tf.compat.v1.InteractiveSession

View source on GitHub

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

tf.compat.v1.InteractiveSession(
    target='', graph=None, config=None
)

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.compat.v1.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.compat.v1.Session():
  # We can also use 'c.eval()' here.
  print(c.eval())

Args:

Attributes:

Methods

as_default

View source

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.compat.v1.Session()

with sess.as_default():
  assert tf.compat.v1.get_default_session() is sess
  print(c.eval())

To get the current default session, use tf.compat.v1.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.compat.v1.Session()
with sess.as_default():
  print(c.eval())
# ...
with sess.as_default():
  print(c.eval())

sess.close()

Alternatively, you can use with tf.compat.v1.Session(): to create a session that is automatically closed on exiting the context, including when an uncaught exception is raised.

N.B. The default session 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.

N.B. Entering a with sess.as_default(): block does not affect the current default graph. If you are using multiple graphs, and sess.graph is different from the value of tf.compat.v1.get_default_graph, you must explicitly enter a with sess.graph.as_default(): block to make sess.graph the default graph.

Returns:

A context manager using this session as the default session.

close

View source

close()

Closes an InteractiveSession.

list_devices

View source

list_devices()

Lists available devices in this session.

devices = sess.list_devices()
for d in devices:
  print(d.name)

Where:

Each element in the list has the following properties

Raises:

Returns:

A list of devices in the session.

make_callable

View source

make_callable(
    fetches, feed_list=None, accept_options=False
)

Returns a Python callable that runs a particular step.

The returned callable will take len(feed_list) arguments whose types must be compatible feed values for the respective elements of feed_list. For example, if element i of feed_list is a tf.Tensor, the ith argument to the returned callable must be a numpy ndarray (or something convertible to an ndarray) with matching element type and shape. See tf.Session.run for details of the allowable feed key and value types.

The returned callable will have the same return type as tf.Session.run(fetches, ...). For example, if fetches is a tf.Tensor, the callable will return a numpy ndarray; if fetches is a tf.Operation, it will return None.

Args:

Returns:

A function that when called will execute the step defined by feed_list and fetches in this session.

Raises:

partial_run

View source

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:

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:

partial_run_setup

View source

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:

Returns:

A handle for partial run.

Raises:

run

View source

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:

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 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:

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:

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). Order in which fetches operations are evaluated inside the call is undefined.

Raises: