def test_grad(self):
a = graph.Constant(1)
b = graph.Constant(2)
c = graph.Constant(4)
d = graph.Sum(a, b)
e = graph.Sum(b, c)
f = graph.MultiplyByScalar(d, e)
g = graph.Grad(f, [a, b, c])
self.assertSequenceEqual(graph.run(g), [6, 9, 3])
def __init__(self,
x,
filters,
filter_size,
step,
bias=True,
activation=None,
padding='VALID',
*activation_args,
**activation_kwargs):
if isinstance(x, Layer):
x = x.output
self.kernel = graph.Variable(
(filter_size, filter_size, x.shape[2], filters))
y = graph.Convolution(x, self.kernel, step, padding)
self.bias = graph.Variable((filters, )) if bias else None
if bias:
y = graph.Sum(y, self.bias)
if activation:
y = activation(y, *activation_args, **activation_kwargs)
super(Convolution, self).__init__((x, ), y, [self.kernel, self.bias])
def test_batched(self):
a = graph.Placeholder(shape=(2,), batched=True)
b = graph.Constant([1, 2])
c = graph.Sum(a, b)
np.testing.assert_array_equal(graph.run(c, {a: np.array([[1, 2]])}), np.array([[2, 4]]))
np.testing.assert_array_equal(graph.run(c, {a: np.array([[1, 2], [3, 4]])}), np.array([[2, 4], [4, 6]]))
def test_placeholder(self):
a = graph.Placeholder(shape=(), batched=False)
b = graph.Constant(2)
c = graph.Sum(a, b)
self.assertEqual(graph.run(c, {a: 3}), 5)
self.assertEqual(graph.run(c, {a: 4}), 6)
def __init__(self,
x,
n,
bias=True,
activation=None,
*activation_args,
**activation_kwargs):
if isinstance(x, Layer):
x = x.output
assert len(x.shape) == 1
self.kernel = graph.Variable((x.shape[0], n))
self.bias = graph.Variable((n, )) if bias else None
y = graph.Matmul(x, self.kernel)
if bias:
y = graph.Sum(y, self.bias)
if activation:
y = activation(y, *activation_args, **activation_kwargs)
super(Dense, self).__init__((x, ), y,
(self.kernel, self.bias) if self.bias else
(self.kernel, ))
def test_grad_simple(self):
a = graph.Constant(1)
b = graph.Constant(2)
c = graph.Constant(4)
d = graph.Sum(a, b)
e = graph.MultiplyByScalar(d, c)
g = graph.Grad(e, [a, b, c])
self.assertSequenceEqual(graph.run(g), [4, 4, 3])
def test_grad_placeholder_variable(self):
a = graph.Constant(1)
b = graph.Placeholder(batched=False, shape=())
c = graph.Variable(shape=())
c.value = 4
d = graph.Sum(a, b)
e = graph.MultiplyByScalar(d, c)
g = graph.Grad(e, [a, b, c])
self.assertSequenceEqual(graph.run(g, {b: 2}), [4, 4, 3])
def test_non_trivial(self):
a = graph.Constant(2)
b = graph.Constant(3)
c = graph.Sum(a, b)
self.assertEqual(graph.run(c), 5)