6
我想了解神经网络，并编写了一个简单的反向传播神经网络，它使用S形激活函数，随机权重初始化和学习/梯度动量。简单的神经网络无法学习异或
当配置2个输入，2个隐藏节点和1时，它无法学习XOR和AND。但是，它会正确学习OR。
我看不出有什么我做错了，因此任何帮助，将不胜感激。
由于
编辑：如上所述，我用2个隐藏节点测试，但下面的代码示出为3的配置我简单忘记改变这种回2使用3-隐藏节点运行测试之后。
network.rb：
module Neural
class Network
attr_accessor :num_inputs, :num_hidden_nodes, :num_output_nodes, :input_weights, :hidden_weights, :hidden_nodes,
:output_nodes, :inputs, :output_error_gradients, :hidden_error_gradients,
:previous_input_weight_deltas, :previous_hidden_weight_deltas
def initialize(config)
initialize_input(config)
initialize_nodes(config)
initialize_weights
end
def initialize_input(config)
self.num_inputs = config[:inputs]
self.inputs = Array.new(num_inputs+1)
self.inputs[-1] = -1
end
def initialize_nodes(config)
self.num_hidden_nodes = config[:hidden_nodes]
self.num_output_nodes = config[:output_nodes]
# treat threshold as an additional input/hidden node with no incoming inputs and a value of -1
self.output_nodes = Array.new(num_output_nodes)
self.hidden_nodes = Array.new(num_hidden_nodes+1)
self.hidden_nodes[-1] = -1
end
def initialize_weights
# treat threshold as an additional input/hidden node with no incoming inputs and a value of -1
self.input_weights = Array.new(hidden_nodes.size){Array.new(num_inputs+1)}
self.hidden_weights = Array.new(output_nodes.size){Array.new(num_hidden_nodes+1)}
set_random_weights(input_weights)
set_random_weights(hidden_weights)
self.previous_input_weight_deltas = Array.new(hidden_nodes.size){Array.new(num_inputs+1){0}}
self.previous_hidden_weight_deltas = Array.new(output_nodes.size){Array.new(num_hidden_nodes+1){0}}
end
def set_random_weights(weights)
(0...weights.size).each do |i|
(0...weights[i].size).each do |j|
weights[i][j] = (rand(100) - 49).to_f/100
end
end
end
def calculate_node_values(inputs)
inputs.each_index do |i|
self.inputs[i] = inputs[i]
end
set_node_values(self.inputs, input_weights, hidden_nodes)
set_node_values(hidden_nodes, hidden_weights, output_nodes)
end
def set_node_values(values, weights, nodes)
(0...weights.size).each do |i|
nodes[i] = Network::sigmoid(values.zip(weights[i]).map{|v,w| v*w}.inject(:+))
end
end
def predict(inputs)
calculate_node_values(inputs)
output_nodes.size == 1 ? output_nodes[0] : output_nodes
end
def train(inputs, desired_results, learning_rate, momentum_rate)
calculate_node_values(inputs)
backpropogate_weights(desired_results, learning_rate, momentum_rate)
end
def backpropogate_weights(desired_results, learning_rate, momentum_rate)
output_error_gradients = calculate_output_error_gradients(desired_results)
hidden_error_gradients = calculate_hidden_error_gradients(output_error_gradients)
update_all_weights(inputs, desired_results, hidden_error_gradients, output_error_gradients, learning_rate, momentum_rate)
end
def self.sigmoid(x)
1.0/(1 + Math::E**-x)
end
def self.dsigmoid(x)
sigmoid(x) * (1 - sigmoid(x))
end
def calculate_output_error_gradients(desired_results)
desired_results.zip(output_nodes).map{|desired, result| (desired - result) * Network::dsigmoid(result)}
end
def reversed_hidden_weights
# array[hidden node][weights to output nodes]
reversed = Array.new(hidden_nodes.size){Array.new(output_nodes.size)}
hidden_weights.each_index do |i|
hidden_weights[i].each_index do |j|
reversed[j][i] = hidden_weights[i][j];
end
end
reversed
end
def calculate_hidden_error_gradients(output_error_gradients)
reversed = reversed_hidden_weights
hidden_nodes.each_with_index.map do |node, i|
Network::dsigmoid(hidden_nodes[i]) * output_error_gradients.zip(reversed[i]).map{|error, weight| error*weight}.inject(:+)
end
end
def update_all_weights(inputs, desired_results, hidden_error_gradients, output_error_gradients, learning_rate, momentum_rate)
update_weights(hidden_nodes, inputs, input_weights, hidden_error_gradients, learning_rate, previous_input_weight_deltas, momentum_rate)
update_weights(output_nodes, hidden_nodes, hidden_weights, output_error_gradients, learning_rate, previous_hidden_weight_deltas, momentum_rate)
end
def update_weights(nodes, values, weights, gradients, learning_rate, previous_deltas, momentum_rate)
weights.each_index do |i|
weights[i].each_index do |j|
delta = learning_rate * gradients[i] * values[j]
weights[i][j] += delta + momentum_rate * previous_deltas[i][j]
previous_deltas[i][j] = delta
end
end
end
end
end
test.rb：
#!/usr/bin/ruby
load "network.rb"
learning_rate = 0.3
momentum_rate = 0.2
nn = Neural::Network.new(:inputs => 2, :hidden_nodes => 3, :output_nodes => 1)
10000.times do |i|
# XOR - doesn't work
nn.train([0, 0], [0], learning_rate, momentum_rate)
nn.train([1, 0], [1], learning_rate, momentum_rate)
nn.train([0, 1], [1], learning_rate, momentum_rate)
nn.train([1, 1], [0], learning_rate, momentum_rate)
# AND - very rarely works
# nn.train([0, 0], [0], learning_rate, momentum_rate)
# nn.train([1, 0], [0], learning_rate, momentum_rate)
# nn.train([0, 1], [0], learning_rate, momentum_rate)
# nn.train([1, 1], [1], learning_rate, momentum_rate)
# OR - works
# nn.train([0, 0], [0], learning_rate, momentum_rate)
# nn.train([1, 0], [1], learning_rate, momentum_rate)
# nn.train([0, 1], [1], learning_rate, momentum_rate)
# nn.train([1, 1], [1], learning_rate, momentum_rate)
end
puts "--- TESTING ---"
puts "[0, 0]"
puts "result "+nn.predict([0, 0]).to_s
puts
puts "[1, 0]"
puts "result "+nn.predict([1, 0]).to_s
puts
puts "[0, 1]"
puts "result "+nn.predict([0, 1]).to_s
puts
puts "[1, 1]"
puts "result "+nn.predict([1, 1]).to_s
puts
该行将正确答案与错误分隔开。如果你明白，你可以写OR。 XOR将是一个麻烦。
我会通过设置一个完整的测试用例来开始调试，这个用例包括两个例子的初始权重和导数/错误，然后进入代码。 – Motasim
你应该真的把它简化为解释问题的最小*代码。你在这里有几乎是一个完整的应用程序。 – tadman
你的程序看起来可能会做一些有趣的事情。但是每种语言都有自己的约定，而Ruby的部分功能就是其简洁。您可能会将此问题带到codereview.stackexchange.com。 –