A generic implementation of a Neural Network using a backpropagation algorithm that can be used for any kind for vector-represented data.
There are a few things you are going to have to install before using this:
- Python 3.x
- Numpy/SciPy
- Scikit-Learn - This is only used for the svm() class. The BP class runs without this.
-
Backpropagator.Input- The class used for feeding training data to the neural network.
Instantiate anInputobject with:Input(input_vector_as_numpy-array, label)
-
Backpropagator.BP- The class containing the Neural Network. ABPobject is instantiated with the statement:BP(num_of_hidden_units, dimensions, num_loops, bias)
If you want to create a
BPobject based on existing weights, use:BP(num_of_hidden_units, dimensions, num_loops, bias, v_vector, W_weight_matrix)
-
Backpropagator.OVAClassifier- A class used for implementing a One-vs-all multi-class classification algorithm.
AnOVAClassifieris instantiated with the statement:Backpropagator.OVAClassifier(hidden_units, dimensions, num_loops, gradient_corrective_step)
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Generic_OVA.OVA- A class containing a generic implementation of a One-vs-All classifier. UnlikeOVAClassifier, this is intended to be used if you have written your own predictor from scratch. AnOVAobject is instantiated with the statement:Generic_OVA.OVA()
Any great README should have a good exmple of how to use the tools. So here is one that I wrote to train a neural network for recognizing numbers using the USPS Dataset.
import random
import io
import numpy as np
import Backpropagator
def getInputs(path, num_to_classify):
training_file = open(path, "r")
raw_training_data = np.loadtxt(training_file).tolist()
random.shuffle(raw_training_data)
num_data = []
not_num_data = []
training_data = []
for data in raw_training_data:
training_array = data[1:]
training_label = data[0]
if training_label == num_to_classify:
num_data.append(Backpropagator.Input(training_array, 1))
else:
not_num_data.append(Backpropagator.Input(training_array, -1))
for num in range(0,len(num_data)):
training_data.append(num_data[num])
training_data.append(not_num_data[num])
return training_data
def start():
ova = Backpropagator.OVAClassifier(15, 257, 20, nu=0.1)
for num in range(0, 10):
training_data = getInputs("usps.train", num)
backprop = Backpropagator.BP(15, 257, 20, bias=1)
backprop.train(training_data, 0.2)
ova.add_class_from_predictor(backprop, num)
test(ova)
def test(multiclass):
test_file = open("usps.test", "r")
text_lines = test_file.readlines()
lines = []
for num in range(0, 1000):
lines.append(np.loadtxt(io.StringIO(text_lines[num])))
true = 0
false = 0
predictions = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
labels = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
for num in range(0, 1000):
line = lines[num]
inVec = line[1:]
result = multiclass.predict(np.array(inVec))
predictions[int(result)] += 1
labels[int(line[0])] += 1
if result == line[0]:
true += 1
else:
false += 1
for index in range(0, 10):
print("Number of " + index.__str__() + "s predicted: " + predictions[index].__str__())
print(" Vs: "+ labels[index].__str__() + " real ones")
print()
print("Number of Accurate Estimates: " + true.__str__())
print("Number of Errors: " + false.__str__())
start()