Enrollments closing soon for Post Graduate Certificate Program in Applied Data Science & AI By IIT Roorkee | 3 Seats Left
Login using Social Account
Login using your credentials
We need lower-level Python API in TensorFlow when extra control is need for writing:
Custom loss functions.
Custom metrics.
Custom models.
All of the above.
Want to create exercises like this yourself? Click here.
Note - Having trouble with the assessment engine? Follow the steps listed here
No hints are availble for this assesment
1 Custom Models and Training with Tensorflow - Custom Models and Training - Part I
2 Custom Models and Training with Tensorflow - Custom Models and Training - Part II
3 MCQ - Writing a custom training loop will make your code longer, more error-prone, and harder to maintain?
4 MCQ - We can add a scalar value to a tensor?
5 MCQ - The Huber loss is not currently part of the official Keras API, but it is available in tf.keras?
6 MCQ - You cannot create a tensor from a NumPy array?
7 MCQ - You can create a tensor with tf.constant()?
8 MCQ - A TensorFlow tensor is usually a multidimensional array, but it can also hold a scalar?
9 MCQ - TensorFlow Lite runs only on Mac devices?
10 MCQ - TensorFlow's JIT compiler allows it to optimize computations for speed and memory usage?
11 MCQ - TensorFlow has a core similar to Numpy?
12 MCQ - We need lower-level Python API in TensorFlow when extra control is need for writing:
Loading comments...