tf.compat.v1.serialize

A class for running TensorFlow operations.

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

Used in the notebooks

Used in the guide	Used in the tutorials
Migrate your TensorFlow 1 code to TensorFlow 2	Universal Sentence Encoder-Lite demo Generating Images with Little Data Using S3GAN Generating Images with BigBiGAN Generating Images with BigGAN Exploring the TF-Hub CORD-19 Swivel Embeddings

A Session object encapsulates the environment in which Operation objects 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 as tf.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(...)

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

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.

Attributes
`graph`	The graph that was launched in this session.
`graph_def`	A serializable version of the underlying TensorFlow graph.
`sess_str`	The TensorFlow process to which this session will connect.

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.

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.

Returns
A context manager using this session as the default session.

`close`

View source

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.

`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

name: A string with the full name of the device. ex: /job:worker/replica:0/task:3/device:CPU:0
device_type: The type of the device (e.g. CPU, GPU, TPU.)
memory_limit: The maximum amount of memory available on the device. Note: depending on the device, it is possible the usable memory could be substantially less.

Raises
`tf.errors.OpError`	If it encounters an error (e.g. session is in an invalid state, or network errors occur).

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
`fetches`	A value or list of values to fetch. See `tf.Session.run` for details of the allowable fetch types.
`feed_list`	(Optional.) A list of `feed_dict` keys. See `tf.Session.run` for details of the allowable feed key types.
`accept_options`	(Optional.) If `True`, the returned `Callable` will be able to accept `tf.compat.v1.RunOptions` and `tf.compat.v1.RunMetadata` as optional keyword arguments `options` and `run_metadata`, respectively, with the same syntax and semantics as `tf.Session.run`, which is useful for certain use cases (profiling and debugging) but will result in measurable slowdown of the `Callable`'s performance. Default: `False`.

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

Raises
`TypeError`	If `fetches` or `feed_list` cannot be interpreted as arguments to `tf.Session.run`.

`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
`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`

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
`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`

View source

@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 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`

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:

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 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:

   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 a tf.compat.v1.placeholder, the shape of the value will be checked for compatibility with the placeholder.
If the key is a tf.sparse.SparseTensor, the value should be a tf.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.

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

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.

`enter`

View source

__enter__()

`exit`

View source

__exit__(
    exec_type, exec_value, exec_tb
)

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.serialize_sparse

Used in the notebooks

Args

Attributes

Methods

as_default

close

list_devices

Where:

make_callable

partial_run

partial_run_setup

reset

NOTE:

run

Example:

__enter__

__exit__

`as_default`

`close`

`list_devices`

`make_callable`

`partial_run`

`partial_run_setup`

`reset`

`run`

`enter`

`exit`