MNIST Dataset Introduction
Most examples are using MNIST dataset of handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).
Overview
Usage
In our examples, we are using TensorFlow input_data.py script to load that dataset. It is quite useful for managing our data, and handle:
Dataset downloading
Loading the entire dataset into numpy array:
# Import MNIST
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# Load data
X_train = mnist.train.images
Y_train = mnist.train.labels
X_test = mnist.test.images
Y_test = mnist.test.labels
- A
next_batchfunction that can iterate over the whole dataset and return only the desired fraction of the dataset samples (in order to save memory and avoid to load the entire dataset).
# Get the next 64 images array and labels
batch_X, batch_Y = mnist.train.next_batch(64)