《Python神经网络编程》——用Python制作神经网络

以下代码来自于《Python神经网络编程》

mnist_train.csv和mnist_test.csv来自于https://pjreddie.com/projects/mnist-in-csv/

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# python notebook for Make Your Own Neural Network
# code for a 3-layer neural network, and code for learning the MNIST dataset
# (c) Tariq Rashid, 2016# license is GPLv2
import numpy
# scipy.special for the sigmoid function expit()
import scipy.special
# library for plotting arrays
import matplotlib.pyplot
# ensure the plots are inside this notebook, not an external window
%matplotlib inline
# neural network class definition
class neuralNetwork :
# initialise the neural network
	def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate) :
# set number of nodes in each input, hidden, output layer
		self.inodes = inputnodes
		self.hnodes = hiddennodes
		self.onodes = outputnodes
# link weight matrices, wih and who
# weights inside the arrays are w_i_j, where link is from node i to node j in the next layer
# w11 w21
# w12 w22 etc
		self.wih = numpy.random.normal(0.0, pow(self.hnodes, -0.5),(self.hnodes, self.inodes))
		self.who = numpy.random.normal(0.0, pow(self.onodes, -0.5),(self.onodes, self.hnodes))
# learning rate
		self.lr = learningrate# activation function is the sigmoid function
		self.activation_function = lambda x: scipy.special.expit(x)
		pass
# train the neural network
	def train(self, inputs_list, targets_list) :
# convert inputs list to 2d array
		inputs = numpy.array(inputs_list, ndmin=2).T
		targets = numpy.array(targets_list, ndmin=2).T
# calculate signals into hidden layer
		hidden_inputs = numpy.dot(self.wih, inputs)
# calculate the signals emerging from hidden layer
		hidden_outputs = self.activation_function(hidden_inputs)
# calculate signals into final output layer
		final_inputs = numpy.dot(self.who, hidden_outputs)
# calculate the signals emerging from final output layer
		final_outputs = self.activation_function(final_inputs)
# output layer error is the (target - actual)
		output_errors = targets - final_outputs
# hidden layer error is the output_errors, split by weights, recombined at hidden nodes
		hidden_errors = numpy.dot(self.who.T, output_errors)
# update the weights for the links between the hidden and output layers
		self.who += self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)), numpy.transpose(hidden_outputs))
# update the weights for the links between the input and hidden layers
		self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)), numpy.transpose(inputs))
		pass
# query the neural network
	def query(self, inputs_list) :
# convert inputs list to 2d array
		inputs = numpy.array(inputs_list, ndmin=2).T
# calculate signals into hidden layer
		hidden_inputs = numpy.dot(self.wih, inputs)
# calculate the signals emerging from hidden layer
		hidden_outputs = self.activation_function(hidden_inputs)
# calculate signals into final output layer
		final_inputs = numpy.dot(self.who, hidden_outputs)
# calculate the signals emerging from final output layer
		final_outputs = self.activation_function(final_inputs)
		return final_outputs
# number of input, hidden and output nodes
input_nodes = 784
hidden_nodes = 200
output_nodes = 10
# learning rate
learning_rate = 0.1
# create instance of neural network
n = neuralNetwork(input_nodes,hidden_nodes,output_nodes, learning_rate)
# load the mnist training data CSV file into a list
training_data_file = open("F:/Book_Learning/Python神经网络编程/mnist_dataset/mnist_train.csv", 'r')
training_data_list = training_data_file.readlines()
training_data_file.close()
# train the neural network
# epochs is the number of times the training data set is used for training
epochs = 5
for e in range(epochs):# go through all records in the training data set
	for record in training_data_list:
# split the record by the ',' commas
		all_values = record.split(',')
# scale and shift the inputs
		inputs = (numpy.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
# create the target output values (all 0.01, except the desired label which is 0.99)
		targets = numpy.zeros(output_nodes) + 0.01
# all_values[0] is the target label for this record
		targets[int(all_values[0])] = 0.99
		n.train(inputs, targets)
		pass
	pass
# load the mnist test data CSV file into a list
test_data_file = open("F:/Book_Learning/Python神经网络编程/mnist_dataset/mnist_test.csv", 'r')
test_data_list = test_data_file.readlines()
test_data_file.close()
# test the neural network
# scorecard for how well the network performs, initially empty
scorecard = []
# go through all the records in the test data set
for record in test_data_list:
# split the record by the ',' commas
	all_values = record.split(',')
# correct answer is first value
	correct_label = int(all_values[0])
# scale and shift the inputs
	inputs = (numpy.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
# query the network
	outputs = n.query(inputs)
# the index of the highest value corresponds to the label
	label = numpy.argmax(outputs)
# append correct or incorrect to list
	if (label == correct_label):
# network's answer matches correct answer, add 1 to scorecard
		scorecard.append(1)
	else:
# network's answer doesn't match correct answer, add 0 toscorecard
		scorecard.append(0)
		pass
	pass
# calculate the performance score, the fraction of correct answers
scorecard_array = numpy.asarray(scorecard)
print ("performance = ", scorecard_array.sum() / scorecard_array.size)