def _UnbatchGrad(op, grad): # pylint: disable=invalid-name return [ gen_batch_ops.unbatch_grad(op.inputs[0], op.inputs[1], grad, op.inputs[2], shared_name="unbatch_gradient_{}".format( op.name)), None, None ]
def _UnbatchGrad(op, grad): # pylint: disable=invalid-name return [ gen_batch_ops.unbatch_grad( op.inputs[0], op.inputs[1], grad, op.inputs[2], shared_name="unbatch_gradient_{}".format(op.name)), None, None ]
def testUnbatchGradInvalidBatchId(self): with self.assertRaises(errors.InvalidArgumentError): self.evaluate( gen_batch_ops.unbatch_grad( original_input=constant_op.constant([1]), batch_index=constant_op.constant([ [0, 0], ], dtype=dtypes.int64), grad=constant_op.constant([ 1, ]), id=constant_op.constant([ 1, ], dtype=dtypes.int64)))
def testUnbatchGradInvalidArgs(self): original_input = random_ops.random_uniform(shape=(3, 1), dtype=dtypes.float64, maxval=None) batch_index = random_ops.random_uniform(shape=(3, 1), dtype=dtypes.int64, maxval=65536) grad = random_ops.random_uniform(shape=(3, 1), dtype=dtypes.float64, maxval=None) batch_id = random_ops.random_uniform(shape=(3, 1), dtype=dtypes.int64, maxval=65536) with self.assertRaises(errors.InvalidArgumentError): self.evaluate( gen_batch_ops.unbatch_grad(original_input=original_input, batch_index=batch_index, grad=grad, id=batch_id, container="", shared_name="", name=""))