def testUnknownShape(self): x = array_ops.placeholder(dtypes.float32) num_dimensions = array_ops.placeholder(dtypes.int32) ret = random_grad.add_leading_unit_dimensions(x, num_dimensions) with self.cached_session() as sess: ret_val = sess.run(ret, {x: np.ones([2, 2]), num_dimensions: 2}) self.assertAllEqual(ret_val.shape, [1, 1, 2, 2])
def testUnknownShape(self): x = array_ops.placeholder(dtypes.float32) num_dimensions = array_ops.placeholder(dtypes.int32) ret = random_grad.add_leading_unit_dimensions(x, num_dimensions) with self.test_session() as sess: ret_val = sess.run(ret, {x: np.ones([2, 2]), num_dimensions: 2}) self.assertAllEqual(ret_val.shape, [1, 1, 2, 2])
def testScalarInput(self): ret = random_grad.add_leading_unit_dimensions(1.0, 2) self.assertAllEqual(ret.shape, [1, 1])
def testZeroExtraDimensions(self): ret = random_grad.add_leading_unit_dimensions(array_ops.ones([3, 2, 1]), 0) self.assertAllEqual(ret.shape, [3, 2, 1])
def testBasic(self): ret = random_grad.add_leading_unit_dimensions(array_ops.ones([3, 2, 1]), 3) self.assertAllEqual(ret.shape, [1, 1, 1, 3, 2, 1])