框架tensorflow3

时间：2022-02-07 15:55:19

tensorflow3

tensorflow 可视化好帮手；

tf.train.SummaryWriter报错，改为tf.summary.FileWriter

软件包安装yum install sqlite-devel
[root@shenzhen tensorflow]# python3 tensor6.py

2018-08-24 21:14:52.513641: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

[root@shenzhen tensorflow]# ls

events.out.tfevents.1535116493.shenzhen.com  tensor2.py  tensor4.py  tensor6.py

tensor1.py                                   tensor3.py  tensor5.py

[root@shenzhen tensorflow]# cat tensor6.py

#!/usr/local/bin/python3

#coding:utf-8

import tensorflow as tf

def add_layer(inputs,in_size, out_size, activation_function=None):

    #add one more layer and return the output of this layer

    with tf.name_scope('layer'):

        with tf.name_scope('weights'):

            Weights = tf.Variable(tf.random_normal([in_size, out_size]),\

                    name='W')

        with tf.name_scope('biases'):

            biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name='b')

        with tf.name_scope('Wx_plus_b'):

            Wx_plus_b = tf.add(tf.matmul(inputs, Weights),biases)

        if activation_function is None:

            outputs = Wx_plus_b

        else:

            outputs = activation_function(Wx_plus_b,)

        return outputs

#define placeholder for inputs to network

with tf.name_scope('inputs'):

    xs = tf.placeholder(tf.float32,[None,1],name='x_input')

    ys = tf.placeholder(tf.float32,[None,1],name='y_input')

#add hidden layer

l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)

#add output layer

prediction = add_layer(l1,10,1,activation_function=None)

#the error between prediction and real data

with tf.name_scope('loss'):

    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),\

                       reduction_indices=[1]))

with tf.name_scope('train'):

    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.global_variables_initializer()

sess = tf.Session()

writer = tf.summary.FileWriter('.',sess.graph)

#important step

sess.run(init)

#tensorboard    --logdir='/logs/'

访问浏览器：、、、、

1报错位置：.tf.scalar_summary('batch_loss', loss)AttributeError: 'module' object has no attribute 'scalar_summary'修改为：tf.summary.scalar('batch_loss', loss)原因：新版本做了调整

2.AttributeError: 'module' object has no attribute 'histogram_summary'修改为：tf.summary.histogram

3.tf.merge_all_summaries()改为：summary_op = tf.summaries.merge_all()

4.AttributeError: 'module' object has no attribute 'SummaryWriter':tf.train.SummaryWriter改为tf.summary.FileWriter

报错：

tf.scalar_summary(l.op.name + ' (raw)', l)

AttributeError: 'module' object has no attribute 'scalar_summary'

解决：

tf.scalar_summary('images', images)改为：tf.summary.scalar('images', images)

tf.image_summary('images', images)改为：tf.summary.image('images', images)

还有：

tf.train.SummaryWriter改为：tf.summary.FileWriter

tf.merge_all_summaries()改为：summary_op = tf.summary.merge_all

tf.histogram_summary(var.op.name, var)改为：  tf.summary.histogram

concated = tf.concat(1, [indices, sparse_labels])改为：concated = tf.concat([indices, sparse_labels], 1)

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