def test_OneNeuronGradient(self):
layer = Linear(2, 1)
x = np.random.rand(2)
y = layer.forward(x)
deriv_grad = layer.backward(np.ones(1))
numgrad = numerical_gradient.calc(layer.forward, x)
numerical_gradient.assert_are_similar(deriv_grad, numgrad[0])
def test_compare_with_Linear(self):
in_size = 2
out_size = 3
x = np.random.rand(in_size)
# x = np.array([1., 1])
optimizer = SGD(0.1)
linear = Linear(in_size, out_size, initialize='zeros')
wx = Wx(in_size, out_size, initialize='zeros')
plusbias = PlusBias(out_size, initialize='zeros')
wxbias = Seq(wx, plusbias)
linear_y = linear.forward(x)
wxbias_y = wxbias.forward(x)
assert_array_equal(linear_y, wxbias_y)
dJdy = np.random.rand(out_size)
linear_grad = linear.backward(dJdy)
wxbias_grad = wxbias.backward(dJdy)
assert_array_equal(linear_grad, wxbias_grad)
linear.update_weights(optimizer)
wxbias.update_weights(optimizer)
stack = np.vstack([plusbias.b.get(), wx.W.get().T]).T
assert_array_equal(linear.W, stack)
def test_TwoNeuronsGradient(self):
layer = Linear(3, 2)
x = np.random.rand(3)
y = layer.forward(x)
deriv_grad = layer.backward(np.ones(2))
numgrad = numerical_gradient.calc(layer.forward, x)
numgrad = np.sum(numgrad, axis=0)
numerical_gradient.assert_are_similar(deriv_grad, numgrad)
def test_OneNeuronBackward(self):
layer = Linear(2, 1, initialize='ones')
x = np.array([2., 2.])
y = layer.forward(x)
self.assertEqual(y, [5.])
dJdy = np.array([3])
dxdy = layer.backward(dJdy)
assert_array_equal(dxdy, [3., 3.])
def test_OneNeuronUpdateGradient(self):
layer = Linear(2, 1, initialize='ones')
x = np.array([2., 2.])
y = layer.forward(x)
self.assertEqual(y, [5.])
dJdy = np.array([3])
dxdy = layer.backward(dJdy)
assert_array_equal(dxdy, [3., 3.])
update_grad = layer.calc_update_gradient(dJdy)
assert_array_equal(layer.W + update_grad, np.array([[4, 7, 7]]))
