def test_multidim_sigmoid(m_):
with pm.Node(name="logistic") as graph:
m = pm.parameter(name="m")
n = pm.parameter(name="n")
x = pm.input("x", shape=(m))
w = pm.state("w", shape=(m))
i = pm.index(0, m - 1, name="i")
o = pm.sigmoid(w[i] * x[i], name="out")
x_ = np.random.randint(0, 10, m_).astype(np.float)
w_ = np.random.randint(0, 10, m_).astype(np.float)
shape_dict = {"m": m_}
input_dict = {"x": x_, "w": w_}
np_res = sigmoid((x_ * w_))
coarse_eval = graph("out", input_dict)
np.testing.assert_allclose(np_res, coarse_eval)
lowered = set_shape_and_lower(graph, shape_dict)
keys = [f"out/out({i},)" for i in range(m_)]
x_ = np.random.randint(0, 10, m_).astype(np.float)
w_ = np.random.randint(0, 10, m_).astype(np.float)
input_dict = {}
for i in range(m_):
input_dict[f"x/x({i},)"] = x_[i]
input_dict[f"w/w({i},)"] = w_[i]
np_res = sigmoid((x_ * w_))
lower_res = np.asarray(lowered(keys, input_dict)).reshape(np_res.shape)
np.testing.assert_allclose(lower_res, np_res)
def define_graph(self, x, w, y, y_pred, mu, m):
i = pm.index(0, (m - 1).set_name("m-1"), name="i")
h = pm.temp(name="h", shape=(m))
h = pm.sigmoid(pm.sum([i], (x[i] * w[i]), name="h"))
d = (h - y).set_name("h-y")
g = (d * x[i]).set_name("d*x")
w[i] = w[i] - mu * g[i]
def test_sigmoid(m_):
with pm.Node(name="logistic1") as graph:
m = pm.parameter(name="m")
n = pm.parameter(name="n")
x = pm.input("x", shape=(m))
w = pm.state("w", shape=(m))
i = pm.index(0, m - 1, name="i")
o = pm.sigmoid(pm.sum([i], w[i] * x[i]), name="out")
x_ = np.random.randint(0, 10, m_)
w_ = np.random.randint(0, 10, m_)
input_dict = {"x": x_, "w": w_}
np_res = int(sigmoid(np.sum(x_ * w_)))
shape_dict = {"m": m_}
coarse_eval = graph("out", x=x_, w=w_)
np.testing.assert_allclose(np_res, coarse_eval)
lowered = set_shape_and_lower(graph, shape_dict)
def define_graph(self, x, w, y_pred, mu, m):
i = pm.index(0, (m - 1).set_name("m-1"), name="i")
h = pm.sigmoid(pm.sum([i], (x[i] * w[i]), name="h"))
def define_graph(self, x, out):
indices = _get_single_node_indices(out, shape=out.shape)
out[indices] = pm.sigmoid(x[indices])
def define_graph(self, x, w, y):
i = pm.index(0, (w.shape[1] - 1), name="i")
j = pm.index(0, (w.shape[0] - 1), name="j")
y[j] = pm.sigmoid(pm.sum([i], w[j, i] * x[i], name="h"))