tf.ensure_shape     View source on GitHub    Updates the shape of a tensor and checks at runtime that the shape holds.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.ensure_shape  
tf.ensure_shape(
    x, shape, name=None
)
 For example: 
@tf.function(input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)])
def f(tensor):
  return tf.ensure_shape(tensor, [3, 3])

f(tf.zeros([3, 3])) # Passes
<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([[0., 0., 0.],
       [0., 0., 0.],
       [0., 0., 0.]], dtype=float32)>
f([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 the shape (3,) is not compatible with the shape (None, 3, 3) With eager execution this is a shape assertion, that returns the input: 
x = tf.constant([1,2,3])
print(x.shape)
(3,)
x = tf.ensure_shape(x, [3])
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]
 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. 
Note: This differs from tf.Tensor.set_shape in that it sets the static shape of the resulting tensor and enforces it at runtime, raising an error if the tensor's runtime shape is incompatible with the specified shape. tf.Tensor.set_shape sets the static shape of the tensor without enforcing it at runtime, which may result in inconsistencies between the statically-known shape of tensors and the runtime value of tensors.
 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].
 Caution: if you don't use the result of tf.ensure_shape the check may not run. 
@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.