def update_step(x_batch, y_batch, model, learning_rate):
"""Performs on single update step, (i.e. forward then backward).
Args:
x_batch(numpy.ndarray): input data of dimension (N, ndims).
y_batch(numpy.ndarray): label data of dimension (N, 1).
model(LinearModel): Initialized linear model.
"""
f = LinearRegression.forward(model, x_batch)
grad = learning_rate * LinearRegression.backward(model, f, y_batch)
model.w = model.w - learning_rate * grad
def test_input_output(self):
model = LinearRegression(10)
x = np.zeros([4, 10])
y = np.zeros([4, ])
# Check forward shape.
f = model.forward(x)
self.assertEqual(f.shape, (4,))
# Check backward shape.
gradient = model.backward(f, y)
self.assertEqual(gradient.shape, (11,))
# Check loss shape.
loss = model.loss(f, y)
self.assertEqual(loss.shape, ())
