小编Shi*_*chi的帖子

$ bundle install

Errno::ENOENT: No such file or directory @ rb_sysopen - /Users/Sean/.rbenv/versions/2.1.0/lib/ruby/gems/2.1.0/gems/eventmachine-1.0.3/ext/gem_make.out

An error occurred while installing eventmachine (1.0.3), and Bundler cannot
continue.
Make sure that `gem install eventmachine -v '1.0.3'` succeeds before bundling.

$ gem list

eventmachine (1.0.3 x86-mingw32)

$ gem build eventmachine -v 1.0.3

ERROR:  While executing gem ... (Gem::CommandLineError)
Too many gem names (eventmachine, 1.0.3); please specify only one

$ bundle exec cap production deploy

(Backtrace仅限于导入任务)上限中止!
SSHKit :: Runner :: ExecuteError:
执行Psara @ sakura时出现异常:git退出状态:128 git stdout:没有写入git stderr:Permission denied(publickey).
致命:远程端意外挂断

SSHKit :: Command :: Failed:
git退出状态:128
git stdout:没有写任何
git stderr:Permission denied(publickey).
致命:远程端意外挂断

任务:TOP => git:check(通过--trace运行任务查看完整跟踪)部署失败并显示错误:
执行Psara @ sakura时出现 异常:
git退出状态:128 git stdout:没有写
git stderr:Permission denied(publickey).

致命:远程端意外挂断

*

deploy.rb

set:application,'
Psara'set:repo_url,'git @ bitbucket.org:CBLaughter/psara.git'set
:deploy_to,'/ home/Psara/Psara'set
:default_run_options,:pty => true

namespace:deploy do

after:restart,:clear_cache do on roles(:web),in :: groups,limit:3,wait:10 do#这里我们可以做任何事情,例如:#inplace_path do #execute:rake,'cache:clear' #end end end

结束

set:ssh_options,{forward_agent:true,paranoid:true,keys:"〜/ .ssh/id_rsa"}

*

production.rb

set:stage,:staging
set:rails_env,:production

角色:app,%w {sakura} …

我正在尝试使用TensorFlow生成摘要并使用TensorBoard可视化它们.但是,我收到一个InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_1' with dtype float我不明白的错误().

这是我的计划的完整来源:

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow as tf

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)

_ = tf.histogram_summary("weights", W)
_ = tf.histogram_summary("biases", b)
_ = tf.histogram_summary("y", y)


y_ = tf.placeholder(tf.float32, [None, 10])

with tf.name_scope("xent") as scope:
    cross_entropy = -tf.reduce_sum(y_*tf.log(y))
    _ = tf.scalar_summary("cross entropy", cross_entropy)

with tf.name_scope("train") as scope:
    train_step …