Tensorflow 实现 Logistics Regressi

今天学完了吴恩达 深度学习课程logistic回归，利用tensorflow 参照网上知识实现

from  tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

learning_rate = 0.001
training_epoch = 25
batch_size = 100
display_step = 1

x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])

W = tf.placeholder(tf.zeros([784, 10]))
b = tf.placeholder(tf.zeros([10]))

pred = tf.nn.softmax(tf.matmul(x, W) + b)

cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred)), reduction_indices=1)

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

init =tf.initialize_all_variables()

with tf.Session() as sess:
  sess.run(init)
  for epoch in training_epoch:
      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)
          _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, y: batch_ys})
          avg_cost += c / total_batch
      if (epoch + 1) % display_step == 0:
          print "Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost)
  print "Optimization Finished!"


correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print "Accuracy:", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]})