Returns a tensor containing the shape of the input tensor.

See also tf.size, tf.rank.

tf.shape returns a 1-D integer tensor representing the shape of input. For a scalar input, the tensor returned has a shape of (0,) and its value is the empty vector (i.e. []).

For example:

<tf.Tensor: shape=(0,), dtype=int32, numpy=array([], dtype=int32)>
t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])
<tf.Tensor: shape=(3,), dtype=int32, numpy=array([2, 2, 3], dtype=int32)>
a = tf.keras.layers.Input((None, 10))
<... shape=(3,) dtype=int32...>

In these cases, using tf.Tensor.shape will return more informative results.

TensorShape([None, None, 10])

(The first None represents the as yet unknown batch size.)

tf.shape and Tensor.shape should be identical in eager mode. Within tf.function or within a compat.v1 context, not all dimensions may be known until execution time. Hence, when defining custom layers and models for graph mode, prefer the dynamic tf.shape(x) over the static x.shape.

input A Tensor or SparseTensor.
out_type (Optional) The specified output type of the operation (int32 or int64). Defaults to tf.int32. (Note: there is an experimental flag, tf_shape_default_int64 that changes the default to tf.int64. This is an unsupported, experimental setting that causes known breakages.)
name A name for the operation (optional).

A Tensor of type out_type.