小编jua*_*aza的帖子

我试图在xcode项目中安装可可豆荚但是在做的时候:

$ pod install

我收到以下错误:

Setting up CocoaPods master repo
[!] The `master` repo requires CocoaPods 0.32.1 - 
Update CocoaPods, or checkout the appropriate tag in the repo.
/Library/Ruby/Gems/2.0.0/gems/claide-0.5.0/lib/claide/command.rb:281:in `rescue in run': undefined method `verbose?' for nil:NilClass (NoMethodError)

所以我更新了cocoapods

$ sudo gem update cocoapods

但它告诉我没有更新:

Password:
Updating installed gems
Nothing to update

运行pod --version

但我得到:

 0.31.0

会是什么呢？为什么不更新到最新版本(.32.1)？

Xcode:5.1

ruby:2.0.0p353

OSX:10.9.2

执行时

format.json{render json: {}, status: :ok}

在Rails 4.0.4中,我收到以下错误:

ArgumentError (too few arguments):

虽然我有另一个程序(使用Rails 3.2.13),其中完全相同的行执行没有问题.我在这里错过了什么吗？

任何宝石？

或使用rails 4更改语法？

安装xcode 5并运行模拟器后,我收到以下错误:

'NSInvalidUnarchiveOperationException',原因:' * - [NSKeyedUnarchiver decodeBoolForKey:]:键的值(UIHighlighted)不是布尔值'

应用程序在打开之前关闭.它在xcode 4.6上运行良好.会是什么呢？

它似乎与UIImageView有关,是这样吗？

仅针对iPad报告了问题.堆栈跟踪显示:

Hardware Model:      iPad4,1
Code Type:           ARM-64 (Native)
Parent Process:      launchd [1]

OS Version:          iOS 9.0 (13A344)
Report Version:      105

Exception Type:  00000020
Exception Codes: 0x000000008badf00d
Exception Note:  SIMULATED (this is NOT a crash)
Highlighted by Thread:  3

Application Specific Information:
com.Tappsi.tappsiuser failed to scene-create after 19.55s (launch took 0.45s of total time limit 20.00s)

Elapsed total CPU time (seconds): 22.530 (user 22.530, system 0.000), 56% CPU 
Elapsed application CPU time (seconds): 18.920, 47% CPU

Filtered syslog:
None found


Thread …

我有一个使用 TensorFlow 1 运行 Keras 的代码。该代码修改了损失函数以进行深度强化学习：

import os
import gym
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
env = gym.make("CartPole-v0").env
env.reset()
n_actions = env.action_space.n
state_dim = env.observation_space.shape


from tensorflow import keras
import random
from tensorflow.keras import layers as L
import tensorflow as tf
from tensorflow.python.keras.backend import set_session

sess = tf.compat.v1.Session()
graph = tf.compat.v1.get_default_graph()
init = tf.global_variables_initializer()
sess.run(init)

network = keras.models.Sequential()
network.add(L.InputLayer(state_dim))

# let's create a network for approximate q-learning following guidelines above
network.add(L.Dense(5, activation='elu'))
network.add(L.Dense(5, …