e97a5389cd4169b54ded7eb173fc0d4d3f4cf8d5,pymanopt/autodiff/backends/_tensorflow.py,_TensorFlowBackend,compute_gradient,#_TensorFlowBackend#,68

Before Change

        
        tfgrad = tf.gradients(objective, argument)

        if not isinstance(argument, list):
            def grad(x):
                feed_dict = {argument: x}
                return self._session.run(tfgrad[0], feed_dict)
        else:
            def grad(x):
                feed_dict = {i: d for i, d in zip(argument, x)}
                return self._session.run(tfgrad, feed_dict)

        return grad

    @Backend._assert_backend_available
    def compute_hessian(self, objective, argument):

After Change

    @Backend._assert_backend_available
    def compute_gradient(self, function, arguments):
        flattened_arguments = flatten_arguments(arguments)
        gradient = tf.gradients(function, flattened_arguments)

        if len(flattened_arguments) == 1:
            (argument,) = flattened_arguments

Instances