What is going on everybody and welcome to a much-needed 每個人都在發生什麼，歡迎來到急需的地方
Update to the deep learning and Python with tensorflow as well as now chaos tutorial 使用tensorflow以及現在的混亂教程更新深度學習和Python
it's been a bit over two years since I did just a basic deep learning video in Python and 自從我用Python和Python做了一個基本的深度學習視頻已經有兩年多了
Since then a lot has changed. It's now much simpler to both like get into it 從那以後，很多都發生了變化。它現在變得更加簡單，兩者都喜歡進入它
But then also just to work with deep learning models 但隨後也只是與深度學習模型一起工作
So if you want to get into the more nitty gritty details in the lower-level 所以如果你想深入了解更低層次的細節
Tensorflow code you can still check out the older video Tensorflow代碼您仍然可以查看舊視頻
But if you're just trying to get started with deep learning that's not necessary anymore because we have these nice high-level 但是如果你只是想開始深度學習，那就不再需要了，因為我們有這些不錯的高級別
api's like chaos that sit on top of tensorflow and api就像坐在張量流上面的混亂
Make it super super simple. So anybody can follow along if you don't know anything about deep learning that's totally fine 讓它超級簡單。所以如果你對深度學習一無所知，那麼任何人都可以繼續學習
We're going to do a quick run-through of neural networks. Also, you're gonna want Python 我們將快速完成神經網絡的運行。此外，你會想要Python
3.6 at least as of the release of this video hopefully very very soon 3.6至少截至本視頻發布時，希望非常快
Tensorflow will be supported on three seven and later versions of Python just happens to be the case right now Tensorflow將支持三個七個版本和更高版本的Python恰好就是現在的情況
it isn't I think it's something to do with the 它不是我認為它與它有關
async 異步
Changes, I'm not really sure anyways 變化，反正我不太確定
Let's get into it starting with an overview of how neural networks just work 讓我們從概述神經網絡如何工作開始
Alright to begin 好了開始
we need to have some sort of balance between treating neural networks like a total black box that we just don't understand at all and 我們需要在處理神經網絡之間取得某種平衡，就像我們根本不理解的總黑盒子一樣
Understanding every single detail to them. So I'm gonna show you guys what I think is just the kind of bare essential to understanding 了解每一個細節。所以我要向你們展示我認為只是對理解非常重要的東西
What's going on? So a neural network is going to consist of the following things. Like what's the goal of any machine learning model? 這是怎麼回事？因此，神經網絡將包含以下內容。就像任何機器學習模型的目標一樣？
Well, you've got some input 好吧，你有一些意見
So let's say X 1 X 2 X 3 and you're just trying to map those 那麼讓我們說X 1 X 2 X 3，你只是試圖映射那些
inputs to some sort of output 輸入到某種輸出
Let's say that output is determining whether something is a dog or that something is a cat 讓我們說輸出決定了某些東西是狗還是某種東西是貓
So the output is going to be two neurons in this case. So it's just boom two neurons 因此在這種情況下輸出將是兩個神經元。所以它只是繁榮了兩個神經元
Now what we want to do is is figure out how are we going to map to those things? 現在我們要做的是弄清楚我們將如何映射到那些東西？
We could use a single hidden layer. Let's say we're going to do some neurons here and 我們可以使用一個隱藏層。假設我們要在這裡做一些神經元
That's our first 那是我們的第一次
Hidden lair now 隱藏的巢穴現在
What's gonna happen is each of these X 1 X 2 and X 3 these are gonna map to that hidden lair 將要發生的是這些X 1 X 2和X 3中的每一個都將映射到隱藏的巢穴
each of the 每一個
input data x' gets 輸入數據x'得到
Connected to each of the neurons in that first hidden layer. And each of those connections has its own 連接到第一個隱藏層中的每個神經元。每個連接都有自己的連接
Unique weight now from here that first hidden layer could then map and connect to that output layer 現在，從第一個隱藏層可以映射並連接到該輸出層的唯一權重
the problem is if you did this the relationship between x1 and dog or cat and 問題是如果你這樣做了x1和狗或貓之間的關係
All the other ones those relationships would only be linear relationships 所有其他的那些關係只是線性關係
so if we're looking to map and nonlinear relationships 所以，如果我們要尋找地圖和非線性關係
Which is probably going to be the case in a complex question. You need to have two or more one 在一個複雜的問題中可能會出現這種情況。你需要有兩個或更多
Hidden layer means you just have a neural network two or more hidden layers means you have a quote-unquote deep neural network 隱藏層意味著你只有一個神經網絡兩個或多個隱藏層意味著你有一個引用 - 不引用深度神經網絡
So we'll add one more layer and then we're gonna fully connect that one two 所以我們將再添加一層，然後我們將完全連接一兩層
And then once that's fully connected again all unique weights, each of those blue lines has a unique weight associated with it 然後，一旦完全連接所有獨特的重量，每條藍線都具有與之相關的獨特權重
and then that gets mapped to 然後將其映射到
The output and again each blue line has a unique weight associated with it 輸出和每條藍線再次具有與之相關的唯一權重
so now what we're gonna do is talk about what's happening on an 所以現在我們要做的就是談論發生了什麼
individual 個人
Neuron level. So again that neuron has certain inputs coming to it 神經元水平。所以神經元再次有了某些輸入
It might be the input layer X values or it could be inputs coming from the other neurons 它可能是輸入層X值，也可能是來自其他神經元的輸入
So we're gonna again we're gonna call the inputs x1 x2 and x3 所以我們將再次打電話給輸入x1 x2和x3
But just keep in mind this could actually not be it might not be your input data 但請記住，這實際上可能不是它可能不是您的輸入數據
It might be data coming from another neuron 它可能是來自另一個神經元的數據
But regardless that data's gonna come in and we're just gonna get the sum of that data 但無論數據是什麼，我們都會得到這些數據的總和
So it's gonna come in and be summed all together 所以它會進來並總結在一起
But remember we also have those weights each of the inputs has a unique weight that gets put you know multiplied 但請記住，我們也有這些權重，每個輸入都有一個獨特的權重，讓你知道成倍增加
Against the input data and then we sum it together finally and this is kind of where the artificial neural network comes into play 對輸入數據，然後我們最後將它們加在一起，這就是人工神經網絡發揮作用的地方
we have an activation function and this activation function is kind of meant to 我們有一個激活功能，這種激活功能是有意義的
Simulate a neuron actually firing or not 模擬實際射擊與否的神經元
So you can think of the activation function like on a graph, you know? 所以你可以想象一下圖表上的激活功能，你知道嗎？
You got your X and your Y and then a really basic activation function would be like a step or function 你有你的X和你的Y然後一個真正基本的激活功能就像一個步驟或功能
So if X is than a certain value boom we step up and we have a value. So let's say here 因此，如果X超過某個價值的繁榮，我們就會加強並且我們有一個價值。所以我們在這裡說
This is zero here. The value is one 這裡零。價值是一
So let's say this is our x-axis 1 2 3 所以我們假設這是我們的x軸1 2 3
so if X, you know after being all the inputs are multiplied by their weights sum together if that value is 因此，如果X，您知道在所有輸入之後，如果該值是，則將它們的權重加在一起
let's say 讓我們說吧
greater than 3 大於3
well, then this activation function returns a 1 but today we tend to use more of a 那麼，這個激活函數返回1，但今天我們傾向於使用更多的a
sigmoid activation function so it's not going to be a 0 or 1 it's going to be some sort of value between sigmoid激活函數，所以它不會是0或1，它將成為某種價值
0 and a 1 so instead we might actually return like a point seven nine or something like that 0和1所以相反，我們可能實際上返回像七點九或類似的東西
So coming back to this neural network here that we've been drawing 所以回到這個我們一直在畫的神經網絡
Let's say here on this final output layer. You've got dog and cat 讓我們在這個最終輸出層說。你有狗和貓
well, this output layer is almost certain to have just a sigmoid activation function and 好吧，這個輸出層幾乎肯定只有一個sigmoid激活功能
What's gonna say is maybe dog is a point seven nine and cat is a point two one 可能會說的是，狗可能是七點九，而貓是第二點
these two values are gonna add up to a perfect 1.0 but we're gonna go with whatever the 這兩個值將加起來一個完美的1.0，但我們將無論如何
Largest value is so in this case 在這種情況下，最大的值是如此
The neural network is you could say 79 percent confident that it's a dog 21 percent confidence a cat 你可以說神經網絡有79％的人確信它是一隻狗21％的信心
We're gonna say we're gonna take the Arg max basically and we're gonna say hmm. We think it's a dog 我們要說我們基本上會採取Arg max，我們會說嗯。我們認為這是一隻狗
All right. Now that we're all experts on the concepts of neural networks. Let's go ahead and build one. You're gonna need tensorflow 行。現在我們都是神經網絡概念的專家。讓我們繼續構建一個。你需要張量流
So do a pip install - - upgrade tensorflow you should be on tensorflow version 1.1 or greater. So 所以做一個pip安裝 - 升級tensorflow你應該是tensorflow 1.1或更高版本。所以
one thing you can do is import tensorflow and then 你可以做的一件事是導入tensorflow然後
Actually touch flow as TF and then TF dot version will give you your current version 實際上觸摸流量為TF然後TF點版本將為您提供當前版本
so mine is 我的是
1.10 1.10
Now let's go ahead and get started. So the first thing we're going to do is import a data set. We're going to use em 現在讓我們開始吧。所以我們要做的第一件事就是導入數據集。我們打算用em
nacelle 機艙
of 的
Data sets with machine learning. It is a dataset that consists of 28 by 28 sized images 數據集與機器學習。它是一個由28乘28個大小的圖像組成的數據集
So it's like the resolution 所以這就像決議一樣
images of handwritten 手寫的圖像
Digits 0 through 9. So it'll be like a 0 1 2 3 and so on and it's a handwritten kind of unique image 數字0到9.所以它就像一個0 1 2 3等等，它是一種手寫的獨特圖像
so it's actually a 所以它實際上是一個
Picture we can graph it 圖片我們可以繪製它
soon enough so you can see it's actually an image and the idea is to feed through the pixel values to the neural network and 很快你就可以看到它實際上是一個圖像，並且想法是將像素值輸入到神經網絡中
Then have the neural network output 然後有神經網絡輸出
Which number it actually thinks that image is 它實際上認為圖像是哪個數字
So that's our data set, and now what we want to do is 這就是我們的數據集，現在我們想做的是
Unpack that data set to training and testing variables 將該數據集解壓縮到訓練和測試變量
So this is a far more complex 所以這要複雜得多
Operation when it's actually a data set that you're kind of bringing in or that you built or whatever 當它實際上是您帶來的數據集或您構建的數據集或其他任何操作時的操作
For the sake of this tutorial we want to use something real basic like M inist 為了本教程的目的，我們想要使用像M inist這樣的基本內容
so we're gonna unpack it to X train Y train and 所以我們要打開X火車和Y火車
then we're going to do X test Y test and 然後我們要做X測試Y測試和
that's gonna equal m n-- 這將等於m n--
Astana score data, so that's gonna unpack it into there now 阿斯塔納得分數據，所以現在要打開它
Just to show you guys what this is 只是為了告訴你們這是什麼
We're gonna use Matt plot Lib you can pip install or just look at it with me, but we're gonna import matplotlib 我們將使用Matt plot Lib你可以點擊安裝或者只是看看它，但我們要導入matplotlib
Pipe lot as a PLT. And what we're gonna do is peel TM show and we're gonna do X 管道很多作為PLT。而我們要做的就是剝離TM秀，我們要做X.
