|TensorFlow 1 version||View source on GitHub|
tf.Tensor represents a multidimensional array of elements.
See Migration guide for more details.
tf.Tensor( op, value_index, dtype )
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 size of
each dimension) may not always be fully known. In
definitions, the shape may only be partially known.
Most operations produce tensors of fully-known shapes if the shapes of their inputs are also fully known, but in some cases it's only possible to find the shape of a tensor at execution time.
a = np.array([1, 2, 3]) b = tf.constant(a) a = 4 print(b) # tf.Tensor([4 2 3], shape=(3,), dtype=int64)