def test_update_parameters(self):
layer = Dense(10, sigmoid)
layer.build(20)
learning_rate = 0.1
dW = np.random.randn(10, 20)
db = np.random.randn(10, 1)
layer.update_gradients(dW, db)
new_dW = layer.dW - learning_rate * dW
new_db = layer.db - learning_rate * db
layer.update_parameters(learning_rate)
self.assertEqual(layer.dW.all(), new_dW.all())
self.assertEqual(layer.db.all(), new_db.all())
def test_layer_output_shape_after_forward_activation(self):
layer = Dense(10, sigmoid)
layer.build(20)
layer.forward_activation(np.random.randn(20, 35))
self.assertEqual(layer.A.shape, (10, 35))
def test_b_shape_after_build(self):
layer = Dense(10, sigmoid)
layer.build(20)
self.assertEqual(layer.b.shape, (10, 1))
