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Updates the shape of a tensor and checks at runtime that the shape holds.
tf.ensure_shape(
x, shape, name=None
)
When executed, this operation asserts that the input tensor x
's shape
is compatible with the shape
argument.
See tf.TensorShape.is_compatible_with
for details.
x = tf.constant([[1, 2, 3],
[4, 5, 6]])
x = tf.ensure_shape(x, [2, 3])
Use None
for unknown dimensions:
x = tf.ensure_shape(x, [None, 3])
x = tf.ensure_shape(x, [2, None])
If the tensor's shape is not compatible with the shape
argument, an error
is raised:
x = tf.ensure_shape(x, [5])
Traceback (most recent call last):
tf.errors.InvalidArgumentError: Shape of tensor dummy_input [3] is not
compatible with expected shape [5]. [Op:EnsureShape]
During graph construction (typically tracing a tf.function
),
tf.ensure_shape
updates the staticshape of the result tensor by
merging the two shapes. See tf.TensorShape.merge_with
for details.
This is most useful when you know a shape that can't be determined statically by TensorFlow.
The following trivial tf.function
prints the input tensor's
staticshape before and after ensure_shape
is applied.
@tf.function
def f(tensor):
print("Staticshape before:", tensor.shape)
tensor = tf.ensure_shape(tensor, [None, 3])
print("Staticshape after:", tensor.shape)
return tensor
This lets you see the effect of tf.ensure_shape
when the function is traced:
>>> cf = f.get_concrete_function(tf.TensorSpec([None, None]))
Staticshape before: (None, None)
Staticshape after: (None, 3)
cf(tf.zeros([3, 3])) # Passes
cf(tf.constant([1, 2, 3])) # fails
Traceback (most recent call last):
InvalidArgumentError: Shape of tensor x [3] is not compatible with expected shape [3,3].
The above example raises tf.errors.InvalidArgumentError
, because x
's
shape, (3,)
, is not compatible with the shape
argument, (None, 3)
Inside a tf.function
or v1.Graph
context it checks both the buildtime and
runtime shapes. This is stricter than tf.Tensor.set_shape
which only
checks the buildtime shape.
For example, of loading images of a known size:
@tf.function
def decode_image(png):
image = tf.image.decode_png(png, channels=3)
# the `print` executes during tracing.
print("Initial shape: ", image.shape)
image = tf.ensure_shape(image,[28, 28, 3])
print("Final shape: ", image.shape)
return image
When tracing a function, no ops are being executed, shapes may be unknown. See the Concrete Functions Guide for details.
concrete_decode = decode_image.get_concrete_function(
tf.TensorSpec([], dtype=tf.string))
Initial shape: (None, None, 3)
Final shape: (28, 28, 3)
image = tf.random.uniform(maxval=255, shape=[28, 28, 3], dtype=tf.int32)
image = tf.cast(image,tf.uint8)
png = tf.image.encode_png(image)
image2 = concrete_decode(png)
print(image2.shape)
(28, 28, 3)
image = tf.concat([image,image], axis=0)
print(image.shape)
(56, 28, 3)
png = tf.image.encode_png(image)
image2 = concrete_decode(png)
Traceback (most recent call last):
tf.errors.InvalidArgumentError: Shape of tensor DecodePng [56,28,3] is not
compatible with expected shape [28,28,3].
@tf.function
def bad_decode_image(png):
image = tf.image.decode_png(png, channels=3)
# the `print` executes during tracing.
print("Initial shape: ", image.shape)
# BAD: forgot to use the returned tensor.
tf.ensure_shape(image,[28, 28, 3])
print("Final shape: ", image.shape)
return image
image = bad_decode_image(png)
Initial shape: (None, None, 3)
Final shape: (None, None, 3)
print(image.shape)
(56, 28, 3)
Args  

x

A Tensor .

shape

A TensorShape representing the shape of this tensor, a
TensorShapeProto , a list, a tuple, or None.

name

A name for this operation (optional). Defaults to "EnsureShape". 
Returns  

A Tensor . Has the same type and contents as x .

Raises  

tf.errors.InvalidArgumentError

If shape is incompatible with the shape
of x .
