[SOLVED] CS

CSCC11 Introduction to Machine Learning, Winter 2021, Assignment 3
B. Chan, Z. Zhang, D. Fleet

import _pickle as pickle
import numpy as np

def softmax(logits):
This function applies softmax function to the logits.

Args:
logits (ndarray (shape: (N, K))): A NxK matrix consisting N K-dimensional logits.

Output:
(ndarray (shape: (N, K))): A NxK matrix consisting N K-categorical distribution.

e_logits = np.exp(logits np.max(logits, axis=1, keepdims=True))
return e_logits / np.sum(e_logits, axis=1, keepdims=True)

def load_pickle_dataset(file_path):
This function loads a pickle file given a file path.

Args:
file_path (str): The path of the pickle file

Output:
(dict): A dictionary consisting the dataset content.

return pickle.load(open(file_path, rb))