You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
49 lines
1.6 KiB
49 lines
1.6 KiB
# answers.py
|
|
# ----------
|
|
# Licensing Information: You are free to use or extend these projects for
|
|
# educational purposes provided that (1) you do not distribute or publish
|
|
# solutions, (2) you retain this notice, and (3) you provide clear
|
|
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
|
|
#
|
|
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
|
|
# The core projects and autograders were primarily created by John DeNero
|
|
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
|
|
# Student side autograding was added by Brad Miller, Nick Hay, and
|
|
# Pieter Abbeel (pabbeel@cs.berkeley.edu).
|
|
|
|
|
|
import util
|
|
|
|
|
|
def q2():
|
|
"*** YOUR CODE HERE ***"
|
|
return 'b'
|
|
|
|
def q3():
|
|
"*** YOUR CODE HERE ***"
|
|
return 'a'
|
|
|
|
def q7():
|
|
"*** YOUR CODE HERE ***"
|
|
return 'both'
|
|
|
|
def q10():
|
|
"""
|
|
Returns a dict of hyperparameters.
|
|
|
|
Returns:
|
|
A dict with the learning rate and momentum.
|
|
|
|
You should find the hyperparameters by empirically finding the values that
|
|
give you the best validation accuracy when the model is optimized for 1000
|
|
iterations. You should achieve at least a 97% accuracy on the MNIST test set.
|
|
"""
|
|
hyperparams = dict()
|
|
hyperparams['learning_rate'] = 0.01
|
|
hyperparams['momentum'] = 0.9
|
|
"*** YOUR CODE HERE ***"
|
|
# filter out any item in the dict that is not the learning rate nor momentum
|
|
allowed_hyperparams = ['learning_rate', 'momentum']
|
|
hyperparams = dict([(k, v) for (k, v) in hyperparams.items() if k in allowed_hyperparams])
|
|
return hyperparams
|