原文转载自「刘悦的技术博客」https://v3u.cn/a_id_189
笔者投入 M1 的怀抱曾经有一段时间了，俗话说得好，但闻新人笑，不见旧人哭，Intel mac 早已被束之高阁，而 M1 mac 曾经不能用真香来形容了，几乎就是“香透满堂金玉彩，扇遮半面桃花开！”，轻抚 M1 mac 那滑若柔荑的秒控键盘，别说 996 了，就是 007，咱们也能安之若素，也能够笑慰平生。好了，日常吹 M1 的环节完结，正所谓剑虽利, 不厉一直，材虽美，不学不高。本次咱们尝试在 M1 Mac os 中搭建 Python3 的开发环境。
个别状况下，间接 Python 官网 (python.org) 下载最新的基于 arm 架构的 python3.9 即可，然而因为向下兼容等问题，咱们尝试应用 Python 多版本管理软件 conda，conda 在业界有三大巨头别离是:Anaconda、Miniconda 以及 Condaforge，尽管都放出音讯要适配 M1 芯片，然而目前最先放出稳定版的是 Condaforge，进入下载页面：https://github.com/conda-forg… 抉择 mac arm64 位架构:
该文件并不是安装包，而是一个 shell 脚本，下载胜利后，进入命令行目录:
cd ~/Downloas执行命令进行装置：
sudo bash ./Miniforge3-MacOSX-arm64.sh随后会有一些条款须要确认，这里按回车之后键入 yes:
Welcome to Miniforge3 4.9.2-7
In order to continue the installation process, please review the license
agreement.
Please, press ENTER to continue
>>>
BSD 3-clause license
Copyright (c) 2019-2020, conda-forge
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors
may be used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Do you accept the license terms? [yes|no]
[no] >>> yes装置的默认版本还是 3.9，会附带装置 35 个根底库，这样就不必咱们本人手动装置了：
brotlipy 0.7.0 py39h46acfd9_1001 installed
bzip2 1.0.8 h27ca646_4 installed
ca-certificates 2020.12.5 h4653dfc_0 installed
certifi 2020.12.5 py39h2804cbe_1 installed
cffi 1.14.5 py39h702c04f_0 installed
chardet 4.0.0 py39h2804cbe_1 installed
conda 4.9.2 py39h2804cbe_0 installed
conda-package-handling 1.7.2 py39h51e6412_0 installed
cryptography 3.4.4 py39h6e07874_0 installed
idna 2.10 pyh9f0ad1d_0 installed
libcxx 11.0.1 h168391b_0 installed
libffi 3.3 h9f76cd9_2 installed
ncurses 6.2 h9aa5885_4 installed
openssl 1.1.1j h27ca646_0 installed
pip 21.0.1 pyhd8ed1ab_0 installed
pycosat 0.6.3 py39h46acfd9_1006 installed
pycparser 2.20 pyh9f0ad1d_2 installed
pyopenssl 20.0.1 pyhd8ed1ab_0 installed
pysocks 1.7.1 py39h2804cbe_3 installed
python 3.9.2 hcbd9b3a_0_cpython installed
python_abi 3.9 1_cp39 installed
readline 8.0 hc8eb9b7_2 installed
requests 2.25.1 pyhd3deb0d_0 installed
ruamel_yaml 0.15.80 py39h46acfd9_1004 installed
setuptools 49.6.0 py39h2804cbe_3 installed
six 1.15.0 pyh9f0ad1d_0 installed
sqlite 3.34.0 h6d56c25_0 installed
tk 8.6.10 hf7e6567_1 installed
tqdm 4.57.0 pyhd8ed1ab_0 installed
tzdata 2021a he74cb21_0 installed
urllib3 1.26.3 pyhd8ed1ab_0 installed
wheel 0.36.2 pyhd3deb0d_0 installed
xz 5.2.5 h642e427_1 installed
yaml 0.2.5 h642e427_0 installed
zlib 1.2.11 h31e879b_1009 installed而后编辑配置文件 vim ~/.zshrc，退出如下内容(此处 liuyue 是笔者用户名，需改成你的 Mac 以后用户名)：
path=('/Users/liuyue/miniforge3/bin' $path)
export PATH存盘之后执行命令：
source ~/.zshrc配置好环境变量之后，键入 python3：
➜ ~ python3
Python 3.9.2 | packaged by conda-forge | (default, Feb 21 2021, 05:00:30)
[Clang 11.0.1] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>能够看到曾经应用 conda 装置的 python 版本了。
这里简略介绍一下 conda 命令：
conda info 能够查看以后 conda 的根本信息内核，平台，下载源以及目录地位：
➜ ~ conda info
active environment : None
user config file : /Users/liuyue/.condarc
populated config files : /Users/liuyue/miniforge3/.condarc
conda version : 4.9.2
conda-build version : not installed
python version : 3.9.2.final.0
virtual packages : __osx=11.2.2=0
__unix=0=0
__archspec=1=arm64
base environment : /Users/liuyue/miniforge3 (read only)
channel URLs : https://conda.anaconda.org/conda-forge/osx-arm64
https://conda.anaconda.org/conda-forge/noarch
package cache : /Users/liuyue/miniforge3/pkgs
/Users/liuyue/.conda/pkgs
envs directories : /Users/liuyue/.conda/envs
/Users/liuyue/miniforge3/envs
platform : osx-arm64
user-agent : conda/4.9.2 requests/2.25.1 CPython/3.9.2 Darwin/20.3.0 OSX/11.2.2
UID:GID : 502:20
netrc file : None
offline mode : False因为一些家喻户晓的学术问题，咱们须要配置一下国内下载源：
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/main/
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/msys2/
