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Chapter 11 This dataset comes from a popular dataset called CIFAR-10. It contains 60,000 images that are 32 pixels wide and 32 pixels high, with each pixel having a red-green-blue (RGB) value. The dataset is already split into training and testing, although we will not use the testing dataset until after we complete our training. The CIFAR-10 dataset is available for download at: http://www. cs.toronto.edu/~kriz/cifar.html. Download the python version, which has already been converted to NumPy arrays. Opening a new IPython Notebook, we can see what the data looks like. First, we set up the data filenames. We will only worry about the first batch to start with, and scale up to the full dataset size towards the end; import os data_folder = os.path.join(os.path.expanduser("~"), "Data", "cifar-10- batches-py") batch1_filename = os.path.join(data_folder, "data_batch_1") Next, we create a function that can read the data stored in the batches. The batches have been saved using pickle, which is a python library to save objects. Usually, we can just call pickle.load on the file to get the object. However, there is a small issue with this data: it was saved in Python 2, but we need to open it in Python 3. In order to address this, we set the encoding to latin (even though we are opening it in byte mode): import pickle # Bigfix thanks to: http://stackoverflow.com/questions/11305790/ pickle-incompatability-of-numpy-arrays-between-python-2-and-3 def unpickle(filename): with open(filename, 'rb') as fo: return pickle.load(fo, encoding='latin1') Using this function, we can now load the batch dataset: batch1 = unpickle(batch1_filename) This batch is a dictionary, containing the actual data in NumPy arrays, the corresponding labels and filenames, and finally a note to say which batch it is (this is training batch 1 of 5, for instance). We can extract an image by using its index in the batch's data key: image_index = 100 image = batch1['data'][image_index] [ 243 ]
Classifying Objects in Images Using Deep Learning The image array is a NumPy array with 3,072 entries, from 0 to 255. Each value is the red, green, or blue intensity at a specific location in the image. The images are in a different format than what matplotlib usually uses (to display images), so to show the image we first need to reshape the array and rotate the matrix. This doesn't matter so much to train our neural network (we will define our network in a way that fits with the data), but we do need to convert it for matplotlib's sake: image = image.reshape((32,32, 3), order='F') import numpy as np image = np.rot90(image, -1) Now we can show the image using matplotlib: %matplotlib inline from matplotlib import pyplot as plt plt.imshow(image) The resulting image, a boat, is displayed: The resolution on this image is quite poor—it is only 32 pixels wide and 32 pixels high. Despite that, most people will look at the image and see a boat. Can we get a computer to do the same? [ 244 ]
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Learning Data Mining with Python Co
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About the Author Robert Layton has
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Christophe Van Gysel is pursuing a
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www.allitebooks.com
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Table of Contents Preprocessing usi
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Table of Contents Chapter 7: Discov
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Table of Contents GPU optimization
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Preface If you have ever wanted to
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What you need for this book It shou
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Preface Reader feedback Feedback fr
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Getting Started with Data Mining We
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Chapter 1 In the preceding dataset,
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After you have the above "Hello, wo
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Chapter 1 Windows users may need to
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Chapter 1 The dataset we are going
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Chapter 1 As an example, we will co
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We get the names of the features fo
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Chapter 1 Two rules are near the to
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Chapter 1 The scikit-learn library
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We then iterate over all the sample
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Chapter 1 Overfitting is the proble
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Chapter 1 Summary In this chapter,
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Classifying with scikit-learn Estim
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Predicting Sports Winners with Deci
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Recommending Movies Using Affinity
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Chapter 4 The classic algorithm for
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Chapter 4 When loading the file, we
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Chapter 4 We will sample our datase
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Chapter 4 Implementation On the fir
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Chapter 4 We want to break out the
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The process starts by creating dict
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movie_name_data.columns = ["MovieID
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To do this, we will compute the tes
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Chapter 4 - Train Confidence: 1.000
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Extracting Features with Transforme
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Chapter 5 Thought should always be
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Chapter 5 Other features describe a
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Chapter 5 Similarly, we can convert
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Chapter 5 [18, 19, 20], [21, 22, 23
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Chapter 5 Next, we create our trans
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Chapter 5 This returns a different
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Also, we want to set the final colu
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Chapter 5 The downside to transform
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Chapter 5 A transformer is akin to
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We can then create an instance of t
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Chapter 5 Putting it all together N
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Social Media Insight Using Naive Ba
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Discovering Accounts to Follow Usin
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Chapter 7 Next, we will need a list
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Chapter 7 Make sure the filename is
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Chapter 7 cursor = results['next_cu
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Chapter 7 Next, we are going to rem
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Chapter 7 Creating a graph Now, we
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Chapter 7 As you can see, it is ver
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Chapter 7 Next, we will only add th
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Chapter 7 The difference in this gr
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Chapter 7 We can graph the entire s
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Chapter 7 Optimizing criteria Our a
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Chapter 7 Next, we need to get the
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• method='nelder-mead': This is u
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Beating CAPTCHAs with Neural Networ
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Chapter 8 The red lines indicate th
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Chapter 8 The combination of an app
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Chapter 8 Next we set the font of t
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Chapter 8 We can then extract the s
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Chapter 8 Our targets are integer v
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Chapter 8 Then we iterate over our
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Chapter 8 From these predictions, w
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Chapter 8 This code correctly predi
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The result is shown in the next gra
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Chapter 8 However, it isn't very go
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Chapter 8 Summary In this chapter,
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Authorship Attribution Attributing
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Authorship Attribution If we cannot
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Authorship Attribution After taking
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Working with Big Data Left-click th
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Working with Big Data The result is
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Next Steps… Extending the IPython
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Next Steps… Chapter 3: Predicting
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Next Steps… Vowpal Wabbit http://
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Next Steps… Deeper networks These
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Next Steps… Real-time clusterings
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Next Steps… More resources Kaggle
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authorship, attributing 185-188 AWS
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feature extraction about 82 common
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NetworkX about 145 defining 303 URL
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scikit-learn package references 305
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Thank you for buying Learning Data
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Learning Python Data Visualization
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