python notebook for Make Your Own Neural Network

 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.inodes, -0.5), (self.hnodes, self.inodes))     self.who = numpy.random.normal(0.0, pow(self.hnodes, -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 = 0.05  # 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("C:/Users/001/Downloads/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 = 14

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("C:/Users/001/Downloads/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 to scorecard
        scorecard.append(0)
        pass
    
    pass
# calculate the pe/rformance score, the fraction of correct answers
scorecard_array = numpy.asarray(scorecard)
print ("performance = ", scorecard_array.sum() / scorecard_array.size)

#result：
#input_nodes = 784
#hidden_nodes =200
#output_nodes = 10
#learning_rate = 0.1
#epochs = 5
#performance =  0.9734

#input_nodes = 784
#hidden_nodes =200
#output_nodes = 10
#learning_rate = 0.05
#epochs = 14
#performance =  0.9773