train 培養
And we'll do the zero width 我們將做零寬度
So one thing we could do just just for the record 所以我們只能為記錄做一件事
Let me just print so we can you can see what we're talking about here. So this is just going to be an array 讓我打印一下，這樣我們就能看到我們在這裡談論的內容。所以這只是一個數組
It'll be a multi-dimensional array which is all a tensor is by the way 這將是一個多維數組，順便說一句，這是一個張量
So this is this is here's your tensor 所以這就是你的張量
right 對
Okay, so that's the the actual data that we're gonna attempt to pass through our neural network and just to show you if we were 好的，這就是我們要嘗試通過我們的神經網絡的實際數據，只是為了告訴你我們是不是
To actually graph it and then do a peel t touch show. It's gonna be the number and you can just excuse the color 要實際繪製它，然後做一個剝離觸摸顯示。這將是數字，你可以原諒顏色
It's definitely black and white. It's a single color. It's a binary 它絕對是黑色和白色。這是一種顏色。這是一個二進制文件
So one thing we could say is the color map is equal to P LTCM for color map 所以有一點我們可以說顏色圖等於P LTCM用於顏色圖
Binary Reap lot it and there you go. It's a it's not a color image 二進制收穫很多你去那裡。這不是彩色圖像
So anyways back to our actual code up here 所以無論如何回到我們的實際代碼
Once we have the data one thing we want to do is is normalize that data 一旦我們獲得數據，我們要做的一件事就是規範化該數據
so again, if I print it out, you can see it's data that seems to vary from 0 to 所以再次，如果我打印出來，你可以看到它的數據似乎從0到0不等
Looks like we have as high as 253. It's 0 to 255 4 pixel data 看起來我們有高達253.它是0到255 4像素數據
So what we want to do is scale this data or normalize it but really what we're doing in this normalization is scaling it 所以我們要做的是縮放這些數據或將其標準化，但我們在這種規範化中所做的實際上是縮放它
So we're going to just redefine X train and X testing but it's gonna be TF caras dot utils dot 因此，我們將重新定義X列車和X測試，但它將是TF caras dot utils dot
Normalize and we're gonna pass X 正常化，我們將通過X.
Train and it'll be on axis 1 and then we're gonna copy 訓練，它將在軸1上，然後我們將要複製
paste and we're gonna do the exact same thing for X test and 粘貼，我們將為X測試做同樣的事情
All this does let's just run that and then we'll run this again and you can see how the 5 has changed a little bit 所有這一切都讓我們只是運行它然後我們將再次運行它，你可以看到5如何改變了一點點
looks like I got a little lighter and 看起來我有點輕了
Then we come down here and we can see the values here are now 然後我們來到這裡，我們現在可以看到這裡的值
Scaled between 0 and 1 and that just makes it easier for a network to learn we don't have to do this 縮放在0和1之間，這使網絡更容易學習，我們不必這樣做
But at the end of this only probably won't have time 但在這結束時，可能沒有時間
But if you want on, you know, comment those lines out and see how it effects the network. It's it's pretty significant 但是，如果您想要，請注意這些線路，看看它如何影響網絡。這是非常重要的
Ok. So the next thing we're gonna do now is actually build the model 好。所以接下來我們要做的就是建立模型
So the model itself is gonna start as TF karosta model's dot and then it's going to be the sequential type of model 因此模型本身將以TF karosta模型的點開始，然後它將成為順序類型的模型
There's two types of models 有兩種類型的模型
The sequential is your your most common one. It's a feed-forward like the image we drew 順序是您最常見的順序。它像我們畫的圖像一樣前饋
So we're gonna use this sequential model and then from here we can use this like model dot add syntax 所以我們將使用這個順序模型，然後從這裡我們可以像模型點添加語法一樣使用它
so the first layer is gonna be the input layer and now right now our images are 28 by 28 in this like 所以第一層將成為輸入層，現在我們的圖像就像28到28一樣
Multi-dimensional array we don't want that 我們不希望這樣的多維數組
We want them to be just like flat if we were doing like a confident or something like that 如果我們像自信或類似的東西那樣做，我們希望它們就像平板一樣
We might not want it to be flat 我們可能不希望它持平
but in this case 但在這種情況下
we definitely want to flatten it so we could use that we could use like numpy and reshape or 我們肯定想要展平它，所以我們可以使用我們可以使用的像numpy和reshape或
We can actually use one of the layers that's built into chaos, which is flattened. So 我們實際上可以使用其中一個內置為混亂的層，這些層是扁平的。所以
So we're gonna do ad and what we're gonna add is TF. Chaos layers dot flatten 所以我們要做廣告，我們要添加的是TF。混亂層數點平
so one of the reasons why you you want this to actually be a layer type is like when you have a 所以你希望它實際上是一個圖層類型的原因之一就像你有一個
Convolutional neural network a lot of times at the end of the convolutional neural network. There'll be just like a densely connected 卷積神經網絡結束時很多次卷積神經網絡。就像密集連接一樣
Layer, and so you need to flatten it before that layer. So it's it's it's used for more than then the input layer 圖層，因此您需要在該圖層之前將其展平。所以它的用途不僅僅是輸入層
We're just use it for the input layer 我們只是將它用於輸入層
Just to make our lives easier. So once we've got that 只是為了讓我們的生活更輕鬆所以一旦我們有了這個
That's our input layer. Now. We want to do our hidden layers again 那是我們的輸入層。現在。我們想再次做隱藏的圖層
We're going to go with I think just two hidden layers. This isn't a complex problem to solve 我們將繼續我認為只有兩個隱藏層。這不是一個需要解決的複雜問題
So again, we're going to use the model set up model dot add syntax and we're gonna add and in fact 所以，我們將再次使用模型設置模型點添加語法，我們將添加並實際上
I think what I'm gonna do is copy paste and then rather than a flattened layer it's a dense layer in the dense layer 我想我要做的就是複製粘貼然後而不是平坦的層，它是致密層中的致密層
We're gonna pass a couple parameters here. So the first one is gonna be how many units in the layer. So we're gonna use 我們要在這裡傳遞幾個參數。所以第一個是層中的單位數。所以我們要用
128 units or 128 neurons in the layer, then we're gonna pass the activation function 層中有128個單元或128個神經元，那麼我們將通過激活功能
This is like the function. Like I said like a stepper function or a sigmoid function 這就像功能一樣。就像我說的像步進功能或sigmoid功能
What is gonna make that neuron fire or sort of fire whatever so we're gonna use TF tenon 什麼會使神經元發生火災或火災，所以我們將使用TF榫頭
Lu for rectified linear, this is kind of the default go-to Lu用於糾正線性，這是一種默認的選擇
Activation function just use it as your default and then later you can tweak it to see if you can get better results 激活功能只是將其用作默認值，然後您可以調整它以查看是否可以獲得更好的結果
But it's a pretty good one to always fall back on 但總是可以依靠它是一個非常好的
So we're gonna add the second one just by copying and pasting 所以我們只是通過複製和粘貼來添加第二個
Again, I'm gonna add a final one and this is our output layer, which is still going to be a dense layer 再說一次，我要添加一個最後一個，這是我們的輸出層，它仍然是一個密集的層
It's not a hundred and twenty eight your output layer will always if it's in the case of classification 如果在分類的情況下，輸出層總是不是一百二十八
Anyways, it'll have your number of classifications in our case. That's ten and 無論如何，在我們的案例中，它會有你的分類數量。那是十和
the activation function we don't want it to be rel you because we actually this is like a 激活功能我們不希望它依賴於你，因為我們實際上這就像一個
Probability distribution so we want to use softmax for a probability distribution. So 概率分布因此我們希望使用softmax進行概率分布。所以
That is our entire model. We're done with defining, you know, the the architecture I guess of our model 這是我們的整個模型。我們完成了定義，我知道我的模型的架構
Now what we need to define is some parameters for the training of the model 現在我們需要定義的是模型訓練的一些參數
So here we're going to say model dot compile and in here 所以我們在這裡說模型點編譯
We're gonna pass some we're gonna pass the optimizer that we want to use 我們要通過一些我們要通過我們想要使用的優化器
We're gonna pass the loss metric which loss you don't know we haven't really talked about it loss is the degree of error 我們將通過損失指標，你不知道我們還沒有真正談到它的損失是錯誤的程度
Basically, it's it's it's it's what you got wrong. So a normal network doesn't actually attempt to optimize for accuracy 基本上，就是這就是你錯了。因此，普通網絡實際上並不試圖優化準確性
It doesn't try to maximize accuracy. It's always trying to minimize loss 它並不試圖最大限度地提高準確性。它一直在努力減少損失
So the way that you calculate loss can make a huge impact because it's it's what's the losses? 因此，計算損失的方式可以產生巨大的影響，因為它的損失是什麼？
relationship to your 與你的關係
to your accuracy op timer 你的準確度
Optimizer 優化
Okay, so the optimizer that we're going to use is going to be the Adam optimizer you could use something 好的，所以我們將要使用的優化器將成為你可以使用的Adam優化器
this is basically it's just your it's your 這基本上就是你的，這就是你的
It's the this is like the most complex part of the entire neural network 這就像整個神經網絡中最複雜的部分
So if you're familiar with gradient descent, you could pass something like stochastic gradient descent 因此，如果您熟悉漸變下降，您可以傳遞類似隨機梯度下降的東西
But the Adam optimizer kind of like the rectified linear is the you know 但是你所知道的亞當優化器有點像糾正線性
Just kind of the default go-to optimizer you can use others 只是一種默認的定位優化器，您可以使用其他人
There's lots of them not lots, but I don't know ten or so in Karis anyways, so anyways 有很多不是很多，但我不知道卡里斯十個左右，所以無論如何
There's other ones to go with Adam seems to be the one that you should start with for loss again 亞當似乎是另一個與你一起開始再次失敗的人
There's many ways to calculate loss 計算損失的方法有很多種
probably the most popular one is a 可能最受歡迎的一個是
categorical 明確的
Cross and true P or some version of that and that in this case, we're gonna use sparse 交叉和真正的P或某些版本，在這種情況下，我們將使用稀疏
You can also use binary like in the case of cats versus dogs. You probably use binary in that case 你也可以使用像貓和狗一樣的二進制文件。在這種情況下你可能會使用二進制
But you could you could just kind of blanket categorical cross entropy everything 但是你可以只用一種毯子分類交叉熵一切
Anyways, then finally what what are the metrics we want to track like as we go and we're going to just do accuracy 無論如何，那麼最後我們想要跟蹤的指標是什麼，就像我們去的那樣，我們只是做準確性
accuracy 準確性
Okay. So once we have all this we're actually ready to to train the model 好的。所以一旦我們擁有了所有這些，我們就會準備好訓練模型
So to Train it's just model that fit and then you're gonna pass your what do you want to fit? 所以要訓練它只是適合的模型然後你會通過你想要的東西嗎？
so X train X test, I'm sorry y 所以X訓練X測試，對不起
Ok X train white rain and then epochs three total brain for it, okay, let's go ahead and run that and 好的X訓練白色的雨，然後是時代的三個大腦，好吧，讓我們繼續前進並運行那個和
We should start to get some training 我們應該開始接受一些培訓
Hopefully it doesn't crash me as I'm recording but okay looks good 希望它不會讓我崩潰，因為我正在錄音，但好看起來不錯
Let's zoom out just slightly 讓我們稍微縮小一下
