human
I am working on the file "DLND-your-first-network/dlnd-your-first-neural-network.ipynb".
The first task is:
Thinking about your results
Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?

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The configuration with 50 hidden neurons, 300 epochs and 0.1 learning rate is best among my trials on validation test (loss <= 0.150). Furthermore also on the test set it follows the data quite well. The most difficult days to predict are 13, 14 October and 20, 21 October, maybe special days of the week (separated by exactly one week, checking the calendar they should be thrusday and friday). This final network performs better than other trials, but it is still wrong on that days and to predict some rapid spikes (the prediction is more conservative). On the other hand it seems easy to understand the periodicity.
Comment added after review
Don't know why when I submitted the result the graph was about October and not final days of December. Probably I ran multiple times the test_features splitting, that involves a change of the original data variable. On the real test set we have errors on the last part of the year (holidays pattern not predicted).
Unit tests
Run these unit tests to check the correctness of your network implementation. These tests must all be successful to pass the project.
Can you write Python code for it?
assistant
#Here is the Python code you requested.

import unittest

inputs = [0.5, -0.2, 0.1]
targets = [0.4]
test_w_i_h = np.array([[0.1, 0.4, -0.3], 
                       [-0.2, 0.5, 0.2]])
test_w_h_o = np.array([[0.3, -0.1]])

class TestMethods(unittest.TestCase):
    
    ##########
    # Unit tests for data loading
    ##########
    
    def test_data_path(self):
        # Test that file path to dataset has been unaltered
        self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')
        
    def test_data_loaded(self):
        # Test that data frame loaded
        self.assertTrue(isinstance(rides, pd.DataFrame))
    
    ##########
    # Unit tests for network functionality
    ##########

    def test_activation(self):
        network = NeuralNetwork(3, 2, 1, 0.5)
        # Test that the activation function is a sigmoid
        self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))

    def test_train(self):
        # Test that weights are updated correctly on training
        network = NeuralNetwork(3, 2, 1, 0.5)
        network.weights_input_to_hidden = test_w_i_h.copy()
        network.weights_hidden_to_output = test_w_h_o.copy()
        
        network.train(inputs, targets)
        self.assertTrue(np.allclose(network.weights_hidden_to_output, 
                                    np.array([[ 0.37275328, -0.03172939]])))
        self.assertTrue(np.allclose(network.weights_input_to_hidden,
                                    np.array([[ 0.10562014,  0.39775194, -0.29887597],
                                              [-0.20185996,  0.50074398,  0.19962801]])))

    def test_run(self):
        # Test correctness of run method
        network = NeuralNetwork(3, 2, 1, 0.5)
        network.weights_input_to_hidden = test_w_i_h.copy()
        network.weights_hidden_to_output = test_w_h_o.copy()

        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))

suite = unittest.TestLoader().loadTestsFromModule(TestMethods())
unittest.TextTestRunner().run(suite)