Layer Class for Neural Network

Author: Cole Howard

class finnegan.layer.Layer(num_neurons, vector_size)

A matrix representation of the neurons in a layer Inspired by: I Am Trask http://iamtrask.github.io/2015/07/12/basic-python-network/

Parameters:
  • num_neurons (int) – The number of instances of the class Neuron in each layer.
  • vector_size (int) – The number of inputs from the previous layer/original input. Also, equivalent to the length of incoming input vector.
weights

numpy array

A matrix reprsentation of the weight space. Each column represents a neurnon in the layer. Each entry in those columns is the value of a weight in that neuron.

mr_output

numpy array

Output of the layer

mr_input

numpy array

Input vector from the layer below (or original input)

deltas

numpy array

Calculated change in the weightspace for the backprop

l_rate

float

The learning rate for the update weight method

reg_rate

float

The factor by which the weights are adjusted for regularization to prevent overfitting.

_act_derivative(vector)

Calculate the derivative of the activation function

Parameters:vector (numpy array) – A vector representing the most recent output of a given layer
Returns:
Return type:numpy array
_layer_level_backprop(output, layer_ahead, target_vector, hidden=True)

Calculates the error at this level

Parameters:
  • layer_ahead (object) – The instance of Layer that this layer’s output is connected to
  • target_vector (numpy array) – A representation of the expected output of the net for the original vector input on this particular pass
  • hidden (bool) – Whether or not the current layer is hidden (default: True)
Returns:

For acknoledgment of execution
Return type:

True
_update_weights()

Update the weights of each neuron based on the backprop calculation

_vector_pass(vector, do_dropout=True)

Takes the vector through the neurons of the layer

Parameters:
  • vector (numpy array) – The input array to the layer
  • do_dropout (bool) – Whether or not weight dropout should happen as the vector passes through the layer
Returns:

The ouput of the layer
Return type:

numpy array