Package: deepAttention
Deep Attention ModelThe Deep Attention Model is a package that provides an implementation of a deep learning model with attention mechanism for classification tasks. It utilizes LSTM and multi-head attention layers to capture temporal dependencies and highlight relevant features in the input data.InstallationTo install the Deep Attention Model package, you can use pip:bashpipinstalldeepAttentionUsageInitializing the Deep Attention ModelFirst, you need to initialize the Deep Attention Model by providing a path to the working directory where data, images, and models will be stored during calculations. This step allows the package to import all required dependencies and create the necessary folders.pythonimportdeep_attention_modelasda# Specify the path to the working directorymypath='/path/to/working/directory'# Initialize the Deep Attention Modelda.DAModel.initialize(path=mypath)Data Preprocessing and SplittingThe Deep Attention Model provides a convenient method for data preprocessing and splitting into train and test sets. Theget_samplesmethod handles missing data, duplicates, infinities, and also numerizes categorical features.pythonimportpandasaspd# Load the data into a Pandas DataFramedata=pd.read_csv("data.csv")# Perform data preprocessing and split into train and test setsX_train,X_test,y_train,y_test=da.DAModel.get_samples(dataframe=data,target='label',test_size=0.2,samples=n,stratify=True)Building and Training the ModelAfter preprocessing the data, you can build and train the Deep Attention Model using theDAModelclass. Specify the model architecture and compile it with the desired optimizer, loss function, and metrics. Then, train the model on the training data.python# Define the model architecturemodel=da.DAModel(look_back=seq_length,n_features=X_train.shape[1],n_outputs=num_classes,seq_length=seq_length,num_heads=8)# Build the modelmodel.build()# Compile the modelmodel.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])# Train the modelhistory=model.train(X_train,y_train,epochs=50,batch_size=32,validation_split=0.2)Evaluating Model PerformanceTo evaluate the performance of the trained model on the test data, you can use theevaluate_model_performancemethod in theDAprocessorclass. It calculates metrics such as accuracy, precision, recall, and F1-score, and also generates a confusion matrix.pythonfromdeep_attention_modelimportDAprocessor# Evaluate model performancemetrics,confusion_matrix,y_true,y_pred=DAprocessor.evaluate_model_performance(model,X_test,y_test)# Print the metricsprint(metrics)# Plot the confusion matrixDAprocessor.plot_confusion_matrix(confusion_matrix,file_name='confusion_matrix.png')Saving and Loading the ModelYou can save the trained model to a file and load it later for inference or further training using thesave_modelandload_modelmethods.python# Save the modelmodel.save_model(filepath='model.h5')# Load a pre-trained modelloaded_model=da.DAModel()loaded_model.load_model(filepath='model.h5')# Make predictions using the loaded modelpredictions=loaded_model.predict(X_test)ExamplesYou can find example scripts demonstrating the usage of the Deep Attention Model in theexamplesdirectory.ContributingContributions to the Deep Attention Model project are welcome! If you have any ideas, bug reports, or feature requests, please feel free to open an issue on the project's GitHub repository. If you'd like to contribute code, you can fork the repository, make your changes, and submit a pull request. Please ensure that your code follows the project's coding style and includes appropriate tests.LicenseThe Deep Attention Model package is released under theMIT License.CreditsThe Deep Attention Model package is developed and maintained by the Deep Learning team at the Research Centre for AI and Robotics, Near East University. We would like to acknowledge the contributions of the open-source community and the libraries that we have used to build this package.TensorFlow:https://www.tensorflow.org/Keras:https://keras.io/NumPy:https://numpy.org/Pandas:https://pandas.pydata.org/Matplotlib:https://matplotlib.org/Seaborn:https://seaborn.pydata.org/ContactIf you have any questions or inquiries regarding the Deep Attention Model package, please contact our team atmercel.vubangsi@aiiot.website.Thank you for choosing the Deep Attention Model package! We hope it helps you in your classification tasks. If you have any feedback or suggestions for improvement, we would love to hear from you. Happy coding!