tf.compat.v1.enable_v2_tensorshape

В TensorFlow 2.0 итерация по экземпляру TensorShape возвращает значения.

tf.compat.v1.enable_v2_tensorshape()

Это включает новый режим работы.

Конкретно, tensor_shape[i] возвращало экземпляр Dimension в V1, но в V2 возвращает либо целое число, либо None.

Примеры:

#######################
# If you had this in V1:
value = tensor_shape[i].value

# Do this in V2 instead:
value = tensor_shape[i]

#######################
# If you had this in V1:
for dim in tensor_shape:
  value = dim.value
  print(value)

# Do this in V2 instead:
for value in tensor_shape:
  print(value)

#######################
# If you had this in V1:
dim = tensor_shape[i]
dim.assert_is_compatible_with(other_shape)  # or using any other shape method

# Do this in V2 instead:
if tensor_shape.rank is None:
  dim = Dimension(None)
else:
  dim = tensor_shape.dims[i]
dim.assert_is_compatible_with(other_shape)  # or using any other shape method

# The V2 suggestion above is more explicit, which will save you from
# the following trap (present in V1):
# you might do in-place modifications to `dim` and expect them to be reflected
# in `tensor_shape[i]`, but they would not be.