def setUp(self):
self.testmodel = model.Model()
self.learning_rate = 0.08
self.layer_dims = [1, 10, 1]
self.params = {'W1': np.ones((10, 1)), 'b1': np.ones((10, 1))}
self.datafile = io.BytesIO()
with h5.File(self.datafile, 'w') as df:
df.create_dataset("learning_rate", data=self.learning_rate)
df.create_dataset("layer_dims", data=self.layer_dims)
df.create_dataset("params/W1", data=self.params['W1'])
df.create_dataset("params/b1", data=self.params['b1'])
def setUp(self):
self.testmodel = model.Model()
self.X = np.ones((1, 1))
self.Y = np.ones((1, 1))
self.iterations = 100
self.interval = 10
def test_all_layers_must_be_greater_than_zero(self):
testmodel = model.Model()
with self.assertRaises(AssertionError):
testmodel.layer_dims = [0, 0]
def test_learning_rate_is_less_than_or_equal_to_one(self):
testmodel = model.Model()
with self.assertRaises(AssertionError):
testmodel.learning_rate = 2
def test_learning_rate_is_greater_than_zero(self):
testmodel = model.Model()
with self.assertRaises(AssertionError):
testmodel.learning_rate = 0
def test_has_a_sensible_default_for_layer_dims(self):
testmodel = model.Model()
self.assertTrue(testmodel.layer_dims == [1, 1])
def setUp(self):
self.testmodel = model.Model()
self.datafile = io.BytesIO()
def test_has_a_sensible_default_for_learning_rate(self):
testmodel = model.Model()
self.assertTrue(testmodel.learning_rate == 0.0075)