In [7]:

    

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


    

In [8]:

    

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 [9]:

    

sess = tf.InteractiveSession()


    

In [10]:

    

in_units =784
h1_units =300
W1= tf.Variable(tf.truncated_normal([in_units,h1_units],stddev=0.1))
b1= tf.Variable(tf.zeros([h1_units]))
W2= tf.Variable(tf.zeros([h1_units,10]))
b2= tf.Variable(tf.zeros([10]))


    

In [11]:

    

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


    

In [12]:

    

hidden1 = tf.nn.relu(tf.matmul(x,W1)+b1)
hidden1_drop = tf.nn.dropout(hidden1, keep_prob)
y= tf.nn.softmax(tf.matmul(hidden1_drop,W2)+b2)


    

In [13]:

    

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.AdagradOptimizer(0.3).minimize(cross_entropy)


    

In [15]:

    

tf.global_variables_initializer().run()
for i in range(3000):
    batch_xs,batch_ys = mnist.train.next_batch(100)
    train_step.run({x:batch_xs,y_:batch_ys,keep_prob:0.75})


    

In [17]:

    

correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))


    

    




0.9805

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