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CSCC11 – Introduction to Machine Learning, Winter 2021, Assignment 3
B. Chan, Z. Zhang, D. Fleet
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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.
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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.
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return pickle.load(open(file_path, “rb”))