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/bioconda/
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/menpo/
conda config --set show_channel_urls yes随后查看以后下载源：
conda config --show能够看到国内源曾经被增加进去了：
channel_priority: flexible
channels:
- https://mirrors.ustc.edu.cn/anaconda/cloud/menpo/
- https://mirrors.ustc.edu.cn/anaconda/cloud/bioconda/
- https://mirrors.ustc.edu.cn/anaconda/cloud/msys2/
- https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/
- https://mirrors.ustc.edu.cn/anaconda/pkgs/free/
- https://mirrors.ustc.edu.cn/anaconda/pkgs/main/
- defaults
- conda-forge
client_ssl_cert: None其余的一些 conda 常用命令：
1. conda –version #查看 conda 版本，验证是否装置
2. conda update conda #更新至最新版本，也会更新其它相干包
3. conda update –all #更新所有包
4. conda update package\_name #更新指定的包
5. conda create -n env\_name package\_name #创立名为 env\_name 的新环境，并在该环境下装置名为 package\_name 的包，能够指定新环境的版本号，例如：conda create -n python2 python=python2.7 numpy pandas，创立了 python2 环境，python 版本为 2.7，同时还装置了 numpy pandas 包
6. source activate env\_name #切换至 env\_name 环境
7. source deactivate #退出环境
8. conda info -e #显示所有曾经创立的环境
9. conda create –name new\_env\_name –clone old\_env\_name #复制 old\_env\_name 为 new\_env\_name
10. conda remove –name env\_name –all #删除环境
11. conda list #查看所有曾经装置的包
12. conda install package\_name #在以后环境中安装包
13. conda install –name env\_name package\_name #在指定环境中安装包
14. conda remove — name env\_name package #删除指定环境中的包
15. conda remove package #删除以后环境中的包
16. conda create -n tensorflow\_env tensorflow
conda activate tensorflow\_env #conda 装置 tensorflow 的 CPU 版本
17. conda create -n tensorflow\_gpuenv tensorflow-gpu
conda activate tensorflow\_gpuenv #conda 装置 tensorflow 的 GPU 版本
18. conda env remove -n env\_name #采纳第 10 条的办法删除环境失败时，可采纳这种办法
接着咱们来尝试集成深度学习框架 Tensorflow，因为目前默认是 3.9，咱们应用 conda 创立一个 3.8 的虚构开发环境：
sudo conda create -n py38 python=3.8装置胜利后，输出命令：
conda info -e就能够查看以后 conda 装置的所有版本：
➜ ~ conda info -e
# conda environments:
#
base * /Users/liuyue/miniforge3
py38 /Users/liuyue/miniforge3/envs/py38能够看到一个默认的 3.9 环境，和新装的 3.8 环境，星号代表以后所处的环境，这里咱们切换到 3.8：
conda activate py38此时环境曾经切换到 3.8：
(py38) ➜ ~ conda activate py38
(py38) ➜ ~ conda info -e
# conda environments:
#
base /Users/liuyue/miniforge3
py38 * /Users/liuyue/miniforge3/envs/py38
(py38) ➜ ~上面开启深度学习框架 Tensorflow 之旅，因为苹果对 m1 芯片独自做了适配，所以不能用以前的 pip 形式间接进行装置，须要独自下载文件：https://github.91chifun.worke…://github.com/apple/tensorflow\_macos/releases/download/v0.1alpha1/tensorflow\_macos-0.1alpha1.tar.gz
解压文件：
tar -zxvf tensorflow_macos-0.1alpha1.tar.gz解压后进入目录(肯定要进入 arm64 的文件内)：
cd tensorflow_macos/arm64执行命令利用下载的 arm64 内核安装包进行装置：
pip install --force pip==20.2.4 wheel setuptools cached-property six
pip install --upgrade --no-dependencies --force numpy-1.18.5-cp38-cp38-macosx_11_0_arm64.whl grpcio-1.33.2-cp38-cp38-macosx_11_0_arm64.whl h5py-2.10.0-cp38-cp38-macosx_11_0_arm64.whl tensorflow_addons-0.11.2+mlcompute-cp38-cp38-macosx_11_0_arm64.whl
pip install absl-py astunparse flatbuffers gast google_pasta keras_preprocessing opt_einsum protobuf tensorflow_estimator termcolor typing_extensions wrapt wheel tensorboard typeguard
pip install --upgrade --force --no-dependencies tensorflow_macos-0.1a1-cp38-cp38-macosx_11_0_arm64.whl装置胜利后，测试一下：
(py38) ➜ arm64 python
Python 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 15:50:57)
[Clang 11.0.1] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow
>>>没有任何问题。
上面咱们来测试一下 M1 通过 Tensorflow 训练模型的效率，还记得衣香鬓影的“机械姬”吗：人工智能不过尔尔，基于 Python3 深度学习库 Keras/TensorFlow 打造属于本人的聊天机器人(ChatRobot)
编写 my\_chat.py:
import nltk
import ssl
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import SGD
import pandas as pd
import pickle
import random
words = []
classes = []
documents = []
ignore_words = ['?']
