In [8]:

    

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

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder(tf.float32, [None, 784])

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

train = tf.train.GradientDescentOptimizer(0.5).minimize(crossEntropy)

sess = tf.InteractiveSession()
tf.global_variables_initializer().run()

for _ in range(1000):
    batchXs, batchYs = mnist.train.next_batch(100)
    sess.run(train, feed_dict={x: batchXs, y_: batchYs})
    
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
                                      y_: mnist.test.labels}))


    

    




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
0.9177

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