artificial neural network
(Artificial Neural Network, ANN) is a mathematical model that imitates the structure and function of biological neural networks, and its purpose is to be able to carry out complex nonlinear mapping relationships and realize adaptive intelligent decision-making when dealing with unknown input data through learning and training. It can be said that ANN is one of the most basic and core algorithms in artificial intelligence algorithms.
The basic structure of ANN model contains input layer, hidden layer and output layer. The input layer receives the input data, the hidden layer is responsible for transforming and processing the data in multiple levels and high dimensions, and the output layer outputs the processed data.The training process of ANN is to continuously adjust the weights of each layer in the neural network through multiple iterations, so that the neural network can correctly predict and classify the input data.
Examples of artificial neural network algorithms
Next look at a simple example of an artificial neural network algorithm:
import numpy as np
class NeuralNetwork():
def __init__(self, layers):
"""
layers: an array containing the number of neurons in each layer, e.g. [2, 3, 1] means a 3-layer neural network with 2 neurons in the first layer, 3 neurons in the second layer and 1 neuron in the third layer.
weights: an array containing the weights matrix for each connection, default values are randomized.
biases: array, contains the biases of each layer, default value is 0.
"""
= layers
= [(a, b) for a, b in zip(layers[1:], layers[:-1])]
= [((a, 1)) for a in layers[1:]]
def sigmoid(self, z):
"""Sigmoid activation function."""
return 1 / (1 + (-z))
def forward_propagation(self, a):
"""Forward propagation..."""
for w, b in zip(, ):
z = (w, a) + b
a = (z)
return a
def backward_propagation(self, x, y):
"""Reverse propagation..."""
nabla_w = [() for w in ]
nabla_b = [() for b in ]
a = x
activations = [x]
zs = []
for w, b in zip(, ):
z = (w, a) + b
(z)
a = (z)
(a)
delta = self.cost_derivative(activations[-1], y) * self.sigmoid_prime(zs[-1])
nabla_b[-1] = delta
nabla_w[-1] = (delta, activations[-2].transpose())
for l in range(2, len()):
z = zs[-l]
sp = self.sigmoid_prime(z)
delta = ([-l+1].transpose(), delta) * sp
nabla_b[-l] = delta
nabla_w[-l] = (delta, activations[-l-1].transpose())
return (nabla_w, nabla_b)
def train(self, x_train, y_train, epochs, learning_rate):
"""Training networks..."""
for epoch in range(epochs):
nabla_w = [() for w in ]
nabla_b = [() for b in ]
for x, y in zip(x_train, y_train):
delta_nabla_w, delta_nabla_b = self.backward_propagation(([x]).transpose(), ([y]).transpose())
nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
= [w-(learning_rate/len(x_train))*nw for w, nw in zip(, nabla_w)]
= [b-(learning_rate/len(x_train))*nb for b, nb in zip(, nabla_b)]
def predict(self, x_test):
"""Prediction..."""
y_predictions = []
for x in x_test:
y_predictions.append(self.forward_propagation(([x]).transpose())[0][0])
return y_predictions
def cost_derivative(self, output_activations, y):
"""The derivative of the loss function."""
return output_activations - y
def sigmoid_prime(self, z):
"""The derivative of a Sigmoid function."""
return (z) * (1 - (z))
Use the following code example to instantiate and use this simple neural network class:
x_train = [[0, 0], [1, 0], [0, 1], [1, 1]]
y_train = [0, 1, 1, 0]
# Create neural networks
nn = NeuralNetwork([2, 3, 1])
# Train the neural network
(x_train, y_train, 10000, 0.1)
# Testing neural networks
x_test = [[0, 0], [1, 0], [0, 1], [1, 1]]
y_test = [0, 1, 1, 0]
y_predictions = (x_test)
print("Predictions:", y_predictions)
print("Actual:", y_test)
Output results:
Predictions: [0.011602156431658403, 0.9852717774725432, 0.9839448924887225, 0.020026540429992387]
Actual: [0, 1, 1, 0]
Summary:
Pros:
- Strong nonlinear capability: ANN is able to deal with nonlinear relationships and can recognize and predict data with nonlinear relationships, such as images and speech
- Highly adaptive: ANNs are able to automatically learn data features and adjust them, and can adapt to changes in the environment
- Strong learning ability: ANN is able to predict and categorize unknown data by learning from a large amount of data
- Parallelizable processing: ANN's computational processes can be processed in parallel, enabling large amounts of data to be processed in a short period of time.
- High error tolerance: ANN allows for a certain amount of error in its calculations and can tolerate some data distortion and loss
Drawbacks:
- High data demand: in order to improve prediction and classification accuracy, ANN needs a large amount of data for learning and training, and if the amount of data is insufficient, prediction and classification accuracy will decrease
- Computationally intensive: ANN is computationally intensive and requires powerful computing devices for processing
- Difficulty in parameter setting: each level of ANN has multiple parameters, different parameters need to be set according to different data requirements, and choosing the optimal parameters requires a lot of trials and tests
- Poor interpretability: due to its very complex internal structure, the results of ANN are difficult to interpret and it is difficult to know how decisions are arrived at
- Overfitting phenomenon: in order to achieve high accuracy, ANNs are likely to rely on features that are too detailed, resulting in lower generalization performance to new data
Currently, Artificial Neural Networks (ANNs) have achieved important application results in the fields of image recognition, speech recognition, natural language processing, machine translation and so on. As a highly flexible and powerful artificial intelligence algorithm, ANN has a broad application prospect in the future development. Well, about artificial neural network is briefly introduced here, I hope it will be helpful to you!
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