classify-galaxies

Classifying Galaxies Using Convolutional Neural Networks

graph TD;

A[Input Data] --> B[Train-Test Split]
B --> C[ImageDataGenerator]
C --> D[Model Architecture]
D --> E[Model Compilation]
E --> F[Model Training]
F --> G[Visualize Activations]
G --> H[Final Model]

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• "Input Data" represents the initial dataset of galaxy images.
• "Train-Test Split" represents the step where the dataset is split into training and testing sets using the train_test_split function from the scikit-learn library.
• "ImageDataGenerator" represents the usage of the ImageDataGenerator class from TensorFlow to preprocess the input data, which includes rescaling the pixel values to be between 0 and 1.
• "Model Architecture" represents the architecture of the convolutional neural network (CNN) model, which consists of two convolutional layers followed by max-pooling layers, a flatten layer, and two fully connected layers with ReLU and softmax activations, respectively. This is defined using the tf.keras.Sequential class.
• "Model Compilation" represents the compilation of the CNN model using the Adam optimizer with a learning rate of 0.001, Categorical Crossentropy loss function, and two evaluation metrics: Categorical Accuracy and AUC (Area Under the Curve). This is done using the compile method of the model.
• "Model Training" represents the training of the CNN model on the training data using the fit method of the model. The training is done for 8 epochs and the validation data is used to evaluate the model performance.
• "Visualize Activations" represents the visualization of the activations of the CNN layers on the test data using the visualize_activations function from the visualize module.
• "Final Model" represents the trained CNN model that can be used for classifying galaxies based on their image data.