Why should we normalize data for deep learning in Keras?

Question

I was testing some network architectures in Keras for classifying the MNIST dataset. I have implemented one that is similar to the LeNet.

I have seen that in the examples that I have found on the internet, there is a step of data normalization. For example:

X_train /= 255

I have performed a test without this normalization and I have seen that the performance (accuracy) of the network has decreased (keeping the same number of epochs). Why has this happened?

If I increase the number of epochs, the accuracy can reach the same level reached by the model trained with normalization?

So, the normalization affects the accuracy, or only the training speed?

The complete source code of my training script is below:

from keras.models import Sequential
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dense
from keras.datasets import mnist
from keras.utils import np_utils
from keras.optimizers import SGD, RMSprop, Adam
import numpy as np
import matplotlib.pyplot as plt
from keras import backend as k


def build(input_shape, classes):
    model = Sequential()

    model.add(Conv2D(20, kernel_size=5, padding="same",activation='relu',input_shape=input_shape))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

    model.add(Conv2D(50, kernel_size=5, padding="same", activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))

    model.add(Flatten())
    model.add(Dense(500))
    model.add(Activation("relu"))

    model.add(Dense(classes))
    model.add(Activation("softmax"))

    return model


NB_EPOCH = 4 # number of epochs
BATCH_SIZE = 128 # size of the batch
VERBOSE = 1 # set the training phase as verbose
OPTIMIZER = Adam() # optimizer
VALIDATION_SPLIT=0.2 # percentage of the training data used for 
evaluating the loss function
IMG_ROWS, IMG_COLS = 28, 28 # input image dimensions
NB_CLASSES = 10 # number of outputs = number of digits
INPUT_SHAPE = (1, IMG_ROWS, IMG_COLS) # shape of the input

(X_train, y_train), (X_test, y_test) = mnist.load_data()

k.set_image_dim_ordering("th")

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255

X_train = X_train[:, np.newaxis, :, :]
X_test = X_test[:, np.newaxis, :, :]
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

y_train = np_utils.to_categorical(y_train, NB_CLASSES)
y_test = np_utils.to_categorical(y_test, NB_CLASSES)

model = build(input_shape=INPUT_SHAPE, classes=NB_CLASSES)
model.compile(loss="categorical_crossentropy", 
optimizer=OPTIMIZER,metrics=["accuracy"])

history = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=NB_EPOCH, verbose=VERBOSE, validation_split=VALIDATION_SPLIT)

model.save("model2")

score = model.evaluate(X_test, y_test, verbose=VERBOSE)
print('Test accuracy:', score[1])

Solution 1

Normalization is a generic concept not limited only to deep learning or to Keras.

Why to normalize?

Let me take a simple logistic regression example which will be easy to understand and to explain normalization. Assume we are trying to predict if a customer should be given loan or not. Among many available independent variables lets just consider Age and Income. Let the equation be of the form:

Y = weight_1 * (Age) + weight_2 * (Income) + some_constant

Just for sake of explanation let Age be usually in range of [0,120] and let us assume Income in range of [10000, 100000]. The scale of Age and Income are very different. If you consider them as is then weights weight_1 and weight_2 may be assigned biased weights. weight_2 might bring more importance to Income as a feature than to what weight_1 brings importance to Age. To scale them to a common level, we can normalize them. For example, we can bring all the ages in range of [0,1] and all incomes in range of [0,1]. Now we can say that Age and Income are given equal importance as a feature.

Does Normalization always increase the accuracy?

Apparently, No. It is not necessary that normalization always increases accuracy. It may or might not, you never really know until you implement. Again it depends on at which stage in you training you apply normalization, on whether you apply normalization after every activation, etc.

As the range of the values of the features gets narrowed down to a particular range because of normalization, its easy to perform computations over a smaller range of values. So, usually the model gets trained a bit faster.

Regarding the number of epochs, accuracy usually increases with number of epochs provided that your model doesn't start over-fitting.

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A very good explanation for Normalization/Standardization and related terms is here.

Solution 2

In a nutshell, normalization reduces the complexity of the problem your network is trying to solve. This can potentially increase the accuracy of your model and speed up the training. You bring the data on the same scale and reduce variance. None of the weights in the network are wasted on doing a normalization for you, meaning that they can be used more efficiently to solve the actual task at hand.

Comments

[Yirui Jiang]

Normalization accelerates your training speed

[Shridhar R Kulkarni]

What do you mean by performance here? Is it training speed or is it accuracy?

[Zaratruta]

I mean accuracy.

[Teepeemm]

This feels almost like a link only answer. I can't get anything from your post without actually following the link. Could you explain a bit what happens in the notebook?