# loop through each sentence in our intents patterns
intents = {"intents": [
{"tag": "打招呼",
"patterns": ["你好", "您好", "请问", "有人吗", "徒弟","不好意思","美女","帅哥","靓妹"],
"responses": ["您好", "又是您啊", "吃了么您内","您有事吗"],
"context": [""]
},
{"tag": "辞别",
"patterns": ["再见", "拜拜", "88", "回见", "回头见"],
"responses": ["再见", "一路顺风", "下次见", "拜拜了您内"],
"context": [""]
},
]
}
for intent in intents['intents']:
for pattern in intent['patterns']:
# tokenize each word in the sentence
w = nltk.word_tokenize(pattern)
# add to our words list
words.extend(w)
# add to documents in our corpus
documents.append((w, intent['tag']))
# add to our classes list
if intent['tag'] not in classes:
classes.append(intent['tag'])
# stem and lower each word and remove duplicates
words = [stemmer.stem(w.lower()) for w in words if w not in ignore_words]
words = sorted(list(set(words)))
# sort classes
classes = sorted(list(set(classes)))
# documents = combination between patterns and intents
# print (len(documents), "documents")
# # classes = intents
# print (len(classes), "语境", classes)
# # words = all words, vocabulary
# print (len(words), "词数", words)
# create our training data
training = []
# create an empty array for our output
output_empty = [0] * len(classes)
# training set, bag of words for each sentence
for doc in documents:
# initialize our bag of words
bag = []
pattern_words = doc[0]
pattern_words = [stemmer.stem(word.lower()) for word in pattern_words]
for w in words:
bag.append(1) if w in pattern_words else bag.append(0)
output_row = list(output_empty)
output_row[classes.index(doc[1])] = 1
training.append([bag, output_row])
random.shuffle(training)
training = np.array(training)
train_x = list(training[:,0])
train_y = list(training[:,1])
model = Sequential()
model.add(Dense(128, input_shape=(len(train_x[0]),), activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(len(train_y[0]), activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
def clean_up_sentence(sentence):
# tokenize the pattern - split words into array
sentence_words = nltk.word_tokenize(sentence)
# stem each word - create short form for word
sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
return sentence_words
# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
def bow(sentence, words, show_details=True):
# tokenize the pattern
sentence_words = clean_up_sentence(sentence)
# bag of words - matrix of N words, vocabulary matrix
bag = [0]*len(words)
for s in sentence_words:
for i,w in enumerate(words):
if w == s:
# assign 1 if current word is in the vocabulary position
bag[i] = 1
if show_details:
print ("found in bag: %s" % w)
return(np.array(bag))
def classify_local(sentence):
ERROR_THRESHOLD = 0.25
# generate probabilities from the model
input_data = pd.DataFrame([bow(sentence, words)], dtype=float, index=['input'])
results = model.predict([input_data])[0]
# filter out predictions below a threshold, and provide intent index
results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD]
# sort by strength of probability
results.sort(key=lambda x: x[1], reverse=True)
return_list = []
for r in results:
return_list.append((classes[r[0]], str(r[1])))
# return tuple of intent and probability
return return_list
p = bow("你好", words)
print (p)
print(classify_local('请问'))返回后果：
(py38) ➜ mytornado git:(master) ✗ python3 test_mychat.py
2021-03-03 22:43:21.059383: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2021-03-03 22:43:21.059529: W tensorflow/core/platform/profile_utils/cpu_utils.cc:126] Failed to get CPU frequency: 0 Hz
Epoch 1/200
3/3 [==============================] - 0s 570us/step - loss: 0.6966 - accuracy: 0.5750
Epoch 2/200
3/3 [==============================] - 0s 482us/step - loss: 0.6913 - accuracy: 0.4857
Epoch 3/200
3/3 [==============================] - 0s 454us/step - loss: 0.6795 - accuracy: 0.4750
