def __init__(self,
label,
rng,
input,
nin,
nout,
W=None,
b=None,
activation_fn=op.Sigmoid):
q = pl.qs[0]
self.input = input
if W is None:
nw = np.asarray(rng.uniform(low=-np.sqrt(6. / (nin + nout)),
high=np.sqrt(6. / (nin + nout)),
size=(nin, nout)),
dtype=np.float32)
if activation_fn == op.Sigmoid:
nw *= 4
W = clarray.to_device(q, nw)
if b is None:
b = clarray.zeros(q, (nout, ), np.float32)
self.W = W
self.b = b
vW = expr.Variable('W' + label)
vb = expr.Variable('b' + label)
lin_out = op.Add(op.Dot(self.input, vW), vb)
self.output = lin_out if activation_fn is None else activation_fn(
lin_out)
self.params = [(vW.name, self.W), (vb.name, self.b)]
def test_fwd_grad(self):
x = expr.Variable('x')
wrt = {'x': 1}
valuation = plat.valuation()
e1 = op.Add(x, x)
valuation['x'] = np.eye(2, dtype=np.float32)
d1 = e1.fwd_grad(wrt, valuation)
self.assertEqual(d1, 2)
def test_addition(self):
x = expr.Variable("x")
y = expr.Constant(2)
valuation = plat.valuation()
valuation['x'] = np.eye(2, dtype=np.float32)
z = op.Add(x,y).evaluate(valuation)
zz = z.get()
self.assertEqual(zz[0,0], 3.0)
self.assertEqual(zz[0,1], 2.0)
self.assertEqual(zz[1,0], 2.0)
self.assertEqual(zz[1,1], 3.0)
def test_rev_grad(self):
x = expr.Variable('x')
y = expr.Variable('y')
z = expr.Variable('z')
valuation = plat.valuation()
valuation['x'] = np.ones((2,2), dtype=np.float32)*5
valuation['y'] = np.eye(2, dtype=np.float32)*2
valuation['z'] = np.ones((2,2), dtype=np.float32)*3
ex = op.Add(op.Mul(x, y), z)
# rg(X.*Y+Z) = [
rg = ex.rev_grad(valuation)
# dex/dx = y
dx = rg['x'].get()
yy = valuation['y'].get()
self.assertTrue(np.all(dx == yy))
def test_sigmoid_grads(self):
# TODO Testiraj da li AD gradijent 1/(1+exp(-x)) daje priblizno iste rezultate
# kao i sigmoid(x)*(1-sigmoid(x)), tj. grad sigmoida.
x = expr.Variable('x')
valuation = plat.valuation()
valuation['x'] = np.ones((3,3), dtype=np.float32)*2
sigm1 = op.Div(expr.Constant(1.0), op.Add(expr.Constant(1.0), op.Exp(op.Neg(x))))
sigm2 = op.Sigmoid(x)
e1 = sigm1.evaluate(valuation)
e2 = sigm2.evaluate(valuation)
rg1 = sigm1.rev_grad(valuation)
rg2 = sigm2.rev_grad(valuation)
xg1 = rg1['x'].get()
xg2 = rg2['x'].get()
self.assertTrue(np.all((xg1 - xg2) < 0.001))
def __init__(self, label, rng, input, n_in, n_out, W=None, b=None):
q = pl.qs[0]
self.input = input
if W is None:
self.W = clarray.zeros(q, (n_in, n_out), dtype=np.float32)
else:
self.W = W
if b is None:
self.b = clarray.zeros(q, (n_out, ), dtype=np.float32)
else:
self.b = b
vW = expr.Variable('W' + label)
vb = expr.Variable('b' + label)
self.p_y_given_x = op.Softmax(op.Add(op.Dot(self.input, vW), vb))
self.y_pred = op.Argmax(self.p_y_given_x, axis=-1)
self.params = [(vW.name, self.W), (vb.name, self.b)]