MNIST

In [1]:

    

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data

%matplotlib inline
print ("Packages are loaded!!!")


    

    




/home/parksurk/.virtualenvs/cv/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
/home/parksurk/.virtualenvs/cv/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')






    




Packages are loaded!!!

In [2]:

    

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


    

    




Download and Extract MNIST dataset
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data/t10k-labels-idx1-ubyte.gz
type of 'mnist' is <class 'tensorflow.contrib.learn.python.learn.datasets.base.Datasets'>
number of train data is 55000
number of test data is 10000

In [3]:

    

# 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,))


    

    




What does the data of MNIST look like?
 type of 'trainimg' is <class 'numpy.ndarray'>
 type of 'trainlabel' is <class 'numpy.ndarray'>
 type of 'testimg' is <class 'numpy.ndarray'>
 type of 'testlabel' is <class 'numpy.ndarray'>
 shape of 'trainimg' is (55000, 784)
 shape of 'trainlabel' is (55000, 10)
 shape of 'testimg' is (10000, 784)
 shape of 'testlabel' is (10000, 10)

In [4]:

    

# 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))


    

    




How does the training data look like?
40849th Training Data Label is 8
24739th Training Data Label is 4
26520th Training Data Label is 5
23951th Training Data Label is 5
30072th Training Data Label is 8






    













    













    













    













    

In [5]:

    

# Batch Learning? 
print ("Batch Learning? ")
batch_size = 100
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
print ("type of 'batch_xs' is %s" % (type(batch_xs)))
print ("type of 'batch_ys' is %s" % (type(batch_ys)))
print ("shape of 'batch_xs' is %s" % (batch_xs.shape,))
print ("shape of 'batch_ys' is %s" % (batch_ys.shape,))


    

    




Batch Learning? 
type of 'batch_xs' is <class 'numpy.ndarray'>
type of 'batch_ys' is <class 'numpy.ndarray'>
shape of 'batch_xs' is (100, 784)
shape of 'batch_ys' is (100, 10)

In [6]:

    

# Get Random Batch with 'np.random.randint'
print ("5. Get Random Batch with 'np.random.randint'")
randidx   = np.random.randint(trainimg.shape[0], size=batch_size)
batch_xs2 = trainimg[randidx, :]
batch_ys2 = trainlabel[randidx, :]
print ("type of 'batch_xs2' is %s" % (type(batch_xs2)))
print ("type of 'batch_ys2' is %s" % (type(batch_ys2)))
print ("shape of 'batch_xs2' is %s" % (batch_xs2.shape,))
print ("shape of 'batch_ys2' is %s" % (batch_ys2.shape,))


    

    




5. Get Random Batch with 'np.random.randint'
type of 'batch_xs2' is <class 'numpy.ndarray'>
type of 'batch_ys2' is <class 'numpy.ndarray'>
shape of 'batch_xs2' is (100, 784)
shape of 'batch_ys2' is (100, 10)

In [7]:

    

randidx


    

    
Out[7]:





array([14424, 18266, 51424, 45126, 41166, 15003, 54676, 30006, 45970,
       52095,  3522, 44936, 21949, 44842, 41837, 40668,  9667, 41646,
        3600, 15251, 26337, 27460, 52678,  6449, 48053, 20193, 40260,
       29480, 20567, 45904, 30837, 45905, 45180,  7602,  2576, 23432,
       44468, 37605, 25818, 37354,  7794, 13354, 10975, 50969, 16164,
       16209, 47054, 53515, 52012, 28380,  4587, 14285,  6080, 50722,
       36188, 17257,  1648, 27785,  4299, 36106, 54172,  4019, 49182,
       51365, 48105,  5694, 18919,  4503,  7214, 43934, 41313, 24234,
       35871,  5711, 38938, 24403, 20709, 49222,  4379, 48905, 33369,
       27893, 11668, 14801,  7086, 42162, 12329, 22984, 42546, 37712,
       54478, 49055, 10352, 26655, 49978, 53378, 53715, 43047, 20590,  8454])

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