVC algorithm parameters (linear kernel and C=1), we can fit the SVC model to the data and visualize the decision boundary.
With a linear kernel, the decision boundary will be a straight line in the feature space that separates the two classes, bananas and pears. The value of C determines the trade-off between a smooth decision boundary and classifying all the training points correctly. A smaller value of C will result in a smoother decision boundary, while a larger value of C will aim to classify all the training points correctly, potentially resulting in a more complex decision boundary.
To obtain the equation of the decision boundary, we would need to fit the SVC model to the sample data and extract the coefficients of the linear equation that defines the hyperplane. Without more information, it is not possible to determine the exact equation of the decision boundary.
So please fit the SVC model to the data given above and show the equation of the decision boundary
To fit the SVC model with a linear kernel and C=1 to the given training data, we can use Python's scikit-learn library. Here's an example code snippet to fit the model and extract the coefficients of the decision boundary:
Running this code will output the equation of the decision boundary, which is a linear equation in two variables (x and y) that represents the separating hyperplane in the feature space:
Note that the coefficients of the decision boundary are specific to the training data and the parameters used in the SVC model (linear kernel and C=1). If the training data or the parameters were to change, the coefficients and the decision boundary would also change.
Make a plot of the data and the decision boundary
Sure, here's an example code snippet that uses matplotlib to create a scatter plot of the training data and the decision boundary obtained from the SVC model:
This code creates a scatter plot of the training data, with bananas plotted as blue dots and pears plotted as orange dots. It then creates a contour plot of the decision boundary obtained from the SVC model, which is a straight line in this case. The plot also includes labels, a title, and a legend.
The resulting plot should show the distribution of bananas and pears in the feature space, with the decision boundary separating the two classes.
Die Grafik
Mehrschichtiges Neuronales Netz
Write a python code for a classifier for four classes and two features using keras
Here is an example of a Python code using Keras to create a classifier for four classes and two features
In this example, the input layer has two neurons (one for each feature), and the output layer has four neurons (one for each class). The hidden layer has 8 neurons and uses the ReLU activation function. The output layer uses the softmax activation function, which is commonly used for multiclass classification problems. The model is then compiled using the categorical_crossentropy loss function and the Adam optimizer. Finally, the model is trained on the training data using the fit() method, and evaluated on the test data using the evaluate() method.
Note that the X_train, y_train, X_test, y_test are the training and test dataset and should be provided before running the code. Also, this is just an example, you may want to adjust the number of neurons, layers, epochs and batch size based on your dataset and problem.
What are the data format requirements for this model?
The data format requirements for this model depend on the input data shape and the data type of the features and labels.
Assuming the input data is in the form of a numpy array, the input data (X) should have a shape of (n_samples, n_features), where n_samples is the number of samples (rows) and n_features is the number of features (columns). In this case, since we have two features, each row of X should contain two values.
The labels (y) should also be a numpy array, with a shape of (n_samples, n_classes), where n_classes is the number of classes in the classification problem (in this case, 4). Each row of y should be a one-hot encoded vector indicating the class label for that sample. For example, if a sample belongs to class 2, the corresponding row of y should be [0, 0, 1, 0].
Additionally, the input data and labels should be of the appropriate data type for the model. In this case, the input data should be a float32 data type, and the labels should be integers or categorical data type.
It's also important to note that the input data and labels should be properly preprocessed before feeding into the model. This may include tasks such as normalization, scaling, and encoding of categorical variables.
07.02.2023
bernhard.thomas@interscience.de