Notebook to figure out how to separate MNIST examples of digits 0 - 4.



In [1]:

    
import tensorflow as tf
from tensorflow.contrib.layers import fully_connected
from tensorflow.examples.tutorials.mnist import input_data



In [2]:

    
mnist = input_data.read_data_sets("/tmp/data/")









    



Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting /tmp/data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz



In [3]:

    
X_train, y_train = mnist.train.images, mnist.train.labels



In [4]:

    
X_train.shape









    Out[4]:





(55000, 784)



In [5]:

    
y_train.shape









    Out[5]:





(55000,)



In [26]:

    
pull = [idx for idx in range(len(y_train)) if y_train[idx] < 5]



In [27]:

    
pull



In [6]:

    
enumerated_y = list(enumerate(y_train))



In [7]:

    
indices = [index for index, label in enumerated_y if label < 5]



In [8]:

    
indices



In [9]:

    
enumerated_y









    Out[9]:





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 ...]



In [10]:

    
X_masked_train = X_train[indices]



In [11]:

    
y_masked_train = y_train[indices]



In [12]:

    
y_masked_train.shape









    Out[12]:





(28038,)



In [13]:

    
X_masked_train.shape









    Out[13]:





(28038, 784)