def test_correct_shape_for_biases(self):
params = prop.initialize_params(self.layer_dims)
for l in range(1, self.num_dims):
with self.subTest(l = l):
self.assertEqual(
params['b' + str(l)].shape,
(self.layer_dims[l], 1)
)
def learn_generator(self, X, Y, iterations=3000, progress=1):
"""Train the model. This generator function yields the model and cost
after each count of iterations given by `progress`.
Parameters
----------
X : ndarray
Input data of shape (input size, number of examples).
Y : ndarray
Vector of true values of shape (1, number of examples).
iterations : int
Maximum number of complete cycles of forward and back propagation to
train the model.
progress : int in [0, iterations]
If non-zero, provide progress after each successive increment of the
given number of iterations.
Returns
-------
tuple of (int, ndarray, list)
i : int
Number of completed iterations.
params : dict of {str: ndarray}
Current parameters for the trained model.
`Wl` : ndarray
Current weights matrix.
`bl` : ndarray
Current biases vector.
cost : list
The cost computed for the current iteration of training.
"""
# TODO: Throw error if progress < 0 or progress > iterations.
params = prop.initialize_params(self.layer_dims)
for i in range(1, iterations + 1):
Y_hat, caches = prop.model_forward(X, params)
cost = prop.compute_cost(Y_hat, Y)
grads = prop.model_backward(Y_hat, Y, caches)
params = prop.update_params(params, grads, self.learning_rate)
self._params = params
if progress and (i == 1 or i % progress == 0):
yield (i, params, cost)
def test_biases_are_zero(self):
params = prop.initialize_params(self.layer_dims)
for l in range(1, self.num_dims):
with self.subTest(l = l):
self.assertFalse(params['b' + str(l)].any())
def test_weights_are_not_zero(self):
params = prop.initialize_params(self.layer_dims)
for l in range(1, self.num_dims):
with self.subTest(l = l):
self.assertTrue(params['W' + str(l)].all())
def test_returns_correct_number_of_params(self):
number_of_params = 2 * (self.num_dims - 1)
params = prop.initialize_params(self.layer_dims)
self.assertEqual(len(params), number_of_params)