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Chapter 1
For example, the digit 3 can be encoded as [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]. This type of
representation is called One-hot encoding, or sometimes simply one-hot, and is very
common in data mining when the learning algorithm is specialized in dealing with
numerical functions.
Defining a simple neural network
in TensorFlow 2.0
In this section, we use TensorFlow 2.0 to define a network that recognizes
MNIST handwritten digits. We start with a very simple neural network and then
progressively improve it.
Following Keras style, TensorFlow 2.0 provides suitable libraries (https://www.
tensorflow.org/api_docs/python/tf/keras/datasets) for loading the dataset
and splits it into training sets, X_train, used for fine-tuning our net, and test sets,
X_test, used for assessing the performance. Data is converted into float32 to use
32-bit precision when training a neural network and normalized to the range [0,1].
In addition, we load the true labels into Y_train and Y_test respectively, and
perform a one-hot encoding on them. Let's see the code.
For now, do not focus too much on understanding why certain parameters have
specific assigned values, as these choices will be discussed throughout the rest of the
book. Intuitively, EPOCH defines how long the training should last, BATCH_SIZE is
the number of samples you feed in to your network at a time, and VALIDATION is the
amount of data reserved for checking or proving the validity of the training process.
The reason why we picked EPOCHS = 200, BATCH_SIZE = 128, VALIDATION_
SPLIT=0.2, and N_HIDDEN = 128 will be clearer later in this chapter when we will
explore different values and discuss hyperparameter optimization. Let's look at our
first code fragment of a neural network in TensorFlow. Reading is intuitive but you
will find a detailed explanation in the following pages:
import tensorflow as tf
import numpy as np
from tensorflow import keras
# Network and training parameters.
EPOCHS = 200
BATCH_SIZE = 128
VERBOSE = 1
NB_CLASSES = 10 # number of outputs = number of digits
N_HIDDEN = 128
VALIDATION_SPLIT = 0.2 # how much TRAIN is reserved for VALIDATION
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