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101 lines
3.3 KiB
101 lines
3.3 KiB
3 years ago
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# datasets.py
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# -----------
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# Licensing Information: You are free to use or extend these projects for
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# educational purposes provided that (1) you do not distribute or publish
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# solutions, (2) you retain this notice, and (3) you provide clear
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# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
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#
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# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
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# The core projects and autograders were primarily created by John DeNero
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# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
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# Student side autograding was added by Brad Miller, Nick Hay, and
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# Pieter Abbeel (pabbeel@cs.berkeley.edu).
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import numpy as np
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from tensorflow.examples.tutorials.mnist import input_data
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from util import normal
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_MNIST = None
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def datasetA():
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training = np.array([[-3,3],[-3,0],[-1,1],[1,5],[3,4],[1,-1],[3,2],[5,0]])
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trainingLabels = np.ones((8,2))
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trainingLabels[:4,0] = 0
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trainingLabels[4:,1] = 0
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validation = np.array([[3,0]])
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validationLabels = np.array([[1,0]])
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test = np.array([[-1,2]])
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testLabels = np.array([[0,1]])
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return [training,trainingLabels, validation, validationLabels, test, testLabels]
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def datasetB():
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X_1 = normal(shape=(100, 2), mean=1.5)*3
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X_2 = normal(shape=(100, 2), mean=-1)*3
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training = np.r_[X_1[:50], X_2[:50]]
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trainingLabels = np.ones((100,2))
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trainingLabels[50:,0] = 0
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trainingLabels[:50,1] = 0
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validation = np.r_[X_1[50:80], X_2[50:80]]
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validationLabels = np.ones((60,2))
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validationLabels[30:,0] = 0
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validationLabels[:30,1] = 0
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test = np.r_[X_1[80:], X_2[80:]]
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testLabels = np.ones((40,2))
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testLabels[20:, 0] = 0
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testLabels[:20, 1] = 0
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return [training, trainingLabels, validation, validationLabels, test, testLabels]
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def tinyDataset():
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training = np.array([[0,0,0], [1,0,0], [0,1,0], [0,0,1], [1,1,0], [0,1,1], [1,0,1], [1,1,1]])
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trainingLabels = np.array([[0,1], [0,1], [0,1], [0,1], [0,1], [0,1], [1,0], [1,0]])
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validation = np.array([[1,0,1]])
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validationLabels = np.array([[0,1]])
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test = np.array([[1,0,1]])
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testLabels = np.array([[1,0]])
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return [training, trainingLabels, validation, validationLabels, test, testLabels]
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def mnistDataset():
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global _MNIST
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if _MNIST is None:
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mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
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_MNIST = (mnist.train.images, mnist.train.labels,
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mnist.validation.images, mnist.validation.labels,
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mnist.test.images, mnist.test.labels)
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return [data.copy() for data in _MNIST]
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def largeMnistDataset():
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all_data = mnistDataset()
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training = all_data[0][:2000]
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trainingLabels = all_data[1][:2000]
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validation = all_data[2][:1000]
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validationLabels = all_data[3][:1000]
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test = all_data[4][:1000]
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testLabels = all_data[5][:1000]
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return [training, trainingLabels, validation, validationLabels, test, testLabels]
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def medMnistDataset():
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all_data = mnistDataset()
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training = all_data[0][:1000]
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trainingLabels = all_data[1][:1000]
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validation = all_data[2][:200]
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validationLabels = all_data[3][:200]
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test = all_data[4][:200]
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testLabels = all_data[5][:200]
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return [training, trainingLabels, validation, validationLabels, test, testLabels]
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def tinyMnistDataset():
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all_data = mnistDataset()
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return [data[:10] for data in all_data]
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