tf.compat.v1.Session  |  TensorFlow v2.16.1 (original) (raw)

A class for running TensorFlow operations.

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

Migrate to TF2

Session does not work with either eager execution or tf.function, and you should not invoke it directly. To migrate code that uses sessions to TF2, rewrite the code without it. See themigration guideon replacing Session.run calls.

Description

Used in the notebooks

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

tf.compat.v1.disable_eager_execution() # need to disable eager in TF2.x
# Build a graph.
a = tf.constant(5.0)
b = tf.constant(6.0)
c = a * b

# Launch the graph in a session.
sess = tf.compat.v1.Session()

# Evaluate the tensor `c`.
print(sess.run(c)) # prints 30.0

A session may own resources, such astf.Variable, tf.queue.QueueBase, and tf.compat.v1.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.compat.v1.Session()
sess.run(...)
sess.close()

# Using the context manager.
with tf.compat.v1.Session() as sess:
  sess.run(...)

TheConfigProtoprotocol 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.compat.v1.Session(config=tf.compat.v1.ConfigProto(
    allow_soft_placement=True,
    log_device_placement=True))

Methods

as_default

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as_default()

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

Use with the with keyword to specify that calls totf.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.

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.

close

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close()

Closes this session.

Calling this method frees all resources associated with the session.

list_devices

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list_devices()

Lists available devices in this session.

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

make_callable

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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. Seetf.Session.run for details of the allowable feed key and value types.

The returned callable will have the same return type astf.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.

partial_run

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partial_run(
    handle, fetches, feed_dict=None
)

Continues the execution with more feeds and fetches. (deprecated)

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 subsequentpartial_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})

partial_run_setup

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partial_run_setup(
    fetches, feeds=None
)

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

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.

reset

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@staticmethod 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 targetis 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.

run

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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 Operationand evaluate every Tensor in fetches, substituting the values infeed_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.sparse.SparseTensor. The corresponding fetched value will be atf.compat.v1.SparseTensorValuecontaining 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 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 tensor. Additionally, if the key is atf.compat.v1.placeholder, the shape of the value will be checked for compatibility with the placeholder.
• If the key is atf.sparse.SparseTensor, the value should be atf.compat.v1.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.

__enter__

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__enter__() -> 'Session'

__exit__

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__exit__(
    exec_type, exec_value, exec_tb
)