1.1 Spliting data into train and test sets

In [2]:
(X_train, y_train), (X_test, y_test) = mnist.load_data()

1.2 Checking the shape of the input data

In [3]:
print('x_train:\t{}' .format(X_train.shape))
print('y_train:\t{}' .format(y_train.shape))
print('x_test:\t\t{}'.format(X_test.shape))
print('y_test:\t\t{}'.format(y_test.shape))

x_train:	(60000, 28, 28)
y_train:	(60000,)
x_test:		(10000, 28, 28)
y_test:		(10000,)

1.3 Plot first 64 digits

In [4]:
# set up the figure
fig = plt.figure(figsize=(6, 6))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)

# plot the digits: each image is 28x28 pixels
for i in range(64):
    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
    ax.imshow(X_train[i], cmap=plt.cm.binary, interpolation='nearest')
    
    # label the image with the target value
    ax.text(0, 7, str(y_train[i]))