Method run

tensorflow/python/client/session.py:855–974

Runs operations and evaluates tensors in `fetches`. This method runs one "step" of TensorFlow computation, by running the necessary graph fragment to execute every `Operation` and evaluate every `Tensor` in `fetches`, substituting the values in `feed_dict` for the corresponding inpu

(self, fetches, feed_dict=None, options=None, run_metadata=None)

    return ops.default_session(self)

  def run(self, fetches, feed_dict=None, options=None, run_metadata=None):
    """Runs operations and evaluates tensors in `fetches`.

    This method runs one "step" of TensorFlow computation, by
    running the necessary graph fragment to execute every `Operation`
    and evaluate every `Tensor` in `fetches`, substituting the values in
    `feed_dict` for the corresponding input values.

    The `fetches` argument may be a single graph element, or an arbitrarily
    nested list, tuple, namedtuple, dict, or OrderedDict containing graph
    elements at its leaves.  A graph element can be one of the following types:

    * A `tf.Operation`.
      The corresponding fetched value will be `None`.
    * A `tf.Tensor`.
      The corresponding fetched value will be a numpy ndarray containing the
      value of that tensor.
    * A `tf.SparseTensor`.
      The corresponding fetched value will be a
      `tf.compat.v1.SparseTensorValue`
      containing the value of that sparse tensor.
    * A `get_tensor_handle` op.  The corresponding fetched value will be a
      numpy ndarray containing the handle of that tensor.
    * A `string` which is the name of a tensor or operation in the graph.

    The value returned by `run()` has the same shape as the `fetches` argument,
    where the leaves are replaced by the corresponding values returned by
    TensorFlow.

    Example:

    ```python
       a = tf.constant([10, 20])
       b = tf.constant([1.0, 2.0])
       # 'fetches' can be a singleton
       v = session.run(a)
       # v is the numpy array [10, 20]
       # 'fetches' can be a list.
       v = session.run([a, b])
       # v is a Python list with 2 numpy arrays: the 1-D array [10, 20] and the
       # 1-D array [1.0, 2.0]
       # 'fetches' can be arbitrary lists, tuples, namedtuple, dicts:
       MyData = collections.namedtuple('MyData', ['a', 'b'])
       v = session.run({'k1': MyData(a, b), 'k2': [b, a]})
       # v is a dict with
       # v['k1'] is a MyData namedtuple with 'a' (the numpy array [10, 20]) and
       # 'b' (the numpy array [1.0, 2.0])
       # v['k2'] is a list with the numpy array [1.0, 2.0] and the numpy array
       # [10, 20].
    ```

    The optional `feed_dict` argument allows the caller to override
    the value of tensors in the graph. Each key in `feed_dict` can be
    one of the following types:

    * If the key is a `tf.Tensor`, the
      value may be a Python scalar, string, list, or numpy ndarray
      that can be converted to the same `dtype` as that