Epoch 4/200
3/3 [==============================] - 0s 434us/step - loss: 0.6913 - accuracy: 0.4750
Epoch 5/200
3/3 [==============================] - 0s 417us/step - loss: 0.6563 - accuracy: 0.5107
Epoch 6/200
3/3 [==============================] - 0s 454us/step - loss: 0.6775 - accuracy: 0.5714
Epoch 7/200
3/3 [==============================] - 0s 418us/step - loss: 0.6582 - accuracy: 0.6964
Epoch 8/200
3/3 [==============================] - 0s 487us/step - loss: 0.6418 - accuracy: 0.8071
Epoch 9/200
3/3 [==============================] - 0s 504us/step - loss: 0.6055 - accuracy: 0.6964
Epoch 10/200
3/3 [==============================] - 0s 457us/step - loss: 0.5933 - accuracy: 0.6964
Epoch 11/200
3/3 [==============================] - 0s 392us/step - loss: 0.6679 - accuracy: 0.5714
Epoch 12/200
3/3 [==============================] - 0s 427us/step - loss: 0.6060 - accuracy: 0.7464
Epoch 13/200
3/3 [==============================] - 0s 425us/step - loss: 0.6677 - accuracy: 0.5964
Epoch 14/200
3/3 [==============================] - 0s 420us/step - loss: 0.6208 - accuracy: 0.6214
Epoch 15/200
3/3 [==============================] - 0s 401us/step - loss: 0.6315 - accuracy: 0.6714
Epoch 16/200
3/3 [==============================] - 0s 401us/step - loss: 0.6718 - accuracy: 0.6464
Epoch 17/200
3/3 [==============================] - 0s 386us/step - loss: 0.6407 - accuracy: 0.6714
Epoch 18/200
3/3 [==============================] - 0s 505us/step - loss: 0.6031 - accuracy: 0.6464
Epoch 19/200
3/3 [==============================] - 0s 407us/step - loss: 0.6245 - accuracy: 0.6214
Epoch 20/200
3/3 [==============================] - 0s 422us/step - loss: 0.5805 - accuracy: 0.6964
Epoch 21/200
3/3 [==============================] - 0s 379us/step - loss: 0.6923 - accuracy: 0.5464
Epoch 22/200
3/3 [==============================] - 0s 396us/step - loss: 0.6383 - accuracy: 0.5714
Epoch 23/200
3/3 [==============================] - 0s 427us/step - loss: 0.6628 - accuracy: 0.5714
Epoch 24/200
3/3 [==============================] - 0s 579us/step - loss: 0.6361 - accuracy: 0.5964
Epoch 25/200
3/3 [==============================] - 0s 378us/step - loss: 0.5632 - accuracy: 0.7214
Epoch 26/200
3/3 [==============================] - 0s 387us/step - loss: 0.6851 - accuracy: 0.5214
Epoch 27/200
3/3 [==============================] - 0s 393us/step - loss: 0.6012 - accuracy: 0.6214
Epoch 28/200
3/3 [==============================] - 0s 392us/step - loss: 0.6470 - accuracy: 0.5964
Epoch 29/200
3/3 [==============================] - 0s 348us/step - loss: 0.6346 - accuracy: 0.6214
Epoch 30/200
3/3 [==============================] - 0s 362us/step - loss: 0.6350 - accuracy: 0.4964
Epoch 31/200
3/3 [==============================] - 0s 369us/step - loss: 0.5842 - accuracy: 0.5964
Epoch 32/200
3/3 [==============================] - 0s 481us/step - loss: 0.5279 - accuracy: 0.7214
Epoch 33/200
3/3 [==============================] - 0s 439us/step - loss: 0.5956 - accuracy: 0.7321
Epoch 34/200
3/3 [==============================] - 0s 355us/step - loss: 0.5570 - accuracy: 0.6964
Epoch 35/200
3/3 [==============================] - 0s 385us/step - loss: 0.5546 - accuracy: 0.8071
Epoch 36/200
3/3 [==============================] - 0s 375us/step - loss: 0.5616 - accuracy: 0.6714
Epoch 37/200
3/3 [==============================] - 0s 379us/step - loss: 0.6955 - accuracy: 0.6464
Epoch 38/200
3/3 [==============================] - 0s 389us/step - loss: 0.6089 - accuracy: 0.7321
Epoch 39/200
3/3 [==============================] - 0s 375us/step - loss: 0.5377 - accuracy: 0.6714
Epoch 40/200
3/3 [==============================] - 0s 392us/step - loss: 0.6224 - accuracy: 0.7179
Epoch 41/200
