def compute_grad_by_params(w, b):
layer = Dense(32, 64)
layer.weights = np.array(w)
layer.biases = np.array(b)
x = np.linspace(-1, 1, 10 * 32).reshape([10, 32])
layer.backward(x, np.ones([10, 64]), optim='gd', lr=1)
return w - layer.weights, b - layer.biases
def test_dense_layer_FORWARD(self):
layer = Dense(3, 4)
x = np.linspace(-1, 1, 2 * 3).reshape([2, 3])
layer.weights = np.linspace(-1, 1, 3 * 4).reshape([3, 4])
layer.biases = np.linspace(-1, 1, 4)
self.assertTrue(
np.allclose(
layer.forward(x),
np.array([[0.07272727, 0.41212121, 0.75151515, 1.09090909],
[-0.90909091, 0.08484848, 1.07878788, 2.07272727]])))
def compute_out_given_wb(w, b):
layer = Dense(32, 64)
layer.weights = np.array(w)
layer.biases = np.array(b)
x = np.linspace(-1, 1, 10 * 32).reshape([10, 32])
return layer.forward(x)
