3 mnist

print ("Download and Extract MNIST dataset")
mnist = input_data.read_data_sets('data/', one_hot=True)
print
print (" tpye of 'mnist' is %s" % (type(mnist)))
print (" number of trian data is %d" % (mnist.train.num_examples))
print (" number of test data is %d" % (mnist.test.num_examples))





# What does the data of MNIST look like? 
print ("What does the data of MNIST look like?")
trainimg   = mnist.train.images
trainlabel = mnist.train.labels
testimg    = mnist.test.images
testlabel  = mnist.test.labels
print
print (" type of 'trainimg' is %s"    % (type(trainimg)))
print (" type of 'trainlabel' is %s"  % (type(trainlabel)))
print (" type of 'testimg' is %s"     % (type(testimg)))
print (" type of 'testlabel' is %s"   % (type(testlabel)))
print (" shape of 'trainimg' is %s"   % (trainimg.shape,))
print (" shape of 'trainlabel' is %s" % (trainlabel.shape,))
print (" shape of 'testimg' is %s"    % (testimg.shape,))
print (" shape of 'testlabel' is %s"  % (testlabel.shape,))






>>> np.random.randint(2, size=10)
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])

>>> np.random.randint(1, size=10)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

>>> np.random.randint(5, size=(2, 4))
array([[4, 0, 2, 1],
       [3, 2, 2, 0]])


# How does the training data look like?
print ("How does the training data look like?")
nsample = 5
randidx = np.random.randint(trainimg.shape[0], size=nsample)

for i in randidx:
    curr_img   = np.reshape(trainimg[i, :], (28, 28)) # 28 by 28 matrix 
    curr_label = np.argmax(trainlabel[i, :] ) # Label
    plt.matshow(curr_img, cmap=plt.get_cmap('gray'))
    plt.title("" + str(i) + "th Training Data " 
              + "Label is " + str(curr_label))
    print ("" + str(i) + "th Training Data " 
           + "Label is " + str(curr_label))
    plt.show()