3/3 [==============================] - 0s 379us/step - loss: 0.6234 - accuracy: 0.5464
Epoch 42/200
3/3 [==============================] - 0s 411us/step - loss: 0.5224 - accuracy: 0.8321
Epoch 43/200
3/3 [==============================] - 0s 386us/step - loss: 0.5848 - accuracy: 0.5964
Epoch 44/200
3/3 [==============================] - 0s 401us/step - loss: 0.4620 - accuracy: 0.8679
Epoch 45/200
3/3 [==============================] - 0s 365us/step - loss: 0.4664 - accuracy: 0.8071
Epoch 46/200
3/3 [==============================] - 0s 367us/step - loss: 0.5904 - accuracy: 0.7679
Epoch 47/200
3/3 [==============================] - 0s 359us/step - loss: 0.5111 - accuracy: 0.7929
Epoch 48/200
3/3 [==============================] - 0s 363us/step - loss: 0.4712 - accuracy: 0.8679
Epoch 49/200
3/3 [==============================] - 0s 401us/step - loss: 0.5601 - accuracy: 0.8071
Epoch 50/200
3/3 [==============================] - 0s 429us/step - loss: 0.4884 - accuracy: 0.7929
Epoch 51/200
3/3 [==============================] - 0s 377us/step - loss: 0.5137 - accuracy: 0.8286
Epoch 52/200
3/3 [==============================] - 0s 368us/step - loss: 0.5475 - accuracy: 0.8286
Epoch 53/200
3/3 [==============================] - 0s 592us/step - loss: 0.4077 - accuracy: 0.8536
Epoch 54/200
3/3 [==============================] - 0s 400us/step - loss: 0.5367 - accuracy: 0.8179
Epoch 55/200
3/3 [==============================] - 0s 399us/step - loss: 0.5288 - accuracy: 0.8429
Epoch 56/200
3/3 [==============================] - 0s 367us/step - loss: 0.5775 - accuracy: 0.6964
Epoch 57/200
3/3 [==============================] - 0s 372us/step - loss: 0.5680 - accuracy: 0.6821
Epoch 58/200
3/3 [==============================] - 0s 360us/step - loss: 0.5164 - accuracy: 0.7321
Epoch 59/200
3/3 [==============================] - 0s 364us/step - loss: 0.5334 - accuracy: 0.6571
Epoch 60/200
3/3 [==============================] - 0s 358us/step - loss: 0.3858 - accuracy: 0.9036
Epoch 61/200
3/3 [==============================] - 0s 356us/step - loss: 0.4313 - accuracy: 0.8679
Epoch 62/200
3/3 [==============================] - 0s 373us/step - loss: 0.5017 - accuracy: 0.8429
Epoch 63/200
3/3 [==============================] - 0s 346us/step - loss: 0.4649 - accuracy: 0.8429
Epoch 64/200
3/3 [==============================] - 0s 397us/step - loss: 0.3804 - accuracy: 0.8893
Epoch 65/200
3/3 [==============================] - 0s 361us/step - loss: 0.5030 - accuracy: 0.7929
Epoch 66/200
3/3 [==============================] - 0s 372us/step - loss: 0.3958 - accuracy: 0.9286
Epoch 67/200
3/3 [==============================] - 0s 345us/step - loss: 0.4240 - accuracy: 0.8536
Epoch 68/200
3/3 [==============================] - 0s 360us/step - loss: 0.4651 - accuracy: 0.7929
Epoch 69/200
3/3 [==============================] - 0s 376us/step - loss: 0.4687 - accuracy: 0.7571
Epoch 70/200
3/3 [==============================] - 0s 398us/step - loss: 0.4660 - accuracy: 0.8429
Epoch 71/200
3/3 [==============================] - 0s 368us/step - loss: 0.3960 - accuracy: 0.9393
Epoch 72/200
3/3 [==============================] - 0s 355us/step - loss: 0.5523 - accuracy: 0.6071
Epoch 73/200
3/3 [==============================] - 0s 361us/step - loss: 0.5266 - accuracy: 0.7821
Epoch 74/200
3/3 [==============================] - 0s 371us/step - loss: 0.4245 - accuracy: 0.9643
Epoch 75/200
3/3 [==============================] - 0s 367us/step - loss: 0.5024 - accuracy: 0.7786
Epoch 76/200
3/3 [==============================] - 0s 453us/step - loss: 0.3419 - accuracy: 0.9393
Epoch 77/200
3/3 [==============================] - 0s 405us/step - loss: 0.4930 - accuracy: 0.7429
Epoch 78/200
3/3 [==============================] - 0s 672us/step - loss: 0.3443 - accuracy: 0.9036
Epoch 79/200
