Write a function as seen in the photo. Do NOT use any loops **Problem 3:**Write a function `softMax(x)` to return a 1D array of softmax function values \[f_i = \frac{e^{x_i - m}}{\sum_{j=0}^{K-1} e^{x_j - m}}\]for \( i = 0, 2, \cdots, K-1 \), where \( x \) is a 1D array of \( K \) floats, \( x_i \) is the \((i+1)\)-th element of array \( x \), and \( m \) is the largest element in array \( x \).**Sample:** If `z = np.array([10, 5, 8])`, then `softMax(z)` returns `array([0.876, 0.006, 0.118])`.---```pythonpassdef softMax(x): z = np.array([10, 5, 8]) softMax(z)```**Explanation:**This exercise describes how to implement the softmax function, which is a common activation function in machine learning. The formula involves computing exponential values and normalizing them by their sum, adjusted by the maximum value \( m \) to improve numerical stability. The given code, however, only includes a placeholder `pass` and an example array `z` is passed into the not-yet-defined `softMax(z)` function.

Given : Given the array values. Output : Find the softmax values.

1 Problem 3. Write a function `softMax (x)` to return a 1darray of softmax function values \\(f_i = \frac{e^{x_i - m}}{\sum_{j=0}^{K-1}e^{x_j-m}}\\) for \\(i = 0, 2, \cdots, K-1\\), where \\(x\\) is a 1darray of \\(K\\) floats, \\(x_{i}\\) is the \\((i+1)\\)-th element of array \\(x\\), and \\(m\\) is the largest element in array \\(x\\). Sample: if `z = np.array([10, 5, 8])`, then `softMax(z)` returns `array([0.876, 0.006, 0.118])`. 1 pass 3 def softMax(x): 5 z = np.array([10, 5, 8]) 6 softMax(z)

1 Problem 3. Write a function `softMax (x)` to return a 1darray of softmax function values \\(f_i = \frac{e^{x_i - m}}{\sum_{j=0}^{K-1}e^{x_j-m}}\\) for \\(i = 0, 2, \cdots, K-1\\), where \\(x\\) is a 1darray of \\(K\\) floats, \\(x_{i}\\) is the \\((i+1)\\)-th element of array \\(x\\), and \\(m\\) is the largest element in array \\(x\\). Sample: if `z = np.array([10, 5, 8])`, then `softMax(z)` returns `array([0.876, 0.006, 0.118])`. 1 pass 3 def softMax(x): 5 z = np.array([10, 5, 8]) 6 softMax(z)

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Question

100%

Write a function as seen in the photo. Do NOT use any loops

**Problem 3:**

Write a function `softMax(x)` to return a 1D array of softmax function values

\[
f_i = \frac{e^{x_i - m}}{\sum_{j=0}^{K-1} e^{x_j - m}}
\]

for \( i = 0, 2, \cdots, K-1 \), where \( x \) is a 1D array of \( K \) floats, \( x_i \) is the \((i+1)\)-th element of array \( x \), and \( m \) is the largest element in array \( x \).

**Sample:** If `z = np.array([10, 5, 8])`, then `softMax(z)` returns `array([0.876, 0.006, 0.118])`.

---

```python
pass

def softMax(x):
z = np.array([10, 5, 8])
softMax(z)
```

**Explanation:**

This exercise describes how to implement the softmax function, which is a common activation function in machine learning. The formula involves computing exponential values and normalizing them by their sum, adjusted by the maximum value \( m \) to improve numerical stability. The given code, however, only includes a placeholder `pass` and an example array `z` is passed into the not-yet-defined `softMax(z)` function.

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