def _get_cascade(self, net):
     net._output_connections[0][0] = 2.24107584
     net._output_connections[0][1] = -0.00060484
     net._output_connections[0][2] = -2.24107583
     net._output_connections[0][3] = -2.24107584
     print_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
     return net._generate_candidate(DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
 def _get_cascade(self, net):
     net._output_connections[0][0] = 2.24107584
     net._output_connections[0][1] = -0.00060484
     net._output_connections[0][2] = -2.24107583
     net._output_connections[0][3] = -2.24107584
     print_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
     return net._generate_candidate(DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
    def test_correlation_maximize(self):
        net = self.CLASS(2, 1)
        net.use_quick_prop = True
        net._output_connections[0][0] = -1.7
        net._output_connections[0][1] = 1.7
        net._output_connections[0][2] = -1.7

        print_results(net, XOR_INPUTS, XOR_TARGETS)
        # the error will be large for -1.0, 1.0 but small for everything else

        winner = net._generate_candidate(XOR_INPUTS, XOR_TARGETS)
        print(winner.activations)
        self.assertAlmostEqual(winner.activations[2], max(winner.activations), delta=0.1)
Пример #4
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    def test_double_xor_cascading(self):
        net = self.CLASS(3, 1, num_candidate_nodes=8)
        net.learn_rate = 0.15

        error = net.get_error(DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
        final_error = net.train(DOUBLE_XOR_INPUTS,
                                DOUBLE_XOR_TARGETS,
                                mini_batch_size=3,
                                max_hidden_nodes=10,
                                stop_error_threshold=1.0,
                                max_iterations_per_epoch=100)

        print(len(net._cascade_connections))
        print_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
        self.assert_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
Пример #5
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    def test_correlation_maximize(self):
        net = self.CLASS(2, 1)
        net.use_quick_prop = True
        net._output_connections[0][0] = -1.7
        net._output_connections[0][1] = 1.7
        net._output_connections[0][2] = -1.7

        print_results(net, XOR_INPUTS, XOR_TARGETS)
        # the error will be large for -1.0, 1.0 but small for everything else

        winner = net._generate_candidate(XOR_INPUTS, XOR_TARGETS)
        print(winner.activations)
        self.assertAlmostEqual(winner.activations[2],
                               max(winner.activations),
                               delta=0.1)
Пример #6
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    def test_xor_cascading(self):
        net = self.CLASS(2, 1)
        net.momentum_coefficent = 0.7
        net.learn_rate = 0.2
        error = net.get_error(XOR_INPUTS, XOR_TARGETS)
        final_error = net.train(XOR_INPUTS,
                                XOR_TARGETS,
                                mini_batch_size=1,
                                max_hidden_nodes=10,
                                stop_error_threshold=0.5,
                                max_iterations_per_epoch=50)

        print_results(net, XOR_INPUTS, XOR_TARGETS)

        self.assert_results(net, XOR_INPUTS, XOR_TARGETS)
        self.assertGreater(error, final_error)
    def test_double_xor_cascading(self):
        net = self.CLASS(3, 1, num_candidate_nodes=8)
        net.learn_rate = 0.15

        error = net.get_error(DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
        final_error = net.train(
            DOUBLE_XOR_INPUTS,
            DOUBLE_XOR_TARGETS,
            mini_batch_size=3,
            max_hidden_nodes=10,
            stop_error_threshold=1.0,
            max_iterations_per_epoch=100,
        )

        print(len(net._cascade_connections))
        print_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
        self.assert_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)
    def test_xor_cascading(self):
        net = self.CLASS(2, 1)
        net.momentum_coefficent = 0.7
        net.learn_rate = 0.2
        error = net.get_error(XOR_INPUTS, XOR_TARGETS)
        final_error = net.train(
            XOR_INPUTS,
            XOR_TARGETS,
            mini_batch_size=1,
            max_hidden_nodes=10,
            stop_error_threshold=0.5,
            max_iterations_per_epoch=50,
        )

        print_results(net, XOR_INPUTS, XOR_TARGETS)

        self.assert_results(net, XOR_INPUTS, XOR_TARGETS)
        self.assertGreater(error, final_error)
    def test_generate_candidate(self):
        net = self.CLASS(3, 1)
        # this test keeps failing unless I set candidates to a very high number
        # But I can't see a bug in the code...
        net.num_candidate_nodes = 20
        net._output_connections[0][0] = 2.24107584
        net._output_connections[0][1] = -0.00060484
        net._output_connections[0][2] = -2.24107583
        net._output_connections[0][3] = -2.24107584

        print_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)

        winner = net._generate_candidate(DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)

        print(winner.activations)
        print(winner.weights)
        # This does not always hold true...
        self.assertAlmostEqual(winner.activations[4], max(winner.activations), delta=0.1)
Пример #10
0
    def test_generate_candidate(self):
        net = self.CLASS(3, 1)
        # this test keeps failing unless I set candidates to a very high number
        # But I can't see a bug in the code...
        net.num_candidate_nodes = 20
        net._output_connections[0][0] = 2.24107584
        net._output_connections[0][1] = -0.00060484
        net._output_connections[0][2] = -2.24107583
        net._output_connections[0][3] = -2.24107584

        print_results(net, DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)

        winner = net._generate_candidate(DOUBLE_XOR_INPUTS, DOUBLE_XOR_TARGETS)

        print(winner.activations)
        print(winner.weights)
        # This does not always hold true...
        self.assertAlmostEqual(winner.activations[4],
                               max(winner.activations),
                               delta=0.1)