3/3 [==============================] - 0s 386us/step - loss: 0.3864 - accuracy: 0.8893
Epoch 80/200
3/3 [==============================] - 0s 386us/step - loss: 0.3863 - accuracy: 0.9286
Epoch 81/200
3/3 [==============================] - 0s 391us/step - loss: 0.2771 - accuracy: 0.8679
Epoch 82/200
3/3 [==============================] - 0s 370us/step - loss: 0.6083 - accuracy: 0.5571
Epoch 83/200
3/3 [==============================] - 0s 387us/step - loss: 0.2801 - accuracy: 0.9393
Epoch 84/200
3/3 [==============================] - 0s 357us/step - loss: 0.2483 - accuracy: 0.9286
Epoch 85/200
3/3 [==============================] - 0s 355us/step - loss: 0.2511 - accuracy: 0.9643
Epoch 86/200
3/3 [==============================] - 0s 339us/step - loss: 0.3410 - accuracy: 0.8893
Epoch 87/200
3/3 [==============================] - 0s 361us/step - loss: 0.3432 - accuracy: 0.9036
Epoch 88/200
3/3 [==============================] - 0s 347us/step - loss: 0.3819 - accuracy: 0.8893
Epoch 89/200
3/3 [==============================] - 0s 361us/step - loss: 0.5142 - accuracy: 0.7179
Epoch 90/200
3/3 [==============================] - 0s 502us/step - loss: 0.3055 - accuracy: 0.9393
Epoch 91/200
3/3 [==============================] - 0s 377us/step - loss: 0.3144 - accuracy: 0.8536
Epoch 92/200
3/3 [==============================] - 0s 376us/step - loss: 0.3712 - accuracy: 0.9036
Epoch 93/200
3/3 [==============================] - 0s 389us/step - loss: 0.1974 - accuracy: 0.9393
Epoch 94/200
3/3 [==============================] - 0s 365us/step - loss: 0.3128 - accuracy: 0.9393
Epoch 95/200
3/3 [==============================] - 0s 376us/step - loss: 0.2194 - accuracy: 1.0000
Epoch 96/200
3/3 [==============================] - 0s 377us/step - loss: 0.1994 - accuracy: 1.0000
Epoch 97/200
3/3 [==============================] - 0s 360us/step - loss: 0.1734 - accuracy: 0.9643
Epoch 98/200
3/3 [==============================] - 0s 367us/step - loss: 0.1786 - accuracy: 1.0000
Epoch 99/200
3/3 [==============================] - 0s 358us/step - loss: 0.4158 - accuracy: 0.8286
Epoch 100/200
3/3 [==============================] - 0s 354us/step - loss: 0.3131 - accuracy: 0.7571
Epoch 101/200
3/3 [==============================] - 0s 350us/step - loss: 0.1953 - accuracy: 0.8893
Epoch 102/200
3/3 [==============================] - 0s 403us/step - loss: 0.2577 - accuracy: 0.8429
Epoch 103/200
3/3 [==============================] - 0s 417us/step - loss: 0.2648 - accuracy: 0.8893
Epoch 104/200
3/3 [==============================] - 0s 377us/step - loss: 0.2901 - accuracy: 0.8286
Epoch 105/200
3/3 [==============================] - 0s 383us/step - loss: 0.2822 - accuracy: 0.9393
Epoch 106/200
3/3 [==============================] - 0s 381us/step - loss: 0.2837 - accuracy: 0.9036
Epoch 107/200
3/3 [==============================] - 0s 382us/step - loss: 0.3064 - accuracy: 0.8536
Epoch 108/200
3/3 [==============================] - 0s 352us/step - loss: 0.3376 - accuracy: 0.9036
Epoch 109/200
3/3 [==============================] - 0s 376us/step - loss: 0.3412 - accuracy: 0.8536
Epoch 110/200
3/3 [==============================] - 0s 363us/step - loss: 0.1718 - accuracy: 1.0000
Epoch 111/200
3/3 [==============================] - 0s 347us/step - loss: 0.1899 - accuracy: 0.8786
Epoch 112/200
3/3 [==============================] - 0s 363us/step - loss: 0.2352 - accuracy: 0.8286
Epoch 113/200
3/3 [==============================] - 0s 373us/step - loss: 0.1378 - accuracy: 1.0000
Epoch 114/200
3/3 [==============================] - 0s 353us/step - loss: 0.4288 - accuracy: 0.7071
Epoch 115/200
3/3 [==============================] - 0s 456us/step - loss: 0.4202 - accuracy: 0.6821
Epoch 116/200
3/3 [==============================] - 0s 382us/step - loss: 0.2962 - accuracy: 0.8893
