Master the Secret Sauce of AI: Neural Networks & Backpropagation Unleashed!
Get ready to embark on an exhilarating journey into the world of artificial intelligence, where we unravel the magic behind neural networks and backpropagation!
In our latest video, “Master the Secret Sauce of AI: Neural Networks & Backpropagation Unleashed!”, we delve into the inner workings of these ingenious algorithms that fuel groundbreaking innovations and touch our lives daily.
Whether you’re an AI enthusiast, a budding data scientist, or simply intrigued by the power of cutting-edge technology, this article and accompanying video will be your gateway to understanding the secret sauce that makes neural networks learn, adapt, and excel. Buckle up, and prepare to be amazed by the wonders of AI!
Backpropagation is a process involved in training a neural network. It involves taking the error rate of a forward propagation and feeding this loss backward through the neural network layers to fine-tune the weights. Backpropagation is the essence of neural net training.
class NeuralNetwork:
LEARNING_RATE = 0.5
# Initializes a neural network object with given parameters
def __init__(self, num_inputs, num_hidden, num_outputs, hidden_layer_weights = None,
hidden_layer_bias = None, output_layer_weights = None, output_layer_bias = None):
self.num_inputs = num_inputs # number of inputs for each input layer neuron
# creates a hidden layer as an instance of the NeuronLayer class
# with num_hidden neurons and bias values initialized to hidden_layer_bias
self.hidden_layer = NeuronLayer(num_hidden, hidden_layer_bias)
# creates an output layer as an instance of the NeuronLayer class
# with num_outputs neurons and bias values initialized to output_layer_bias
self.output_layer = NeuronLayer(num_outputs, output_layer_bias)
# initializes the weights from the input layer to the hidden layer neurons
# using the provided hidden_layer_weights or random initialization if None is provided
self.init_weights_from_inputs_to_hidden_layer_neurons(hidden_layer_weights)
# initializes the weights from the hidden layer neurons to the output layer neurons
# using the provided output_layer_weights or random initialization if None is provided
self.init_weights_from_hidden_layer_neurons_to_output_layer_neurons(output_layer_weights)
def init_weights_from_inputs_to_hidden_layer_neurons(self, hidden_layer_weights):
# Initialize a variable to keep track of the current weight index
weight_num = 0
# Iterate through all the neurons in the hidden layer
for h in range(len(self.hidden_layer.neurons)):
# Iterate through all the input neurons
for i in range(self.num_inputs):
# If there are no provided hidden layer weights, assign random weights
if not hidden_layer_weights:
self.hidden_layer.neurons[h].weights.append(random.random())
# Otherwise, assign the provided weights to the hidden layer neurons
else:
self.hidden_layer.neurons[h].weights.append(hidden_layer_weights[weight_num])
# Increment the weight index
weight_num += 1
This function initializes the weights between the input and hidden layer neurons in a neural network. It takes two arguments: self and hidden_layer_weights. The self argument refers to the instance of the class where the function is defined, while hidden_layer_weights is an optional list of weights to be used instead of random values.
The function iterates through all the neurons in the hidden layer, and then through all the input neurons. If there are no provided hidden layer weights, it assigns random weights to the connections between input and hidden layer neurons. If there are provided weights, it assigns those weights to the connections. The function also increments the weight index for each connection to keep track of which weight is being assigned.
def init_weights_from_inputs_to_hidden_layer_neurons(self, hidden_layer_weights):
# Initialize a variable to keep track of the current weight index
weight_num = 0
# Iterate through all the neurons in the hidden layer
for h in range(len(self.hidden_layer.neurons)):
# Iterate through all the input neurons
for i in range(self.num_inputs):
# If there are no provided hidden layer weights, assign random weights
if not hidden_layer_weights:
self.hidden_layer.neurons[h].weights.append(random.random())
# Otherwise, assign the provided weights to the hidden layer neurons
else:
self.hidden_layer.neurons[h].weights.append(hidden_layer_weights[weight_num])
# Increment the weight index
weight_num += 1
This function initializes the weights between the hidden layer and output layer neurons in a neural network. It takes two arguments: self and output_layer_weights. The self argument refers to the instance of the class where the function is defined, while output_layer_weights is an optional list of weights to be used instead of random values.
The function iterates through all the neurons in the output layer and then through all the neurons in the hidden layer. If there are no provided output layer weights, it assigns random weights to the connections between hidden and output layer neurons. If there are provided weights, it assigns those weights to the connections. The function also increments the weight index for each connection to keep track of which weight is being assigned.
def init_weights_from_hidden_layer_neurons_to_output_layer_neurons(self, output_layer_weights):
# Initialize a variable to keep track of the current weight index
weight_num = 0
# Iterate through all the neurons in the output layer
for o in range(len(self.output_layer.neurons)):
# Iterate through all the neurons in the hidden layer
for h in range(len(self.hidden_layer.neurons)):
# If there are no provided output layer weights, assign random weights
if not output_layer_weights:
self.output_layer.neurons[o].weights.append(random.random())
# Otherwise, assign the provided weights to the output layer neurons
else:
self.output_layer.neurons[o].weights.append(output_layer_weights[weight_num])
# Increment the weight index
weight_num += 1
This function inspects and prints the structure of a neural network. It takes one argument: self, which refers to the instance of the class where the function is defined. The function prints information about the neural network, including the number of input neurons, hidden layer details, and output layer details. The inspect() method is called on both the hidden layer and output layer objects to display their respective details. Separators are printed between each section to improve readability.
def inspect(self):
# Print a separator
print('------')
# Print the number of input neurons
print('* Inputs: {}'.format(self.num_inputs))
# Print another separator
print('------')
# Print a label for the hidden layer
print('Hidden Layer')
# Call the inspect method on the hidden layer object to display its details
self.hidden_layer.inspect()
# Print another separator
print('------')
# Print a label for the output layer
print('* Output Layer')
# Call the inspect method on the output layer object to display its details
self.output_layer.inspect()
# Print a final separator
print('------')
This function performs the feed-forward process in a neural network. It takes two arguments: self and inputs. The self argument refers to the instance of the class where the function is defined, while inputs is a list of input values.