tf.Session

class tf.Session

See the guides: Running Graphs > Session management, Running Graphs

A class for running TensorFlow operations.

A Session object encapsulates the environment in which Operation objects are executed, and Tensor objects are evaluated. For example:

# Build a graph.
a = tf.constant(5.0)
b = tf.constant(6.0)
c = a * b

# Launch the graph in a session.
sess = tf.Session()

# Evaluate the tensor `c`.
print(sess.run(c))

A session may own resources, such as tf.Variable, tf.QueueBase, and tf.ReaderBase. It is important to release these resources when they are no longer required. To do this, either invoke the tf.Session.close method on the session, or use the session as a context manager. The following two examples are equivalent:

# Using the `close()` method.
sess = tf.Session()
sess.run(...)
sess.close()

# Using the context manager.
with tf.Session() as sess:
  sess.run(...)

The ConfigProto protocol buffer exposes various configuration options for a session. For example, to create a session that uses soft constraints for device placement, and log the resulting placement decisions, create a session as follows:

# Launch the graph in a session that allows soft device placement and
# logs the placement decisions.
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
                                        log_device_placement=True))

Properties

graph

The graph that was launched in this session.

graph_def

A serializable version of the underlying TensorFlow graph.

Returns:

A graph_pb2.GraphDef proto containing nodes for all of the Operations in the underlying TensorFlow graph.

sess_str

Methods

__init__(target='', graph=None, config=None)

Creates a new TensorFlow session.

If no graph argument is specified when constructing the session, the default graph will be launched in the session. If you are using more than one graph (created with tf.Graph() in the same process, you will have to use different sessions for each graph, but each graph can be used in multiple sessions. In this case, it is often clearer to pass the graph to be launched explicitly to the session constructor.

Args:

• target: (Optional.) The execution engine to connect to. Defaults to using an in-process engine. See Distributed TensorFlow for more examples.
• graph: (Optional.) The Graph to be launched (described above).
• config: (Optional.) A ConfigProto protocol buffer with configuration options for the session.

as_default()

Returns a context manager that makes this object the default session.

Use with the with keyword to specify that calls to tf.Operation.run or tf.Tensor.eval should be executed in this session.

c = tf.constant(..)
sess = tf.Session()

with sess.as_default():
  assert tf.get_default_session() is sess
  print(c.eval())

To get the current default session, use tf.get_default_session.

N.B. The as_default context manager does not close the session when you exit the context, and you must close the session explicitly.

c = tf.constant(...)
sess = tf.Session()
with sess.as_default():
  print(c.eval())
# ...
with sess.as_default():
  print(c.eval())

sess.close()

Alternatively, you can use with tf.Session(): to create a session that is automatically closed on exiting the context, including when an uncaught exception is raised.

N.B. The default graph is a property of the current thread. If you create a new thread, and wish to use the default session in that thread, you must explicitly add a with sess.as_default(): in that thread's function.

Returns:

A context manager using this session as the default session.

close()

Closes this session.

Calling this method frees all resources associated with the session.

Raises:

tf.errors.OpError: Or one of its subclasses if an error occurs while closing the TensorFlow session.

partial_run(handle, fetches, feed_dict=None)

Continues the execution with more feeds and fetches.

This is EXPERIMENTAL and subject to change.

To use partial execution, a user first calls partial_run_setup() and then a sequence of partial_run(). partial_run_setup specifies the list of feeds and fetches that will be used in the subsequent partial_run calls.

The optional feed_dict argument allows the caller to override the value of tensors in the graph. See run() for more information.

Below is a simple example:

a = array_ops.placeholder(dtypes.float32, shape=[])
b = array_ops.placeholder(dtypes.float32, shape=[])
c = array_ops.placeholder(dtypes.float32, shape=[])
r1 = math_ops.add(a, b)
r2 = math_ops.multiply(r1, c)

h = sess.partial_run_setup([r1, r2], [a, b, c])
res = sess.partial_run(h, r1, feed_dict={a: 1, b: 2})
res = sess.partial_run(h, r2, feed_dict={c: res})

Args:

• handle: A handle for a sequence of partial runs.
• fetches: A single graph element, a list of graph elements, or a dictionary whose values are graph elements or lists of graph elements (see documentation for run).
• feed_dict: A dictionary that maps graph elements to values (described above).

Returns:

Either a single value if fetches is a single graph element, or a list of values if fetches is a list, or a dictionary with the same keys as fetches if that is a dictionary (see documentation for run).

Raises:

tf.errors.OpError: Or one of its subclasses on error.

partial_run_setup(fetches, feeds=None)

Sets up a graph with feeds and fetches for partial run.

This is EXPERIMENTAL and subject to change.

Note that contrary to run, feeds only specifies the graph elements. The tensors will be supplied by the subsequent partial_run calls.

Args:

• fetches: A single graph element, or a list of graph elements.
• feeds: A single graph element, or a list of graph elements.

Returns:

A handle for partial run.

Raises:

• RuntimeError: If this Session is in an invalid state (e.g. has been closed).
• TypeError: If fetches or feed_dict keys are of an inappropriate type. tf.errors.OpError: Or one of its subclasses if a TensorFlow error happens.

reset(target, containers=None, config=None)

Resets resource containers on target, and close all connected sessions.

A resource container is distributed across all workers in the same cluster as target. When a resource container on target is reset, resources associated with that container will be cleared. In particular, all Variables in the container will become undefined: they lose their values and shapes.

NOTE: (i) reset() is currently only implemented for distributed sessions. (ii) Any sessions on the master named by target will be closed.

If no resource containers are provided, all containers are reset.

Args:

• target: The execution engine to connect to.
• containers: A list of resource container name strings, or None if all of all the containers are to be reset.
• config: (Optional.) Protocol buffer with configuration options.

Raises:

tf.errors.OpError: Or one of its subclasses if an error occurs while resetting containers.

run(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:

• An 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.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:

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 a Python list with 2 numpy arrays: the numpy 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 tensor. Additionally, if the key is a tf.placeholder, the shape of the value will be checked for compatibility with the placeholder.
• If the key is a tf.SparseTensor, the value should be a tf.SparseTensorValue.
• If the key is a nested tuple of Tensors or SparseTensors, the value should be a nested tuple with the same structure that maps to their corresponding values as above.

Each value in feed_dict must be convertible to a numpy array of the dtype of the corresponding key.

The optional options argument expects a [RunOptions] proto. The options allow controlling the behavior of this particular step (e.g. turning tracing on).

The optional run_metadata argument expects a [RunMetadata] proto. When appropriate, the non-Tensor output of this step will be collected there. For example, when users turn on tracing in options, the profiled info will be collected into this argument and passed back.

Args:

• fetches: A single graph element, a list of graph elements, or a dictionary whose values are graph elements or lists of graph elements (described above).
• feed_dict: A dictionary that maps graph elements to values (described above).
• options: A [RunOptions] protocol buffer
• run_metadata: A [RunMetadata] protocol buffer

Returns:

Either a single value if fetches is a single graph element, or a list of values if fetches is a list, or a dictionary with the same keys as fetches if that is a dictionary (described above).

Raises:

• RuntimeError: If this Session is in an invalid state (e.g. has been closed).
• TypeError: If fetches or feed_dict keys are of an inappropriate type.
• ValueError: If fetches or feed_dict keys are invalid or refer to a Tensor that doesn't exist.

Defined in tensorflow/python/client/session.py.