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Updated content of the book#427
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kavyasrinet merged 1 commit intoPaddlePaddle:developfrom Sep 27, 2017
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Sep 27, 2017
| where $ \text{softmax}(x_i) = \frac{e^{x_i}}{\sum_j e^{x_j}} $ | ||
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| For an $N$-class classification problem with $N$ output nodes, Softmax normalizes the resulting $N$ dimensional vector so that each of its entries falls in the range $[0,1]\in\math{R}$, representing the probability that the sample belongs to a certain class. Here $y_i$ denotes the predicted probability that an image is of digit $i$. | ||
| For an $N$-class classification problem with $N$ output nodes, Softmax normalizes the resulting $N$ dimensional vector so that each of its entries falls in the range $[0,1]\in {R}$, representing the probability that the sample belongs to a certain class. Here $y_i$ denotes the predicted probability that an image is of digit $i$. |
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nice catch! In online latex, it shows correctly, It But it really failed in the paddle book website.
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This is for the MNIST chapter