Package: ak-pynn
MultiLayer Neural Network (ak_pynn)A simplistic and efficient pure-python neural network library, which can be used to build , visualize and deploy deep learning ANN models. It is optimized for best performance.Optimized for performanceBetter VisualizationCross platformAuthors@ankit_kohliLicenseMITSupportFor support, emailcontact.ankitkohli@gmail.comFeaturesEfficient implementations of activation functions and their gradientsSigmoidReLULeaky ReLUSoftmaxSoftplusTanhEluLinearEfficient implementations of loss functions and their gradientsMean squared errorMean absolute errorBinary cross entropyCategorical cross entropySeveral methods for weights initialization'random uniform','random normal''Glorot Uniform','Glorot Normal''He Uniform','He Normal'Neural network optimization usingGradient Descent (Batch/ SGD / Mini-Batch)MomentumAdagradRMSpropAdamRegularizationsL1 NormL2 NormL1_L2 NormDropoutsBatch NormalizationEarly StoppingValidation SplitsPredict ScoresInstallationInstall the release (stable) version from PyPipip install ak-pynnUsage/ExamplesImportfromak_pynn.mlpimportMLPUsagemodel=MLP()model.add_layer(4,input_layer=True)model.add_layer(10,activation_function='relu',batch_norm=True)model.add_layer(10,activation_function='relu',dropouts=True)model.add_layer(10,activation_function='relu')model.add_layer(3,activation_function='softmax',output_layer=True)model.compile_model(optimizer='Adam',loss_function='mse',metrics=['mse','accuracy'])Output( MODEL SUMMARY )                        
        
        ===================================================================
               Layer           Activation    Output Shape      Params    
        ===================================================================

               Input             linear       (None, 4)          0       
        -------------------------------------------------------------------

               Dense              relu        (None, 10)         50      
        -------------------------------------------------------------------

         BatchNormalization       None        (None, 10)         40      
        -------------------------------------------------------------------

               Dense              relu        (None, 10)        110      
        -------------------------------------------------------------------

              Dropout             None        (None, 10)         0       
        -------------------------------------------------------------------

               Dense              relu        (None, 10)        110      
        -------------------------------------------------------------------

               Output           softmax       (None, 3)          33      
        -------------------------------------------------------------------

        ===================================================================

        Total Params  - 343
        Trainable Params  - 323
        Non-Trainable Params  - 20
        ___________________________________________________________________Visualizing modelmodel.visualize()Training the modelmodel.fit(X_train,Y_train,epochs=200,batch_size=32,verbose=False,early_stopping=False,patience=3,validation_split=0.2)model.predict_scores(X_test,Y_test,metrics=['accuracy','precision','macro_recall'])plt.plot(model.history['Val_Losses'])plt.plot(model.history['Losses'])TESTS@mnist_test@iris_test@mlp_demoCitationIf you use this library and would like to cite it, you can use:Ankit kohli, "ak-pynn: Neural Network libray", 2023. [Online]. Available: https://github.com/ankit869/ak-pynn. [Accessed: DD- Month- 20YY].or:@Misc{,
  author = {Ankit kohli},
  title  = {ak-pynn: Neural Network libray},
  month  = May,
  year   = {2023},
  note   = {Online; accessed <today>},
  url    = {https://github.com/ankit869/ak-pynn},
}