def test_1_layer_1D_output(self):
training_input = np.array([[1], [1]])
expected_output = np.array([[1]])
flexible_neural_network = FlexibleNeuralNetwork()
flexible_neural_network.add_layer(NeuralNetworkLayer(1, 2))
loss = None
for i in range(self.training_rounds):
loss = flexible_neural_network.train([(training_input,
expected_output)])
self.log.info("Loss: {}".format(loss))
prediction = flexible_neural_network.predict(training_input)
self.log.info("Prediction, Expected: {}, {}".format(
prediction, expected_output))
np.testing.assert_almost_equal(loss, 0, 2)
def test_2_layers(self):
#training_input = np.array([[0, 0, 1],
# [0, 1, 1],
# [1, 0, 1],
# [1, 1, 1]])
training_input = np.array([[0], [0], [1], [1]])
expected_output = np.array([[0], [1], [1], [0]])
flexible_neural_network = FlexibleNeuralNetwork()
flexible_neural_network.add_layer(NeuralNetworkLayer(4, 4))
flexible_neural_network.add_layer(NeuralNetworkLayer(4, 4))
loss = None
for i in range(self.training_rounds):
loss = flexible_neural_network.train([(training_input,
expected_output)])
self.log.info("Loss: {}".format(loss))
prediction = flexible_neural_network.predict(training_input)
self.log.info("Prediction, Expected: {}, {}".format(
prediction, expected_output))