What is the utility of `Tensor` (as opposed to `EagerTensor`) in Tensorflow 2.0?

Question

In Tensorflow 2.0, the main "tensors" we see are in fact EagerTensors (tensorflow.python.framework.ops.EagerTensor to be more precise):

x = [[2.]]
m = tf.matmul(x, x)
type(m)
# returns tensorflow.python.framework.ops.EagerTensor

But, in some cases, we have the symbolic Tensor object (tensorflow.python.framework.ops.Tensor), as in TF1.X.
For example in keras:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))
type(model.outputs[0])
# returns tensorflow.python.framework.ops.Tensor

So, what are the use for these symbolic:tensorflow.python.framework.ops.Tensor in Tensorflow:

• In the TF library internals: Keras is at least using these tensors, but is it used at other places (which are using a graph, like tf.function, or tf.data.Dataset)?
• In the API: is there an actual use for end-users of these?

Answer 1

In the TF library internals: Keras is at least using these tensors, but is it used at other places (which are using a graph, like tf.function, or tf.data.Dataset)?

Well, yes. Your instinct is correct here. EagreTensor represents a tensor who's value has been calculated in eager mode whereas Tensor represents a tensor node in a graph that may not yet have been calculated.

In the API: is there an actual use for end-users of these?

Well, on some level we use them all the time. We create keras models, tf.data.Dataset pipelines etc. but, really, for the vast majority of use-cases we don't tend to instantiate or interact directly with the Tensor object itself though so probably just want to not worry about the object type and consider them to be an implementation detail internal to tensorflow.