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- import tensorflow as tf
- import import_data
- mnist = import_data.read_data_sets("./", one_hot=True)
- # Define variables
- x = tf.placeholder("float", [None, 784])
- W = tf.Variable(tf.zeros([784, 10]))
- b = tf.Variable(tf.zeros([10]))
- # Softmax(Wx+b) neural network
- y = tf.nn.softmax(tf.matmul(x,W) + b)
- # Cross-entropy function for training evaluation
- y_ = tf.placeholder("float", [None, 10])
- cross_entropy = -tf.reduce_sum(y_*tf.log(y))
- train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
- init = tf.initialize_all_variables()
- session = tf.Session()
- session.run(init)
- # Train by stochastic gradient descent
- for i in xrange(1000):
- batch_xs, batch_ys = mnist.train.next_batch(100)
- session.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
- # Evaluate our training
- correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
- accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
- print session.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
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