So it looks a little better and we can see actually our accuracy is always already quite well 所以它看起來好一點，我們可以看到實際上我們的準確性總是很好
Our loss is also still dropping so our accuracy should still be improving and sure enough it is 我們的損失也在下降，所以我們的準確性仍然應該提高，而且確實如此
Awesome, ok, so we did pretty good. We got a 太棒了，好的，所以我們做得很好。我們有一個
97% accuracy after only three epochs which is pretty good 僅僅三個時期之後97％的準確率非常好
so once we have this we can 所以，一旦我們有這個，我們就可以
This was in sample. So this might this is always gonna really excite you 這是樣本。所以這可能會讓你真的很興奮
But what's really important to remember is neural networks are great at fitting the question is did they over fit? 但是要記住的是，神經網絡能夠很好地解決它們是否過於合適的問題？
So the idea or the hope is that your model actually generalized, right it learned 所以這個想法或者希望是你的模型實際上是概括的，對它的學習是正確的
Patterns and in actual attributes to the toe to what makes an eight ten 模式和實際屬性到腳趾到什麼使八十
what makes a for rather than memorizing every single sample you passed and you'd be surprised how easily a 是什麼讓一個人而不是記住你傳遞的每一個樣本，你會驚訝地發現
Model can just memorize all the samples that you passed to do very well 模型只需記住您傳遞的所有樣本即可
so the next thing we want to always do is calculate the 所以我們要做的下一件事就是計算
validation loss in the validation 驗證中的驗證丟失
Accuracy, and that is just model dot evaluate evaluate 準確性，這只是模型點評估評估
X tests X測試
Y test and Y測試和
then we'll go ahead and just print that loss and Val accuracy and 然後我們將繼續打印那個損失和Val準確度
We can see here the loss is almost point 11 and the accuracy is at ninety six point five 我們可以看到損失幾乎是第11點，準確度是九十六點五
so a little less than the one that we ended on and the loss is quite a bit higher relatively, but 所以比我們結束的那個少一點，相對而言損失相當高，但是
You should expect that 你應該期待的
You should expect your out-of-sample accuracy to be slightly lower and your loss to be slightly higher 你應該期望你的樣本外精度略低，你的損失會略高一些
What you definitely don't want to see is either too close or it too much of a delta if there's a huge Delta 如果有一個巨大的三角洲，你絕對不希望看到的是太近或太多的三角洲
Chances are you probably already have over fit and you'd want to like kind of dial it back a little bit. So 你可能已經過度適合了，你可能想要一點點撥回來。所以
That's basically everything 這基本上就是一切
as far as the basics of 至於基礎知識
Karos and all that 卡羅斯等等
the only other things that I wouldn't mind 我不介意的其他事情
Covering here is like if you want to save a model and load a model 覆蓋這裡就像是要保存模型並加載模型
It's just model dot save and we can save this as epoch num 它只是模型點保存，我們可以將其保存為epoch num
reader 讀者
model and 模特和
Then if you want to reload that model 然後，如果您想重新加載該模型
We'll save it as it will call the new underscore model that's going to be equal to TF. Caras models dot load model 我們將保存它，因為它將調用將等於TF的新下劃線模型。 Caras模型點載模型
And it's this exact model name 這就是這個確切的型號名稱
Whoops. There we go 哎呦。我們走了
So that's our new model 這就是我們的新模型
And then finally if we wanted to make a prediction we could say predictions equals new model dot 最後，如果我們想做出預測，我們可以說預測等於新模型點
Dict and keep in mind predict always takes a list. This will get you a few times for sure Dict並記住預測總是列出一個列表。這肯定會讓你幾次
But anyways, they'll take a list and we'll do X underscore 但無論如何，他們將列出一個列表，我們將做X下劃線
test and 測試和
Then if we just print predictions, it's probably not gonna look too friendly 然後，如果我們只打印預測，它可能看起來不太友好
