def test_forward_propagation(self):
# given
network = Network(4)
network.add_layer(3, Sigmoid)
network.add_layer(1, Linear)
iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
shuffle=True)
for epoch in range(100):
for example_n in range(y_train.size):
network.set_inputs(X_train[example_n])
network.set_desired_outputs([y_train[example_n]])
network.forward_propagate()
network.backward_propagate()
for i in range(y_test.size):
network.set_inputs(X_test[i])
network.set_desired_outputs([y_test[i]])
network.forward_propagate()
print("desired output: ", y_test[i])
print("actual output:", network.get_outputs(), "\n")
self.assertEquals(1, 1)
class TestNetworkBackwardPropagation(unittest.TestCase):
def test_backward_propagate(self):
# given
self.network = Network(inputs_n=4)
self.network.add_layer(Layer(neurons_n=3, activation_f=Sigmoid))
self.network.add_layer(Layer(neurons_n=1, activation_f=Sigmoid))
self.network.compile()
iris = load_iris()
X = iris.data
# Create a variable for the target data
y = iris.target
X_train, X_test, y_train, y_test = \
train_test_split(X[:100], y[:100], test_size=0.2, shuffle=True)
# when
# for epoch in range(50):
for epoch in range(112):
for i in range(y_train.size):
inputs_x = X_train[i].T # 1 set of inputs
desired_output = y_train[i]
self.network.set_inputs(np.reshape(inputs_x, (4, 1)))
self.network.set_desired_outputs(
np.reshape(desired_output, (1, 1)))
self.network.forward_propagate()
self.network.backward_propagate()
print('a')
correct_predictions = 0
for i in range(y_test.size):
inputs_x = X_test[i].T # 1 set of inputs
desired_output = y_test[i]
self.network.set_inputs(np.reshape(inputs_x, (4, 1)))
self.network.set_desired_outputs(np.reshape(
desired_output, (1, 1)))
self.network.forward_propagate()
predicted = convert_output_to_prediction(
self.network.get_actual_outputs())
if predicted == y_test[i]:
correct_predictions += 1
# print("inputs: ", self.network.inputs_x)
print("output predicted: ", self.network.get_actual_outputs())
print("predicted: ", predicted)
print("actual: ", y_test[i], "\n")
print("correct predictions: ", correct_predictions)
from activation_function.Linear import Linear
from activation_function.Sigmoid import Sigmoid
from model.Network import Network
network = Network(2)
network.add_layer(3, Sigmoid)
network.add_layer(1, Sigmoid)
network.set_inputs([-2, 0])
network.set_desired_outputs([2.4])
network.set_all_weights_to(1)
network.forward_propagate()
print("outputs", network.get_outputs())
network.backward_propagate()
network.forward_propagate()
print("outputs", network.get_outputs())