Epoch 117/200
3/3 [==============================] - 0s 394us/step - loss: 0.2571 - accuracy: 0.8893
Epoch 118/200
3/3 [==============================] - 0s 365us/step - loss: 0.2697 - accuracy: 1.0000
Epoch 119/200
3/3 [==============================] - 0s 358us/step - loss: 0.3102 - accuracy: 0.9036
Epoch 120/200
3/3 [==============================] - 0s 367us/step - loss: 0.2928 - accuracy: 0.8286
Epoch 121/200
3/3 [==============================] - 0s 374us/step - loss: 0.3157 - accuracy: 0.8286
Epoch 122/200
3/3 [==============================] - 0s 381us/step - loss: 0.3920 - accuracy: 0.7786
Epoch 123/200
3/3 [==============================] - 0s 335us/step - loss: 0.2090 - accuracy: 0.9036
Epoch 124/200
3/3 [==============================] - 0s 368us/step - loss: 0.5079 - accuracy: 0.7786
Epoch 125/200
3/3 [==============================] - 0s 337us/step - loss: 0.1900 - accuracy: 0.9393
Epoch 126/200
3/3 [==============================] - 0s 339us/step - loss: 0.2047 - accuracy: 0.9643
Epoch 127/200
3/3 [==============================] - 0s 479us/step - loss: 0.3705 - accuracy: 0.7679
Epoch 128/200
3/3 [==============================] - 0s 390us/step - loss: 0.1850 - accuracy: 0.9036
Epoch 129/200
3/3 [==============================] - 0s 642us/step - loss: 0.1594 - accuracy: 0.9393
Epoch 130/200
3/3 [==============================] - 0s 373us/step - loss: 0.2010 - accuracy: 0.8893
Epoch 131/200
3/3 [==============================] - 0s 369us/step - loss: 0.0849 - accuracy: 1.0000
Epoch 132/200
3/3 [==============================] - 0s 349us/step - loss: 0.1145 - accuracy: 1.0000
Epoch 133/200
3/3 [==============================] - 0s 360us/step - loss: 0.1796 - accuracy: 1.0000
Epoch 134/200
3/3 [==============================] - 0s 371us/step - loss: 0.2363 - accuracy: 0.8536
Epoch 135/200
3/3 [==============================] - 0s 386us/step - loss: 0.1922 - accuracy: 0.9393
Epoch 136/200
3/3 [==============================] - 0s 369us/step - loss: 0.3595 - accuracy: 0.7679
Epoch 137/200
3/3 [==============================] - 0s 369us/step - loss: 0.1506 - accuracy: 0.8893
Epoch 138/200
3/3 [==============================] - 0s 377us/step - loss: 0.2471 - accuracy: 0.8536
Epoch 139/200
3/3 [==============================] - 0s 417us/step - loss: 0.1768 - accuracy: 0.8536
Epoch 140/200
3/3 [==============================] - 0s 400us/step - loss: 0.2112 - accuracy: 0.9393
Epoch 141/200
3/3 [==============================] - 0s 377us/step - loss: 0.3652 - accuracy: 0.7179
Epoch 142/200
3/3 [==============================] - 0s 364us/step - loss: 0.3007 - accuracy: 0.8429
Epoch 143/200
3/3 [==============================] - 0s 361us/step - loss: 0.0518 - accuracy: 1.0000
Epoch 144/200
3/3 [==============================] - 0s 373us/step - loss: 0.2144 - accuracy: 0.8286
Epoch 145/200
3/3 [==============================] - 0s 353us/step - loss: 0.0888 - accuracy: 1.0000
Epoch 146/200
3/3 [==============================] - 0s 361us/step - loss: 0.1267 - accuracy: 1.0000
Epoch 147/200
3/3 [==============================] - 0s 341us/step - loss: 0.0321 - accuracy: 1.0000
Epoch 148/200
3/3 [==============================] - 0s 358us/step - loss: 0.0860 - accuracy: 1.0000
Epoch 149/200
3/3 [==============================] - 0s 375us/step - loss: 0.2151 - accuracy: 0.8893
Epoch 150/200
3/3 [==============================] - 0s 351us/step - loss: 0.1592 - accuracy: 1.0000
Epoch 151/200
3/3 [==============================] - 0s 531us/step - loss: 0.1450 - accuracy: 0.8786
Epoch 152/200
3/3 [==============================] - 0s 392us/step - loss: 0.1813 - accuracy: 0.9036
Epoch 153/200
3/3 [==============================] - 0s 404us/step - loss: 0.1197 - accuracy: 1.0000
Epoch 154/200
