コード例 #1
0
    def executeTest(self, indexRange, networkFile):
        inputVectors = [InputVectorParser(COMMON_DIR_PREFIX + LEARNING_VECTOR_FILE_PREFIX + str(i) + '.xml').parse() for i in indexRange]
        network = NetParser(COMMON_DIR_PREFIX + networkFile).parse(lambda : random.uniform(-1.0, 1.0))
        speed = 0.5
        momentum = 0.15
        iterations = 5000

        network.backpropagation_learn(inputVectors, speed, iterations, momentum)

        inputVectors = [InputVectorParser(COMMON_DIR_PREFIX + LEARNING_VECTOR_FILE_PREFIX + str(i) + '.xml').parse() for i in range(1, 9)]
        for inputVector in inputVectors :
            self.printResult(network, inputVector)
コード例 #2
0
 def test_backpropagation(self):
     input_vectors = [InputVectorParser(COMMON_DIR_PREFIX + LEARNING_VECTOR_FILE_PREFIX + str(i) + '.xml').parse() for i in range(1, 4)]
     for input_vec in input_vectors:
         print(input_vec)
     #testVectors = [InputVectorParser(COMMON_DIR_PREFIX + TEST_VECTOR_FILE_PREFIX + str(i) + '.xml').parse() for i in range(1, 4)]
     network = NetParser(COMMON_DIR_PREFIX + NETWORK_FILE).parse(lambda : random.uniform(-1.0, 1.0))
     learning_rate = 0.9
     momentum = 0.6
     iterations = 5000
     network.backpropagation_learn(input_vectors, learning_rate, iterations, momentum)
     print(network)
     for inputVector in input_vectors :
         self.print_results(network, inputVector)
コード例 #3
0
 def test_backpropagation(self):
     input_vectors = [InputVectorParser(COMMON_DIR_PREFIX + LEARNING_VECTOR_FILE_PREFIX + str(i) + '.xml').parse() for i in range(1, 4)]
     for input_vec in input_vectors:
         print(input_vec)
     #testVectors = [InputVectorParser(COMMON_DIR_PREFIX + TEST_VECTOR_FILE_PREFIX + str(i) + '.xml').parse() for i in range(1, 4)]
     network = NetParser(COMMON_DIR_PREFIX + NETWORK_FILE).parse(lambda : random.uniform(-1.0, 1.0))
     learning_rate = 0.5
     momentum = 0.2
     iterations = 5000
     network.backpropagation_learn(input_vectors, learning_rate, iterations, momentum)
     print(network)
     error = 0.0
     for inputVector in input_vectors :
         inputVector.printVector(3)
         outputDict = []
         answer = network.calculte_answer(inputVector, False, outputDict)
         print(answer)
         for id, val in outputDict:
             error = error + 0.5*(inputVector.expected_value_dict[id]-val)**2
     print(error)
コード例 #4
0
ファイル: main.py プロジェクト: krzysztofK/NeuralNetworks
    weight_function = None
    if args.random:
        print(
            "Weight of links between nodes will be set to random values from range [{}; {}]".format(
                args.random[0], args.random[1]
            )
        )
        weight_function = lambda: random.uniform(args.random[0], args.random[1])
    elif args.zeros:
        print("Weight of links between nodes will be set to 0.")
        weight_function = lambda: 0.0
    network = NetParser(args.network).parse(weight_function)
    if args.vector_limits:
        input_vector = RandomInputVectorFactory.create_new(
            args.vector_limits[0], args.vector_limits[1], network.layers[0].get_nodes_ids()
        )
        print(
            "Generated vector with values from range [{}; {}]:\n{}".format(
                args.vector_limits[0], args.vector_limits[1], input_vector
            )
        )
    else:
        input_vectors = [InputVectorParser(vector).parse() for vector in args.vector_file]
    if args.kohonen:
        network.learning_process(input_vectors, args.coefficient, args.coefficient_half_life, args.turns)
    if args.backpropagation:
        network.backpropagation_learn(input_vectors, args.coefficient, args.turns)
    else:
        print("Network response is:")
        print(network.calculte_answer(input_vectors[0]))