pydANN

is an open source, free-to-use, python package that you can use to make deep artificial neural network models. This package comes with a lot of customizations on the models that you create.

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Inorder to make an Artificial Neural Network model using pydANN import the package and instantiate the class `ann()`

from pydann import ann
model = ann()

Inorder to add hidden layers to the model use the method `add_hl( hidden_Layers_list , activations_for_each_layer + 1 )`

# we are now going to add hidden layers to the model 

model.add_hl( [ 2,5,5 ] , activation_functions = ['relu', 'relu' ,'relu' ,'sigmoid'] )

# the above line adds to the model three hidden layers each of 2, 5 and 5 nodes, respectively. 
# The activation_functions should be 1 more in length so as to have one for each hl and an activation for the output layer

To train the model, use the `fit( xtrain, ytrain, epoch = 50, learning_rate = 0.01, verbose = 0,decay = True, decay_iter = 5, decay_rate = 0.9, stop_decay_counter = 100, loss_function = 'mse' )` method

# you can also register the training data before you train the model.

model.fit( train_x , y_train )  # this is the basic implementation of the method without any alteration

epoch : The number of iterations to train the model

learning_rate : The value with which the algorithm optimizes weights and biases

verbose : When put 0, verbose is False ( There won't be data printing ) if set to another value other than 0, then the data is printed after each verbose interval

decay : This value optimizes the learning_rate when set to True

loss_function : The function using which the loss is calculated. Loss function option : 'mse' , 'rmse'

decay_rate : The fractional value with which the learning rate is altered

To plot the change in the cost(s) after each epoch, use `plot_cost_to_epoch()`

model.plot_cost_to_epoch()

To plot the change in learning_rate after each epoch, use `plot_lrc_to_epoch()`

model.plot_lrc_to_epoch()

To predict on test data, use the function `predict( xtest )`

y_predictions = model.predict( xtest )

To evaluate the model base on Mean Squared Error, use `mse_model_eval( ytest,ypreds )`

model.mse_model_eval( ytest,ypreds )

To save the current trained model use method `save_model( file = 'pydann_model.dat' )`

# 'file' is the name of the file in which the model would be saved and it has to be a .dat file
model.save_model()     # to save the model as pydann_model.dat just call the function like this

# to customize the name of the file in which the model should be saved , change 'file'
model.save_model( file = 'model.dat' )

To use the saved model use method `use_model( path )`

# path is the file path ( directory ) to the saved model
model.use_model( )

To dispose the model and clear memory use the `dispose_model()` method

model.dispose_model()

To get the documentations locally on your device about the class use

model.help()

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pydann's Introduction

pydANN

is an open source, free-to-use, python package that you can use to make deep artificial neural network models. This package comes with a lot of customizations on the models that you create.

Docs

Inorder to make an Artificial Neural Network model using pydANN import the package and instantiate the class ann()

Inorder to add hidden layers to the model use the method add_hl( hidden_Layers_list , activations_for_each_layer + 1 )

To train the model, use the fit( xtrain, ytrain, epoch = 50, learning_rate = 0.01, verbose = 0,decay = True, decay_iter = 5, decay_rate = 0.9, stop_decay_counter = 100, loss_function = 'mse' ) method

To plot the change in the cost(s) after each epoch, use plot_cost_to_epoch()

To plot the change in learning_rate after each epoch, use plot_lrc_to_epoch()

To predict on test data, use the function predict( xtest )

To evaluate the model base on Mean Squared Error, use mse_model_eval( ytest,ypreds )

To save the current trained model use method save_model( file = 'pydann_model.dat' )

To use the saved model use method use_model( path )

To dispose the model and clear memory use the dispose_model() method

To get the documentations locally on your device about the class use

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