def testShapeMismatch(self):
c = np.random.randint(0, 2, 6).astype(np.bool).reshape(1, 3, 2)
x = np.random.rand(1, 3, 2) * 100
y = np.random.rand(2, 5, 3) * 100
for t in [np.float32, np.float64, np.int32, np.int64, np.complex64]:
xt = x.astype(t)
yt = y.astype(t)
with self.assertRaises(ValueError):
tf.batch_select(c, xt, yt)
def _compare(self, c, x, y, use_gpu):
np_ans = np.concatenate(
[x_i if c_i else y_i for c_i, x_i, y_i in zip(c, x, y)])
with self.test_session(use_gpu=use_gpu):
out = tf.batch_select(c, x, y)
tf_ans = out.eval()
self.assertAllEqual(np_ans, tf_ans)
self.assertShapeEqual(np_ans, out)
def _compareGradientY(self, c, x, y):
with self.test_session():
inx = tf.convert_to_tensor(x)
iny = tf.convert_to_tensor(y)
out = tf.batch_select(c, inx, iny)
s = list(np.shape(c))
jacob_t, jacob_n = tf.test.compute_gradient(iny,
s,
out,
s,
x_init_value=y)
if x.dtype == np.float32:
self.assertAllClose(jacob_t, jacob_n, rtol=1e-3, atol=1e-3)
elif x.dtype == np.float64:
self.assertAllClose(jacob_t, jacob_n, rtol=1e-5, atol=1e-5)