tf.InteractiveSession

class tf.InteractiveSession

See the guide: Running Graphs > Session management

A TensorFlow Session for use in interactive contexts, such as a shell.

The only difference with a regular Session is that an InteractiveSession installs itself as the default session on construction. The methods tf.Tensor.eval and tf.Operation.run will use that session to run ops.

This is convenient in interactive shells and IPython notebooks, as it avoids having to pass an explicit Session object to run ops.

For example:

sess = tf.InteractiveSession()
a = tf.constant(5.0)
b = tf.constant(6.0)
c = a * b
# We can just use 'c.eval()' without passing 'sess'
print(c.eval())
sess.close()

Note that a regular session installs itself as the default session when it is created in a with statement. The common usage in non-interactive programs is to follow that pattern:

a = tf.constant(5.0)
b = tf.constant(6.0)
c = a * b
with tf.Session():
  # We can also use 'c.eval()' here.
  print(c.eval())

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 interactive 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.
• graph: (Optional.) The Graph to be launched (described above).
• config: (Optional) ConfigProto proto used to configure 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 an InteractiveSession.

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.

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.