def __init__(self, num_nodes, inputs, activation_function, loss_function):
def __init__(self, num_nodes, inputs, activation_function, loss_function):
"""
Layer constructor
Parameters
----------
num_nodes : int
No. of nodes in the layer
inputs : int
No. of inputs to the layer
activation_function
Returns
-------
None
"""def feed(self, input_array: T_Feature_Array) -> T_Output_Array:
def feed(self, input_array: T_Feature_Array) -> T_Output_Array:
"""
Feeds the given input array to a particular layer.
Parameters
----------
input_array: T_Feature_Array
Input array to be fed to the layer
Returns
-------
output_array: T_Output_Array
"""def activate(self, x):
def activate(self, x):
"""
Passes the output array to an activation function.
Parameters
----------
x
Output array from a layer
Returns
-------
Activated output
"""def calculate_gradients(self, target_or_weights, layer_type, next_layer_deltas=None):
def calculate_gradients(self, target_or_weights, layer_type, next_layer_deltas=None):
"""
Calculates the gradients for each weight and bias
Parameters
----------
target_or_weights
This is either targers array of weights matrix.
Specifically, it'll be the targets array while computing the gradients for the output layer
and weights matrix of the next layer.
layer_type
This will either be "hidden" or "output"
[next_layer_deltas]
This is (not exactly) an optional parameter.
This will be passed only while computing the gradients of a hidden layer.
Returns
-------
Doesn't return anything. But stores the gradients as a class attribute.
"""def update_weights(self, inputs, learning_rate):
def update_weights(self, inputs, learning_rate):
"""
Tweak the weights of the layer.
Parameters
----------
inputs: T_Feature_Array
Input to this network
learning_rate: float
Learning rate of the entire network.
Returns
-------
Doesn't return anything.
"""def display(self):
def display(self):
"""
Display the metadata of the layer.
"""