（四）加入模型保存和加载_tf estimator 加载部分模型参数-优快云博客

本文详细介绍了如何使用TensorFlow进行深度学习模型的训练过程，包括定义损失函数、优化器和准确率指标，以及如何利用Saver类保存模型参数到本地文件。同时，也展示了如何重新加载模型进行后续的预测或继续训练。

代码变动部分：

logits, train_op, loss, maintain_averages_op, accuracy = simplenet(x,y,class_num)

saver = tf.train.Saver()    
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    steps = epochs * len(img_data) // batchsize
    for step in range(steps):
        batch_inputs = inputs[step*batchsize:(step+1)*batchsize]
        batch_labels = true_labels[step*batchsize:(step+1)*batchsize]
        ls, acc, _ = sess.run([loss,accuracy,maintain_averages_op],feed_dict={x:batch_inputs,y:batch_labels})
        if step%100 == 0:
            saver.save(sess,model_dir,global_step=step)
            print(' step: ', step, ' loss: ', ls, ' accuracy: ', acc)

模型加载：

tf.train.import_meta_graph('./models/model-0.meta')
for variable_name in tf.global_variables():
    print(variable_name)
for tensor_name in tf.contrib.graph_editor.get_tensors(tf.get_default_graph()):
    print(tensor_name)
# with tf.Session() as sess:
#     for node in sess.graph_def.node:
#         print(node)
saver = tf.train.Saver()
with tf.Session() as sess:
    saver.restore(sess,'./models/model-100')
    #print(sess.run(tf.get_default_graph().get_tensor_by_name('Variable_5:0')))