#!/bin/bash

export LD_LIBRARY_PATH=/usr/local/lib/:/usr/local/HiAI/runtime/lib64

export PATH=/usr/local/HiAI/runtime/ccec_compiler/bin:$PATH

export CUSTOM_OP_LIB_PATH=/usr/local/HiAI/runtime/ops/framework/built-in/tensorflow

export DDK_VERSION_PATH=/usr/local/HiAI/runtime/ddk_info

export WHICH_OP=GEOP

export NEW_GE_FE_ID=

export GE_AICPU_FLAG=

export OPTION_EXEC_EXTERN_PLUGIN_PATH=/usr/local/HiAI/runtime/lib64/plugin/opskernel/libfe.so:/usr/local/HiAI/runtime/lib64/plugin/opskernel/libaicpu_plugin.so:/usr/local/HiAI/runtime/lib64/plugin/opskernel/libge_local_engine.so:/usr/local/H

iAI/runtime/lib64/plugin/opskernel/librts_engine.so:/usr/local/HiAI/runtime/lib64/libhccl.so

export OP_PROTOLIB_PATH=/usr/local/HiAI/runtime/ops/built-in/

export DEVICE_ID=

export PRINT_MODEL=

#export DUMP_GE_GRAPH=

#export DISABLE_REUSE_MEMORY=

#export DUMP_OP=

#export SLOG_PRINT_TO_STDOUT=

export RANK_ID=

export RANK_SIZE=

export JOB_ID=

export OPTION_PROTO_LIB_PATH=/usr/local/HiAI/runtime/ops/op_proto/built-in/

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/fwkacllib/lib64/:/usr/local/Ascend/driver/lib64/common/:/usr/local/Ascend/driver/lib64/driver/:/usr/local/Ascend/add-ons/

export PYTHONPATH=$PYTHONPATH:/usr/local/Ascend/opp/op_impl/built-in/ai_core/tbe

export PATH=$PATH:/usr/local/Ascend/fwkacllib/ccec_compiler/bin

export ASCEND_HOME=/usr/local/Ascend

export ASCEND_OPP_PATH=/usr/local/Ascend/opp

export SOC_VERSION=Ascend910

rm -f *.pbtxt

rm -f *.txt

rm -r /var/log/npu/slog/*.log

rm -rf train_url/*

python3 mnist_train.py

import os

import numpy as np

import tensorflow as tf

import time

from tensorflow.examples.tutorials.mnist import input_data

import mnist_inference

from npu_bridge.estimator import npu_ops #导入NPU算子库

from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig #重写tensorFlow里的配置，针对NPU的配置

batch_size =

learning_rate = 0.1

training_step = 

model_save_path = "./model/"

model_name = "model.ckpt"

def train(mnist):

    x = tf.placeholder(tf.float32, [batch_size, mnist_inference.image_size, mnist_inference.image_size, mnist_inference.num_channels], name = 'x-input')

    y_ = tf.placeholder(tf.float32, [batch_size, mnist_inference.num_labels], name = "y-input")

    regularizer = tf.contrib.layers.l2_regularizer(0.001)

    y = mnist_inference.inference(x, train = True, regularizer = regularizer) #推理过程

    global_step = tf.Variable(, trainable=False)

    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits = y, labels = tf.argmax(y_, )) #损失函数

    cross_entropy_mean = tf.reduce_mean(cross_entropy)

    loss = cross_entropy_mean + tf.add_n(tf.get_collection("loss"))

    train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step = global_step) #优化器调用

    saver = tf.train.Saver() #启动训练

#以下代码是NPU所必须的代码，开始配置参数

    config = tf.ConfigProto(

        allow_soft_placement = True,

        log_device_placement = False)

    custom_op =  config.graph_options.rewrite_options.custom_optimizers.add()

    custom_op.name =  "NpuOptimizer"

    custom_op.parameter_map["use_off_line"].b = True

    #custom_op.parameter_map["profiling_mode"].b = True

    #custom_op.parameter_map["profiling_options"].s = tf.compat.as_bytes("task_trace:training_trace")

config.graph_options.rewrite_options.remapping = RewriterConfig.OFF

#配置参数结束

    writer = tf.summary.FileWriter("./log_dir", tf.get_default_graph())

    writer.close()

#参数初始化

    with tf.Session(config = config) as sess:

        tf.global_variables_initializer().run()

        start_time = time.time()

        for i in range(training_step):

xs, ys = mnist.train.next_batch(batch_size)

            reshaped_xs = np.reshape(xs, (batch_size, mnist_inference.image_size, mnist_inference.image_size, mnist_inference.num_channels))

            _, loss_value, step = sess.run([train_step, loss, global_step], feed_dict={x:reshaped_xs, y_:ys})

            #每训练10个epoch打印损失函数输出日志

            if i %  == :

                print("****************************++++++++++++++++++++++++++++++++*************************************\n" * )

                print("After %d training steps, loss on training batch is %g, total time in this 1000 steps is %s." % (step, loss_value, time.time() - start_time))

                #saver.save(sess, os.path.join(model_save_path, model_name), global_step = global_step)

                print("****************************++++++++++++++++++++++++++++++++*************************************\n" * )

                start_time = time.time()

def main():

    mnist = input_data.read_data_sets('MNIST_DATA/', one_hot= True)

    train(mnist)

if __name__ == "__main__":

    main()