# tf.Tensor

### class tf.Tensor

See the guide: Building Graphs > Core graph data structures

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.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.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:

# 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.Session()

# Execute the graph and store the value that e represents in result.
result = sess.run(e)


## Properties

### device

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

### dtype

The DType of elements in this tensor.

### graph

The Graph that contains this tensor.

### name

The string name of this tensor.

### op

The Operation that produces this tensor as an output.

### shape

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:

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)])


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.

#### Returns:

A TensorShape representing the shape of this tensor.

### value_index

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

## Methods

### __init__(op, value_index, dtype)

Creates a new Tensor.

#### Args:

• op: An Operation. Operation that computes this tensor.
• value_index: An int. Index of the operation's endpoint that produces this tensor.
• dtype: A DType. Type of elements stored in this tensor.

#### Raises:

• TypeError: If the op is not an Operation.

### consumers()

Returns a list of Operations that consume this tensor.

#### Returns:

A list of Operations.

### eval(feed_dict=None, session=None)

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.

#### Args:

• feed_dict: A dictionary that maps Tensor objects to feed values. See tf.Session.run for a description of the valid feed values.
• session: (Optional.) The Session to be used to evaluate this tensor. If none, the default session will be used.

#### Returns:

A numpy array corresponding to the value of this tensor.

### get_shape()

Alias of Tensor.shape.

### set_shape(shape)

Updates the shape of this tensor.

This method can be called multiple times, and will merge the given shape with the current shape of this tensor. It can be used to provide additional information about the shape of this tensor that cannot be inferred from the graph alone. For example, this can be used to provide additional information about the shapes of images:

_, image_data = tf.TFRecordReader(...).read(...)
image = tf.image.decode_png(image_data, channels=3)

# The height and width dimensions of image are data dependent, and
# cannot be computed without executing the op.
print(image.shape)
==> TensorShape([Dimension(None), Dimension(None), Dimension(3)])

# We know that each image in this dataset is 28 x 28 pixels.
image.set_shape([28, 28, 3])
print(image.shape)
==> TensorShape([Dimension(28), Dimension(28), Dimension(3)])


#### Args:

• shape: A TensorShape representing the shape of this tensor.

#### Raises:

• ValueError: If shape is not compatible with the current shape of this tensor.

## Class Members

### OVERLOADABLE_OPERATORS

Defined in tensorflow/python/framework/ops.py.