# load the mnist training data CSV file into a list
training_data_file = open("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("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 to scorecard
    scorecard.append(0)
    pass

pass