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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.
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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:
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