September 2019
Intermediate to advanced
420 pages
10h 29m
English
book
Machine Learning for OpenCV 4 - Second Edition
by Aditya Sharma, Michael Beyeler (USD), Vishwesh Ravi Shrimali, Michael Beyeler
Content preview from Machine Learning for OpenCV 4 - Second Edition
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Loading the MNIST dataset
The easiest way to obtain the MNIST dataset is by using Keras:
In [1]: from keras.datasets import mnist... (X_train, y_train), (X_test, y_test) = mnist.load_data()Out[1]: Using TensorFlow backend. Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz
This will download the data from Amazon Cloud (might take a while depending on your internet connection) and automatically split the data into training and test sets.
MNIST provides its own predefined train-test split. This way, it is easier to compare the performance of different classifiers because they will all use the same data for training and the same data for testing.
This data comes in a format that we are already familiar with:
In [2]: X_train.shape, ...
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