I am a beginner to Keras and I have started with the MNIST example to understand how the library actually works. The code snippet of the MNIST problem in the Keras example folder is given as :

import numpy as np
np.random.seed(1337)  # for reproducibility

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten  
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils

batch_size = 128
nb_classes = 10
nb_epoch = 12

# input image dimensions
img_rows, img_cols = 28, 28
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
nb_pool = 2
# convolution kernel size
nb_conv = 3

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')

I am unable to understand the reshape function here. What is it doing and why we have applied it?


2 Answers 2


mnist.load_data() supplies the MNIST digits with structure (nb_samples, 28, 28) i.e. with 2 dimensions per example representing a greyscale image 28x28.

The Convolution2D layers in Keras however, are designed to work with 3 dimensions per example. They have 4-dimensional inputs and outputs. This covers colour images (nb_samples, nb_channels, width, height), but more importantly, it covers deeper layers of the network, where each example has become a set of feature maps i.e. (nb_samples, nb_features, width, height).

The greyscale image for MNIST digits input would either need a different CNN layer design (or a param to the layer constructor to accept a different shape), or the design could simply use a standard CNN and you must explicitly express the examples as 1-channel images. The Keras team chose the latter approach, which needs the re-shape.

  • $\begingroup$ Can you please explain the logic behind the "np.random.seed(1337)" used in the code? Why 1337? $\endgroup$
    – enterML
    May 13, 2016 at 13:01
  • 2
    $\begingroup$ Nothing special about 1337 for the purposes of the script except repeatability. It is good practice to seed your RNG so that you you can repeat your successful work exactly on another occasion. The number is a bit of an inside joke for hackers - urbandictionary.com/define.php?term=1337 $\endgroup$ May 13, 2016 at 13:04

Just a small correction from the accepted answer , the input shape indices are named as follows: (n_images, x_shape, y_shape, channels)


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