def function()

in maddpg/common/tf_util.py [0:0]

• 9 lines of code
• 3 McCabe index (conditional complexity)

def function(inputs, outputs, updates=None, givens=None):
    """Just like Theano function. Take a bunch of tensorflow placeholders and expersions
    computed based on those placeholders and produces f(inputs) -> outputs. Function f takes
    values to be feed to the inputs placeholders and produces the values of the experessions
    in outputs.

    Input values can be passed in the same order as inputs or can be provided as kwargs based
    on placeholder name (passed to constructor or accessible via placeholder.op.name).

    Example:
        x = tf.placeholder(tf.int32, (), name="x")
        y = tf.placeholder(tf.int32, (), name="y")
        z = 3 * x + 2 * y
        lin = function([x, y], z, givens={y: 0})

        with single_threaded_session():
            initialize()

            assert lin(2) == 6
            assert lin(x=3) == 9
            assert lin(2, 2) == 10
            assert lin(x=2, y=3) == 12

    Parameters
    ----------
    inputs: [tf.placeholder or TfInput]
        list of input arguments
    outputs: [tf.Variable] or tf.Variable
        list of outputs or a single output to be returned from function. Returned
        value will also have the same shape.
    """
    if isinstance(outputs, list):
        return _Function(inputs, outputs, updates, givens=givens)
    elif isinstance(outputs, (dict, collections.OrderedDict)):
        f = _Function(inputs, outputs.values(), updates, givens=givens)
        return lambda *args, **kwargs: type(outputs)(zip(outputs.keys(), f(*args, **kwargs)))
    else:
        f = _Function(inputs, [outputs], updates, givens=givens)
        return lambda *args, **kwargs: f(*args, **kwargs)[0]