def __init__(self, I, O, cost="mse"):
def __init__(self, I, O, cost="mse"):
"""
Creates a Feed Forward Neural Network.
Parameters
----------
I : int
Number of inputs to the network
O : int
Number of outputs from the network
[cost]: string
The cost/loss function used by the neural network.
Default value is 'mse' which stands for Mean Squared Error.
Available options:
mse => Mean Squared Error
ce => Cross Entropy
Returns
-------
Doesn't return anything
"""def set_learning_rate(self, lr):
def set_learning_rate(self, lr):
"""
Modifies the learning rate of the network.
Parameters
----------
lr : float
New learning rate
Returns
-------
Doesn't return anything
"""def add_layer(self, num_nodes, activation_function="sigmoid"):
def add_layer(self, num_nodes, activation_function="sigmoid"):
"""
Adds a layer to the network.
Parameters
----------
num_nodes : int
Number of nodes in the hidden layer
[activation_function] :str
It is an optional parameter.
Specifies the activation function of the layer.
Default value is sigmoid.
Available options:
sigmoid,
tanh,
linear,
identity,
softmax
Returns
-------
Doesn't return anything
"""def compile(self, activation_function="sigmoid"):
def compile(self, activation_function="sigmoid"):
"""
Basically, it just adds the output layer to the network.
Parameters
----------
[activation_function] :str
It is an optional parameter.
Specifies the activation function of the layer.
Default value is sigmoid.
Returns
-------
Doesn't return anything
"""def feedforward(self, input_array: T_Feature_Array):
def feedforward(self, input_array: T_Feature_Array):
"""
Feeds the given input throughout the network
Parameters
----------
input_array : T_Feature_Array
Input to be fed to the network.
It is columnar vector of size Inputs x 1
Returns
-------
all_outputs : T_Output_Array
An array of all the outputs produced by each layer.
"""def backpropagate(self, target: T_Target_Array):
def backpropagate(self, target: T_Target_Array):
"""
Backpropagate the error throughout the network
This function is called inside the model only.
Parameters
----------
target : np.array()
It is the ground truth value corresponding to the input.
It is columnar vector of size Outputs x 1
Returns
-------
Error : float
# Returns the Mean Squared Error of the particular output
Returns the error using the specified loss function.
"""def update_weights(self, input_array: T_Feature_Array):
def update_weights(self, input_array: T_Feature_Array):
"""
Update the weights of the network.
This function is called inside the model only.
Parameters
----------
input_array : np.array()
It is the input fed to the network
It is columnar vector of size Inputs x 1
Returns
-------
Doesn't return anything
"""def Train(self, dataset: T_Dataset, size, epochs=100, logging=False, epoch_logging=True, prediction_evaulator=None):
def Train(self, dataset: T_Dataset, size, epochs=100, logging=False, epoch_logging=True, prediction_evaulator=None):
"""
Trains the neural network using the given dataset.
Parameters
----------
dataset : T_Dataset
size : int
Size of the dataset
[epochs] : int
An optional parameter.
Number of epochs to train the network. Default value is 5000
[logging] : bool
An optional parameter.
If its true, all outputs from the network will be logged out onto STDOUT for each epoch.
[epoch_logging] : bool
An optional parameter.
If it is true, Error in each epoch will be logged to STDOUT.
[prediction_evaulator]: (prediction: T_Output_Array, target: T_Output_Array) -> bool
An optional parameter.
Used to evaluate the fed forward output with the actual target.
Default value is 'Utils.judge_prediction' function.
Returns
-------
Doesn't return anything.
"""def predict(self, input_array: T_Feature_Array):
def predict(self, input_array: T_Feature_Array):
"""
Predicts the output using a given input.
Parameters
----------
input_array : np.array()
It is columnar vector of size Inputs x 1
It is the input fed to the network
Returns
-------
prediction : np.array()
Predicted value produced by the network.
"""def epoch_vs_error(self):
def epoch_vs_error(self):
"""
Plot error vs epoch graph
Parameters
----------
Doesn't accept any parameters
Returns
-------
Doesn't return anything
"""def evaluate(self):
def evaluate(self):
"""
Print the basic information about the network.
Like accuracy, error ..etc.
Parameters
----------
Doesn't accept any parameters
Returns
-------
Doesn't return anything
"""def display(self):
def display(self):
"""
Print the information of each layer of the network.
It can be used to debug the network!
Parameters
----------
Doesn't accept any parameters
Returns
-------
Doesn't return anything
"""def export_model(self, filename):
def export_model(self, filename):
"""
Export the model to a json file
Parameters
----------
filename: str
File name to export model
Returns
-------
Doesn't return anything
"""def load_model(filename):
def load_model(filename):
"""
Load model from an eported (json) model
Parameters
----------
filename : str
Exported model (json) file
Returns
-------
brain : NeuralNetwork
NeuralNetwork object
"""