Inaccuracy in compute_cost in DLS2 W3A1 Tensorflow_introduction

Course Q&A Deep Learning Specialization Improving Deep Neural Networks: Hyperparameter tun
1

My code prints out

tf.Tensor(0.81028694, shape=(), dtype=float32)

While the expected output is

tf.Tensor(0.810287, shape=(), dtype=float32)

The test code is:

assert (np.abs(result - (0.50722074 + 1.1133534) / 2.0) < 1e-7), “Test does not match. Did you get the reduce sum of your loss functions?”

Note that “(0.50722074 + 1.1133534) / 2.0)” calculates to 0.81028707, so it’s 0.00000013 or 1.3e-7 from 0.81028694.

I believe the Assert Condition is too strict. “1e-7” can be changed to “1e-6” to make it pass.

Separately, I don’t understand why my answer is not exactly the same as the standard answer. If anyone has an idea, please let me know.

2

I’m guessing that you manually applied softmax, rather than using the from_logits argument to force the cost function to apply the softmax. That gives different behavior precisely because using from_logits is more numerically stable.

1 Like
3

Yes you are right. I tried to use “from_logits=True” instead and it passed the test! Thanks!

4

Great! Here’s an earlier thread about this same point. And here’s another one that is relevant.