In [1]:

    

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


    

In [2]:

    

DATA_DIR = '/tmp/data'
NUM_STEPS = 1000
MINIBATCH_SIZE = 100


    

In [3]:

    

data = input_data.read_data_sets(DATA_DIR, one_hot = True)


    

    




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 [4]:

    

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))

y_true = tf.placeholder(tf.float32, [None, 10])
y_pred = tf.matmul(x, W)


    

In [5]:

    

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = y_pred, labels = y_true))
gd_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)


    

In [6]:

    

correct_mask = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1))
accuracy = tf.reduce_mean(tf.cast(correct_mask, tf.float32))


    

In [7]:

    

with tf.Session() as sess:
    
    # train
    sess.run(tf.global_variables_initializer())
    
    for _ in range(NUM_STEPS):
        batch_xs, batch_ys = data.train.next_batch(MINIBATCH_SIZE)
        sess.run(gd_step, feed_dict = {x: batch_xs, y_true: batch_ys})
        
    # test
    ans = sess.run(accuracy, feed_dict = {x: data.test.images, y_true: data.test.labels})
    
print("accuracy : {:.4}%".format(ans*100))


    

    




accuracy : 91.56%

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