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What is the difference between a sigmoid followed by the cross entropy and sigmoid_cross_entropy_with_logits in TensorFlow?

score 44674 · Answer 1 · 2017-09-19 03:23:43

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What is the difference between a sigmoid followed by the cross entropy and sigmoid_cross_entropy_with_logits in TensorFlow?

44.6k views Asked by D.S.H.J At 19 September 2017 at 03:23

score 2518 · Answer 2 · 2020-10-06 00:44:04

Tensorflow 2.2 [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (dense_1/Sigmoid:0) = ]

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score 1011 · Answer 3 · 2020-10-06 00:53:55

How to get numpy.exp to work with an array as well as a scalar?

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score 44 · Answer 4 · 2020-10-15 13:57:46

Is there a way to round very large fractions like 3.7625745528893614e-83?

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score 548 · Answer 5 · 2020-10-21 08:54:31

Why XOR problem works better with bipolar representation?

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score 917 · Answer 6 · 2020-10-20 12:42:06

Simulate sklearn logistic regression predict_proba with only coefficients and intercept

917 views Asked by United121 At 20 October 2020 at 12:42

score 18 · Answer 7 · 2023-12-10 16:47:30

ValueError: Input 0 of layer "sequential_17" is incompatible with the layer: expected shape=(None, 6), found shape=(None, 5)

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My one neuron neural network does not work with my dataset

38 views Asked by Ahmet Rauf Oktay At 19 December 2023 at 11:53

score 3607 · Answer 9 · 2020-12-11 20:52:17

Activation functions: Softmax vs Sigmoid

3.6k views Asked by bearthum At 11 December 2020 at 20:52

score 5671 · Answer 10 · 2020-12-15 14:25:55

Why is the decoder in an autoencoder uses a sigmoid on the last layer?

5.6k views Asked by Gulzar At 15 December 2020 at 14:25

TechQA.

List Question

What is the difference between a sigmoid followed by the cross entropy and sigmoid_cross_entropy_with_logits in TensorFlow?

Tensorflow 2.2 [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (dense_1/Sigmoid:0) = ]

How to get numpy.exp to work with an array as well as a scalar?

Is there a way to round very large fractions like 3.7625745528893614e-83?

Why XOR problem works better with bipolar representation?

Simulate sklearn logistic regression predict_proba with only coefficients and intercept

ValueError: Input 0 of layer "sequential_17" is incompatible with the layer: expected shape=(None, 6), found shape=(None, 5)

My one neuron neural network does not work with my dataset

Activation functions: Softmax vs Sigmoid

Why is the decoder in an autoencoder uses a sigmoid on the last layer?

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