def test_backpropagation(self): weight1 = np.array([ [0.22667075, 0.38116981, 0.62686969], [1.13062085, 0.40836474, -0.50492125], [-0.22645265, 1.13541005, -2.7876409] ]) weight2 = np.array([ [0.63547163], [0.63347214], [-1.3669957], [-0.42770718] ]) input_layer = TanhLayer(2, weight=weight1) hidden_layer = TanhLayer(3, weight=weight2) output = StepOutputLayer(1, output_bounds=(-1, 1)) network = Backpropagation( input_layer > hidden_layer > output, step=0.3, zero_weight=20, optimizations=[WeightElimination] ) network.train(xor_input_train, xor_target_train, epochs=350) self.assertEqual(round(network.last_error_in(), 2), 0)
def test_backpropagation(self): output = StepOutputLayer(1, output_bounds=(-1, 1)) weight1 = np.array([ [0.31319847, -1.17858149, 0.71556407], [1.60798015, 0.16304449, -0.22483005], [-0.90144173, 0.58500625, -0.01724167] ]) weight2 = np.array([ [-1.34351428], [0.45506056], [0.24790366], [-0.74360389] ]) input_layer = TanhLayer(2, weight=weight1) hidden_layer = TanhLayer(3, weight=weight2) network = Backpropagation( (input_layer > hidden_layer > output), step=0.3, verbose=False ) network.train(xor_input_train, xor_target_train, epochs=1000) self.assertEqual(round(network.last_error_in(), 2), 0)
def test_backpropagation(self): weight1 = np.array([ [-0.53980522, -0.64724144, -0.92496063], [-0.04144865, -0.60458235, 0.25735483], [0.08818209, -0.10212516, -1.46030816] ]) weight2 = np.array([ [0.54230442], [0.1393251], [1.59479241], [0.1479949] ]) input_layer = TanhLayer(2, weight=weight1) hidden_layer = TanhLayer(3, weight=weight2) output = StepOutputLayer(1, output_bounds=(-1, 1)) network = Backpropagation( input_layer > hidden_layer > output, step=0.3, decay_rate=0.0001, optimizations=[WeightDecay] ) network.train(xor_input_train, xor_target_train, epochs=500) self.assertEqual(round(network.last_error_in(), 2), 0)
def test_backpropagation(self): weight1 = np.array([[-0.53980522, -0.64724144, -0.92496063], [-0.04144865, -0.60458235, 0.25735483], [0.08818209, -0.10212516, -1.46030816]]) weight2 = np.array([[0.54230442], [0.1393251], [1.59479241], [0.1479949]]) input_layer = TanhLayer(2, weight=weight1) hidden_layer = TanhLayer(3, weight=weight2) output = StepOutputLayer(1, output_bounds=(-1, 1)) network = Backpropagation(input_layer > hidden_layer > output, step=0.3, decay_rate=0.0001, optimizations=[WeightDecay]) network.train(xor_input_train, xor_target_train, epochs=500) self.assertEqual(round(network.last_error_in(), 2), 0)
def test_backpropagation(self): weight1 = np.array([[0.22667075, 0.38116981, 0.62686969], [1.13062085, 0.40836474, -0.50492125], [-0.22645265, 1.13541005, -2.7876409]]) weight2 = np.array([[0.63547163], [0.63347214], [-1.3669957], [-0.42770718]]) input_layer = TanhLayer(2, weight=weight1) hidden_layer = TanhLayer(3, weight=weight2) output = StepOutputLayer(1, output_bounds=(-1, 1)) network = Backpropagation(input_layer > hidden_layer > output, step=0.3, zero_weight=20, optimizations=[WeightElimination]) network.train(xor_input_train, xor_target_train, epochs=350) self.assertEqual(round(network.last_error_in(), 2), 0)