session里执行graph里定义的运算

#迭代总轮次
n_epochs = 30

with tf.Session() as sess:
	# 在Tensorboard里可以看到图的结构
	writer = tf.summary.FileWriter('./graphs/logistic_reg', sess.graph)

	start_time = time.time()
	sess.run(tf.global_variables_initializer())	
	n_batches = int(mnist.train.num_examples/batch_size)
	for i in range(n_epochs): # 迭代这么多轮
		total_loss = 0

		for _ in range(n_batches):
			X_batch, Y_batch = mnist.train.next_batch(batch_size)
			_, loss_batch = sess.run([optimizer, loss], feed_dict={X: X_batch, Y:Y_batch}) 
			total_loss += loss_batch
		print('Average loss epoch {0}: {1}'.format(i, total_loss/n_batches))

	print('Total time: {0} seconds'.format(time.time() - start_time))

	print('Optimization Finished!')

	# 测试模型
	
	preds = tf.nn.softmax(logits)
	correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(Y, 1))
	accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32))
	
	n_batches = int(mnist.test.num_examples/batch_size)
	total_correct_preds = 0
	
	for i in range(n_batches):
		X_batch, Y_batch = mnist.test.next_batch(batch_size)
		accuracy_batch = sess.run([accuracy], feed_dict={X: X_batch, Y:Y_batch}) 
		total_correct_preds += accuracy_batch[0]
	
	print('Accuracy {0}'.format(total_correct_preds/mnist.test.num_examples))

	writer.close()