scorcard.py

from neural_network import NeuralNetwork
import numpy

# Setting up the value for the neural network
input_nodes = 784
hidden_nodes = 100
output_nodes = 10
learning_rate = 0.3

# Initializing the neural network
neural_network_object = NeuralNetwork(input_nodes, hidden_nodes,
                                       output_nodes, learning_rate)

training_file = open('mnist_train.csv', 'r')
training_list = training_file.readlines()
training_file.close()

for record in training_list:
    values = record.split(',')
    input_array = (numpy.asfarray(values[1:])/255.0*0.99)+0.01
    target = numpy.zeros(output_nodes)+0.01
    target[int(values[0])] = 0.99
    neural_network_object.train(input_array, target)

test_file = open('mnist_test.csv', 'r')
test_list = test_file.readlines()
test_file.close()

scorecard = []
def scorecard_generation(record):
    test_value = record.split(',')
    network_lable = neural_network_object.query(
                                (numpy.asfarray(test_value[1:])/255.0*0.99)+0.01)
    print "Given value:", test_value[0]
    print "Network Value:", numpy.argmax(network_lable)
    if (int(test_value[0]) == numpy.argmax(network_lable)):
        return 1
    else:
        return 0


scorecard = map(lambda record: scorecard_generation(record), test_list)
print scorecard
print len(scorecard)
print "Accuracy is :", (sum(scorecard)/10000.00)