logistic regression by tensorflow

learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

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

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

pred = tf.nn.sigmoid(tf.matmul(x, W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reducetion_indices=1))

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

with tf.Session() as sess:
	sess.run(tf.global_variables_initializer()

	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)
			_, 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 "cost=", "{:.9f}".format(avg_cost)

	print "optimization finished!"