# tf.compat.v1.InteractiveSession

<br />

|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/client/session.py#L1720-L1826) |

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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Tensor#eval)
and [`tf.Operation.run`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Operation#run)
will use that session to run ops.

This is convenient in interactive shells and [IPython
notebooks](http://ipython.org), 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())

<br />

<br />

<br />

| ## 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. |

<br />

<br />

<br />

<br />

| ## 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. |

<br />

## Methods

### `as_default`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/client/session.py#L808-L861)

    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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Operation#run) or [`tf.Tensor.eval`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/compat/v1/get_default_session).

> [!NOTE]
> **Note:** 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.

> [!NOTE]
> **Note:** 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.

> [!NOTE]
> **Note:** 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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/compat/v1/get_default_graph), you must explicitly enter a `with sess.graph.as_default():` block to make `sess.graph` the default graph.

<br />

<br />

<br />

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

<br />

### `close`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/client/session.py#L1813-L1826)

    close()

Closes an `InteractiveSession`.

### `list_devices`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/client/session.py#L721-L757)

    list_devices()

Lists available devices in this session.

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

<br />

| 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. |

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| [`tf.errors.OpError`](https://www.tensorflow.org/api_docs/python/tf/errors/OpError) | If it encounters an error (e.g. session is in an invalid state, or network errors occur). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A list of devices in the session. ||

<br />

### `make_callable`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/client/session.py#L1221-L1345)

    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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Tensor),
the callable will return a numpy ndarray; if `fetches` is a [`tf.Operation`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Operation),
it will return `None`.

<br />

<br />

<br />

| 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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/compat/v1/RunOptions) and [`tf.compat.v1.RunMetadata`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`. |

<br />

<br />

<br />

<br />

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

<br />

<br />

<br />

<br />

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

<br />

### `partial_run`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/client/session.py#L984-L1039)

    partial_run(
        handle, fetches, feed_dict=None
    )

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

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed after 2023-06-01. Instructions for updating: This function is deprecated and we do not expect adding newfunctionality to it. Please do not have your code dependingon this function.

> [!NOTE]
> **Note:** This function is deprecated and we do not expect adding new functionality to it. Please do not have your code depending on this function.

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})

<br />

<br />

<br />

| 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). |

<br />

<br />

<br />

<br />

| 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`). ||

<br />

<br />

<br />

<br />

| Raises ||
|---|---|
| [`tf.errors.OpError`](https://www.tensorflow.org/api_docs/python/tf/errors/OpError) | Or one of its subclasses on error. |

<br />

### `partial_run_setup`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/client/session.py#L1041-L1126)

    partial_run_setup(
        fetches, feeds=None
    )

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

> [!WARNING]
> **Deprecated:** THIS FUNCTION IS DEPRECATED. It will be removed after 2023-06-01. Instructions for updating: This function is deprecated and we do not expect adding newfunctionality to it. Please do not have your code dependingon this function.

> [!NOTE]
> **Note:** This function is deprecated and we do not expect adding new functionality to it. Please do not have your code depending on this function.

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.

<br />

<br />

<br />

| Args ||
|---|---|
| `fetches` | A single graph element, or a list of graph elements. |
| `feeds` | A single graph element, or a list of graph elements. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A handle for partial run. ||

<br />

<br />

<br />

<br />

| 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`](https://www.tensorflow.org/api_docs/python/tf/errors/OpError) | Or one of its subclasses if a TensorFlow error happens. |

<br />

### `run`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/client/session.py#L863-L982)

    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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Operation). The corresponding fetched value will be `None`.
- A [`tf.Tensor`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Tensor). The corresponding fetched value will be a numpy ndarray containing the value of that tensor.
- A [`tf.sparse.SparseTensor`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/sparse/SparseTensor). The corresponding fetched value will be a [`tf.compat.v1.SparseTensorValue`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/sparse/SparseTensor), the value should be a [`tf.compat.v1.SparseTensorValue`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/compat/v1/SparseTensorValue).
- If the key is a nested tuple of `Tensor`s or `SparseTensor`s, 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.

<br />

<br />

<br />

| 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 |

<br />

<br />

<br />

<br />

| 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. ||

<br />

<br />

<br />

<br />

| 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. |

<br />