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26 lines
932 B
26 lines
932 B
6 years ago
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import tensorflow as tf
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from tensorflow.examples.tutorials.mnist import input_data
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mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
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xs = tf.placeholder(tf.float32,[None,784])
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ys = tf.placeholder(tf.float32,[None,10])
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Weight = tf.Variable(tf.zeros([784,10]))
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biases = tf.Variable(tf.zeros([10]))
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y = tf.nn.softmax(tf.matmul(xs,Weight)+biases)
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loss = -tf.reduce_sum(ys*tf.log(y))
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train = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
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init = tf.initialize_all_variables()
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sess = tf.Session()
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sess.run(init)
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for step in range(10000):
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batch = mnist.train.next_batch(100)
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sess.run(train,feed_dict={xs:batch[0],ys:batch[1]})
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if step%50==0:
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correct_prediction = tf.equal(tf.arg_max(ys,1),tf.arg_max(y,1))
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accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
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print(sess.run(accuracy,feed_dict={xs:mnist.test.images,ys:mnist.test.labels}))
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