In [1]:

    

import numpy as np
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)


    

    




Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz

In [2]:

    

x = tf.placeholder(tf.float32)

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])

cross_entropy = tf.reduce_mean(
    - tf.reduce_sum(
        y_ * tf.log(y),
        reduction_indices = [1]
    )
)

train_step = tf.train\
    .GradientDescentOptimizer(0.5)\
    .minimize(cross_entropy)
    
init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})


    

In [12]:

    

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))


    

In [13]:

    

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))


    

In [14]:

    

print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))


    

    




0.9139

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