It's a little messy. So 這有點亂。所以
What's going on here? These are all one hot arrays and it's it. These are our probability distributions 這裡發生了什麼？這些都是一個熱陣列，就是這樣。這些是我們的概率分布
So what do we do with these so I'm gonna use numpy you can also use TF to arc max, but it's an abstract 那麼我們如何處理這些因此我會使用numpy你也可以使用TF來最大化，但它是一個抽象的
It's a tensor and we have to pull it down. We need a session and all that 這是一個張量，我們必須把它拉下來。我們需要一個會議和所有這一切
It's just easier to import numpy for at least for this tutorial here 在這裡至少為本教程導入numpy更容易
import numpy as NP and then print for example NP arg max or max 將numpy導入NP，然後打印例如NP arg max或max
Let's do predictions 我們來做預測吧
And let's just do the zero width prediction. Okay 讓我們做零寬度預測。好的
It says it's a seven 它說它是七
So the prediction for X test zero like the 0th index is it's a seven 所以X測試零的預測就像第0個索引一樣是7
So gee if only we had a way to draw it. Okay, we can definitely do this so we can do PLT 所以，如果我們只有一種方法來繪製它。好的，我們絕對可以這樣做，所以我們可以做PLT
I'm sure and we're gonna do X test 我很有信心，我們要做X測試
zero with and 零和
Then PLT dot show. Oh 然後PLT點顯示。哦
Look at that. It's a seven 看那個。這是一個七
Okay 好的
So I think that's basically all the things I would want to show you guys as far as like just a quick in 所以我認為這基本上是我想要向你們展示的所有東西，就像快速進入一樣
you know QuickStart with 你知道QuickStart
Deep learning and Python and caris and tensorflow this just barely scratches the surface. There's so many things for us to do I 深度學習和Python以及caris和tensorflow這幾乎沒有觸及表面。我有很多事要做
Definitely plan to have at least one more follow-up video 絕對計劃至少再有一個後續視頻
Covering things like loading and outside datasets and definitely some tensor board reading the model understanding what's going on 覆蓋諸如加載和外部數據集之類的東西，絕對是一些張量板讀取模型，了解正在發生的事情
and also what's going wrong because that's 還有什麼問題，因為那是
Eventually, you know, it's really fun when we're doing tutorials and problems or like already solved and we know the answer 最後，你知道，當我們正在做教程和問題或者已經解決了，我們知道答案時，它真的很有趣
It's very exciting. But in reality a lot of times you have to dig to find the model that works with your data. So 這是非常令人興奮。但實際上很多時候你必須挖掘才能找到適合你數據的模型。所以
Anyways, that that's definitely something we have to cover or at least that you're gonna have to learn somehow or other 無論如何，這絕對是我們必須要涵蓋的，或者至少你必須以某種方式或其他方式學習
Anyway, that is all for now. If you got questions comments concerns, whatever. Feel free to leave them below 無論如何，這就是現在。如果你有問題評論問題，無論如何。隨意將它們留在下面
Definitely check out red comm slash are slash machine learning the learned machine learning subreddit 絕對檢查紅色通信斜槓是斜線機學習學習機器學習subreddit
You can come join our discord 你可以加入我們的不和諧
If you've got questions, that's just discord dot G G slash cent decks will get you there. Also special. Thanks to my most recent 如果你有問題，那就是不和諧的GG斜線分甲板會讓你在那裡。還特別。感謝我最近的
Channel members Daniel Jeffrey KB AB Ajit H nur Newcastle geek fubá 44 Jason and eight counts 頻道成員Daniel Jeffrey KB AB Ajit H nur Newcastlegeekfubá44Jason和8個計數
Thank you guys so much for your support without you guys. I couldn't do stuff like this. So really, thank you 沒有你們，非常感謝你們的支持。我做不到這樣的事情。真的，謝謝你
Anyways, that's it for now more to come at some point until next time 無論如何，現在更多的是在下一次到來之前