|TensorFlow 1 version||View source on GitHub|
A tensor is a multidimensional array of elements represented by a
Compat aliases for migration
See Migration guide for more details.
tf.Tensor( op, value_index, dtype )
tf.Tensor object. All elements are of a single known data type.
When writing a TensorFlow program, the main object that is
manipulated and passed around is the
tf.Tensor has the following properties:
- a single data type (float32, int32, or string, for example)
- a shape
TensorFlow supports eager execution and graph execution. In eager execution, operations are evaluated immediately. In graph execution, a computational graph is constructed for later evaluation.
TensorFlow defaults to eager execution. In the example below, the matrix multiplication results are calculated immediately.
# Compute some values using a Tensor
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)
[3. 7.]], shape=(2, 2), dtype=float32)
Note that during eager execution, you may discover your
Tensors are actually
EagerTensor. This is an internal detail, but it does give you
access to a useful function,
tf.functions are a common way to define graph execution.
A Tensor's shape (that is, the rank of the Tensor and the si