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- #!/usr/bin/env python
- # coding=utf-8
-
- import tensorflow as tf
- import input_mnist
-
- mnist=input_mnist.read_data_sets("mnist-data/",one_hot=True)
-
- print mnist.train.images.shape
- print mnist.train.labels.shape
- print mnist.test.images.shape
- print mnist.test.labels.shape
-
- #Create the model
- W=tf.Variable(tf.zeros([784,10]))
- b=tf.Variable(tf.zeros([10]))
- x=tf.placeholder("float",[None,784])
- y=tf.nn.softmax(tf.matmul(x,W)+b)
- y_=tf.placeholder("float",[None,10])
-
- #Define loss and optimizer
- 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)
- #Train
- for i in xrange(10000):
- batch_xs,batch_ys=mnist.train.next_batch(100)
- sess.run(train_step, feed_dict={x:batch_xs, y_:batch_ys})
-
- #Test trained model
- correct_prediction=tf.equal(tf.arg_max(y,1),tf.arg_max(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}))
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