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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 | |
---|---|
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.
|
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
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
close()
Closes an InteractiveSession
.
list_devices
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
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 i
th
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
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
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
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 beNone
. - 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.SparseTensorValue
containing the value of that sparse tensor. - A
get_tensor_handle
op. The corres