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- # Import MINST data
- import input_data
- mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
- import tensorflow as tf
- # Parameters
- learning_rate = 0.01
- training_epochs = 25
- batch_size = 100
- display_step = 1
- # Create model
- x = tf.placeholder("float", [None, 784])
- y = tf.placeholder("float", [None,10])
- W = tf.Variable(tf.zeros([784,10]))
- b = tf.Variable(tf.zeros([10]))
- activation = tf.nn.softmax(tf.matmul(x,W) + b) #softmax
- cost = -tf.reduce_sum(y*tf.log(activation)) #cross entropy
- optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
- # Train
- init = tf.initialize_all_variables()
- with tf.Session() as sess:
- sess.run(init)
- for epoch in range(training_epochs):
- avg_cost = 0.
- total_batch = int(mnist.train.num_examples/batch_size)
- for i in range(total_batch):
- batch_xs, batch_ys = mnist.train.next_batch(batch_size)
- sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
- avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch
- if epoch % display_step == 0:
- print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
- print "Optimization Finished!"
- # Test trained model
- correct_prediction = tf.equal(tf.argmax(activation,1), tf.argmax(y,1))
- accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
- print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})
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