LostTech.TensorFlow : API Documentation

Type Tensor

Namespace tensorflow

Parent PythonObjectContainer

Interfaces _TensorLike, ITensor

Represents one of the outputs of an `Operation`.

A `Tensor` is a symbolic handle to one of the outputs of an `Operation`. It does not hold the values of that operation's output, but instead provides a means of computing those values in a TensorFlow `tf.compat.v1.Session`.

This class has two primary purposes:

1. A `Tensor` can be passed as an input to another `Operation`. This builds a dataflow connection between operations, which enables TensorFlow to execute an entire `Graph` that represents a large, multi-step computation.

2. After the graph has been launched in a session, the value of the `Tensor` can be computed by passing it to tf.Session.run. `t.eval()` is a shortcut for calling `tf.compat.v1.get_default_session().run(t)`.

In the following example, `c`, `d`, and `e` are symbolic `Tensor` objects, whereas `result` is a numpy array that stores a concrete value:
Show Example 
# Build a dataflow graph.
            c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
            d = tf.constant([[1.0, 1.0], [0.0, 1.0]])
            e = tf.matmul(c, d) 
 # Construct a `Session` to execute the graph.
sess = tf.compat.v1.Session() 
 # Execute the graph and store the value that `e` represents in `result`.
result = sess.run(e)

Methods

Properties

Public instance methods

void __array__()

object __array___dyn()

bool __bool__()

Returns True if this shape contains non-zero information.

object __bool___dyn()

Returns True if this shape contains non-zero information.

object __copy__()

Create a copy of this subgraph.

Note that this class is a "view", copying it only create another view and does not copy the underlying part of the tf.Graph.
Returns
object
A new identical instance of the original subgraph view.

object __copy___dyn()

Create a copy of this subgraph.

Note that this class is a "view", copying it only create another view and does not copy the underlying part of the tf.Graph.
Returns
object
A new identical instance of the original subgraph view.

object __iter__()

Returns `self.dims` if the rank is known, otherwise raises ValueError.

object __iter___dyn()

Returns `self.dims` if the rank is known, otherwise raises ValueError.

Nullable<int> __len__()

Returns the rank of this shape, or raises ValueError if unspecified.

object __len___dyn()

Returns the rank of this shape, or raises ValueError if unspecified.

bool __nonzero__()

Dummy method to prevent a tensor from being used as a Python `bool`.

This is the Python 2.x counterpart to `__bool__()` above.

object __nonzero___dyn()

Dummy method to prevent a tensor from being used as a Python `bool`.

This is the Python 2.x counterpart to `__bool__()` above.

object eval(IDictionary<object, object> feed_dict, IDictionary<object, object> session)

Evaluates this tensor in a `Session`.

Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor.

*N.B.* Before invoking `Tensor.eval()`, its graph must have been launched in a session, and either a default session must be available, or `session` must be specified explicitly.
Parameters
IDictionary<object, object> feed_dict
A dictionary that maps `Tensor` objects to feed values. See tf.Session.run for a description of the valid feed values.
IDictionary<object, object> session
(Optional.) The `Session` to be used to evaluate this tensor. If none, the default session will be used.
Returns
object
A numpy array corresponding to the value of this tensor.

object eval(IDictionary<object, object> feed_dict, BaseSession session)

Evaluates this tensor in a `Session`.

Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor.

*N.B.* Before invoking `Tensor.eval()`, its graph must have been launched in a session, and either a default session must be available, or `session` must be specified explicitly.
Parameters
IDictionary<object, object> feed_dict
A dictionary that maps `Tensor` objects to feed values. See tf.Session.run for a description of the valid feed values.
BaseSession session
(Optional.) The `Session` to be used to evaluate this tensor. If none, the default session will be used.
Returns
object
A numpy array corresponding to the value of this tensor.

object eval(IDictionary<object, object> feed_dict, _MonitoredSession session)

Evaluates this tensor in a `Session`.

Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor.

*N.B.* Before invoking `Tensor.eval()`, its graph must have been launched in a session, and either a default session must be available, or `session` must be specified explicitly.
Parameters
IDictionary<object, object> feed_dict
A dictionary that maps `Tensor` objects to feed values. See tf.Session.run for a description of the valid feed values.
_MonitoredSession session
(Optional.) The `Session` to be used to evaluate this tensor. If none, the default session will be used.
Returns
object
A numpy array corresponding to the value of this tensor.

object eval(BaseSession feed_dict, IDictionary<object, object> session)

Evaluates this tensor in a `Session`.

Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor.

*N.B.* Before invoking `Tensor.eval()`, its graph must have been launched in a session, and either a default session must be available, or `session` must be specified explicitly.
Parameters
BaseSession feed_dict
A dictionary that maps `Tensor` objects to feed values. See tf.Session.run for a description of the valid feed values.
IDictionary<object, object> session
(Optional.) The `Session` to be used to evaluate this tensor. If none, the default session will be used.
Returns
object
A numpy array corresponding to the value of this tensor.

object eval(BaseSession feed_dict, BaseSession session)

Evaluates this tensor in a `Session`.

Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor.

*N.B.* Before invoking `Tensor.eval()`, its graph must have been launched in a session, and either a default session must be available, or `session` must be specified explicitly.
Parameters
BaseSession feed_dict
A dictionary that maps `Tensor` objects to feed values. See tf.Session.run for a description of the valid feed values.
BaseSession session
(Optional.) The `Session` to be used to evaluate this tensor. If none, the default session will be used.
Returns
object
A numpy array corresponding to the value of this tensor.

object eval(BaseSession feed_dict, _MonitoredSession session)

Evaluates this tensor in a `Session`.

Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor.

*N.B.* Before invoking `Tensor.eval()`, its graph must have been launched in a session, and either a default session must be available, or `session` must be specified explicitly.
Parameters
BaseSession feed_dict
A dictionary that maps `Tensor` objects to feed values. See tf.Session.run for a description of the valid feed values.
_MonitoredSession session
(Optional.) The `Session` to be used to evaluate this tensor. If none, the default session will be used.
Returns
object
A numpy array corresponding to the value of this tensor.

object eval_dyn(object feed_dict, object session)

Evaluates this tensor in a `Session`.

Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor.

*N.B.* Before invoking `Tensor.eval()`, its graph must have been launched in a session, and either a default session must be available, or `session` must be specified explicitly.
Parameters
object feed_dict
A dictionary that maps `Tensor` objects to feed values. See tf.Session.run for a description of the valid feed values.
object session
(Optional.) The `Session` to be used to evaluate this tensor. If none, the default session will be used.
Returns
object
A numpy array corresponding to the value of this tensor.

object experimental_ref_dyn()

Returns a hashable reference object to this Tensor.

Warning: Experimental API that could be changed or removed.

The primary usecase for this API is to put tensors in a set/dictionary. We can't put tensors in a set/dictionary as `tensor.__hash__()` is no longer available starting Tensorflow 2.0. Instead, we can use `tensor.experimental_ref()`. Also, the reference object provides `.deref()` function that returns the original Tensor.
Show Example 
import tensorflow as tf 
 x = tf.constant(5)
y = tf.constant(10)
z = tf.constant(10) 
 # The followings will raise an exception starting 2.0
# TypeError: Tensor is unhashable if Tensor equality is enabled.
tensor_set = {x, y, z}
tensor_dict = {x: 'five', y: 'ten', z: 'ten'}

TensorShape get_shape_()

object get_shape__dyn()

_ArrayLike numpy()

Gets current value of the Tensor. Only works in eager mode.

For scalar tensors use scalar

object numpy_dyn()

Gets current value of the Tensor. Only works in eager mode.

void set_shape_(TensorShape shape)

object set_shape__dyn(object shape)

Public properties

object device get;

The name of the device on which this tensor will be produced, or None.

object device_dyn get;

The name of the device on which this tensor will be produced, or None.

object dtype get;

The `DType` of elements in this tensor.

object dtype_dyn get;

The `DType` of elements in this tensor.

object graph get;

The `Graph` that contains this tensor.

object graph_dyn get;

The `Graph` that contains this tensor.

Tensor Item get;

Gets tensor slice

object name get;

The string name of this tensor.

object name_dyn get;

The string name of this tensor.

object op get;

The `Operation` that produces this tensor as an output.

object op_dyn get;

The `Operation` that produces this tensor as an output.

ISet<string> OVERLOADABLE_OPERATORS get; set;

object OVERLOADABLE_OPERATORS_dyn get; set;

object PythonObject get;

TensorShape shape get;

Returns the `TensorShape` that represents the shape of this tensor.

The shape is computed using shape inference functions that are registered in the Op for each `Operation`. See tf.TensorShape for more details of what a shape represents.

The inferred shape of a tensor is used to provide shape information without having to launch the graph in a session. This can be used for debugging, and providing early error messages. For example: In some cases, the inferred shape may have unknown dimensions. If the caller has additional information about the values of these dimensions, `Tensor.set_shape()` can be used to augment the inferred shape.
Show Example 
c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) 
 print(c.shape)
==> TensorShape([Dimension(2), Dimension(3)]) 
 d = tf.constant([[1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]]) 
 print(d.shape)
==> TensorShape([Dimension(4), Dimension(2)]) 
 # Raises a ValueError, because `c` and `d` do not have compatible
# inner dimensions.
e = tf.matmul(c, d) 
 f = tf.matmul(c, d, transpose_a=True, transpose_b=True) 
 print(f.shape)
==> TensorShape([Dimension(3), Dimension(4)])

object shape_dyn get;

Returns the `TensorShape` that represents the shape of this tensor.

The shape is computed using shape inference functions that are registered in the Op for each `Operation`. See tf.TensorShape for more details of what a shape represents.

The inferred shape of a tensor is used to provide shape information without having to launch the graph in a session. This can be used for debugging, and providing early error messages. For example: In some cases, the inferred shape may have unknown dimensions. If the caller has additional information about the values of these dimensions, `Tensor.set_shape()` can be used to augment the inferred shape.
Show Example 
c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) 
 print(c.shape)
==> TensorShape([Dimension(2), Dimension(3)]) 
 d = tf.constant([[1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]]) 
 print(d.shape)
==> TensorShape([Dimension(4), Dimension(2)]) 
 # Raises a ValueError, because `c` and `d` do not have compatible
# inner dimensions.
e = tf.matmul(c, d) 
 f = tf.matmul(c, d, transpose_a=True, transpose_b=True) 
 print(f.shape)
==> TensorShape([Dimension(3), Dimension(4)])

int value_index get;

The index of this tensor in the outputs of its `Operation`.

object value_index_dyn get;

The index of this tensor in the outputs of its `Operation`.