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

1 Problem 3. Write a function `softMax(x)` to return a 1darray of softmax function values \\(f_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 1\(x\\), Sample: if `z = \frac{e^{x_i - %3D np.array( [10, 5, 8])`, then softMax(z)` returns `array( [0.876, 0.006, 0.118])`. 1 pass 2 3 def softMax (x): 4 5 z = np.array( [10, 5, 8]) 6 softMax(z)
Transcribed Image Text:1 Problem 3. Write a function `softMax(x)` to return a 1darray of softmax function values \\(f_i m}}{\sum_{j=0}^{K-1}e^{x_j-m}}\\) for \\(i = 0, 2, \cdots, K-1\\), where \\(x\) 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 1\(x\\), Sample: if `z = \frac{e^{x_i - %3D np.array( [10, 5, 8])`, then softMax(z)` returns `array( [0.876, 0.006, 0.118])`. 1 pass 2 3 def softMax (x): 4 5 z = np.array( [10, 5, 8]) 6 softMax(z)
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