3/3 [==============================] - 0s 367us/step - loss: 0.0930 - accuracy: 1.0000
Epoch 155/200
3/3 [==============================] - 0s 580us/step - loss: 0.2587 - accuracy: 0.8893
Epoch 156/200
3/3 [==============================] - 0s 383us/step - loss: 0.0742 - accuracy: 1.0000
Epoch 157/200
3/3 [==============================] - 0s 353us/step - loss: 0.1197 - accuracy: 0.9643
Epoch 158/200
3/3 [==============================] - 0s 371us/step - loss: 0.1716 - accuracy: 0.8536
Epoch 159/200
3/3 [==============================] - 0s 337us/step - loss: 0.1300 - accuracy: 0.9643
Epoch 160/200
3/3 [==============================] - 0s 347us/step - loss: 0.1439 - accuracy: 0.9393
Epoch 161/200
3/3 [==============================] - 0s 366us/step - loss: 0.2597 - accuracy: 0.9393
Epoch 162/200
3/3 [==============================] - 0s 345us/step - loss: 0.1605 - accuracy: 0.8893
Epoch 163/200
3/3 [==============================] - 0s 468us/step - loss: 0.0437 - accuracy: 1.0000
Epoch 164/200
3/3 [==============================] - 0s 372us/step - loss: 0.0376 - accuracy: 1.0000
Epoch 165/200
3/3 [==============================] - 0s 391us/step - loss: 0.0474 - accuracy: 1.0000
Epoch 166/200
3/3 [==============================] - 0s 378us/step - loss: 0.3225 - accuracy: 0.7786
Epoch 167/200
3/3 [==============================] - 0s 368us/step - loss: 0.0770 - accuracy: 1.0000
Epoch 168/200
3/3 [==============================] - 0s 367us/step - loss: 0.5629 - accuracy: 0.7786
Epoch 169/200
3/3 [==============================] - 0s 359us/step - loss: 0.0177 - accuracy: 1.0000
Epoch 170/200
3/3 [==============================] - 0s 370us/step - loss: 0.1167 - accuracy: 1.0000
Epoch 171/200
3/3 [==============================] - 0s 349us/step - loss: 0.1313 - accuracy: 1.0000
Epoch 172/200
3/3 [==============================] - 0s 337us/step - loss: 0.0852 - accuracy: 0.9393
Epoch 173/200
3/3 [==============================] - 0s 375us/step - loss: 0.0545 - accuracy: 1.0000
Epoch 174/200
3/3 [==============================] - 0s 354us/step - loss: 0.0674 - accuracy: 0.9643
Epoch 175/200
3/3 [==============================] - 0s 355us/step - loss: 0.0911 - accuracy: 1.0000
Epoch 176/200
3/3 [==============================] - 0s 404us/step - loss: 0.0980 - accuracy: 0.9393
Epoch 177/200
3/3 [==============================] - 0s 396us/step - loss: 0.0465 - accuracy: 1.0000
Epoch 178/200
3/3 [==============================] - 0s 403us/step - loss: 0.1117 - accuracy: 0.9393
Epoch 179/200
3/3 [==============================] - 0s 373us/step - loss: 0.0415 - accuracy: 1.0000
Epoch 180/200
3/3 [==============================] - 0s 369us/step - loss: 0.0825 - accuracy: 1.0000
Epoch 181/200
3/3 [==============================] - 0s 425us/step - loss: 0.0378 - accuracy: 1.0000
Epoch 182/200
3/3 [==============================] - 0s 381us/step - loss: 0.1155 - accuracy: 0.9393
Epoch 183/200
3/3 [==============================] - 0s 354us/step - loss: 0.0207 - accuracy: 1.0000
Epoch 184/200
3/3 [==============================] - 0s 346us/step - loss: 0.0344 - accuracy: 1.0000
Epoch 185/200
3/3 [==============================] - 0s 379us/step - loss: 0.0984 - accuracy: 0.9393
Epoch 186/200
3/3 [==============================] - 0s 360us/step - loss: 0.1508 - accuracy: 0.8536
Epoch 187/200
3/3 [==============================] - 0s 361us/step - loss: 0.0463 - accuracy: 1.0000
Epoch 188/200
3/3 [==============================] - 0s 358us/step - loss: 0.0476 - accuracy: 0.9643
Epoch 189/200
3/3 [==============================] - 0s 379us/step - loss: 0.1592 - accuracy: 1.0000
Epoch 190/200
3/3 [==============================] - 0s 387us/step - loss: 0.0071 - accuracy: 1.0000
Epoch 191/200
3/3 [==============================] - 0s 405us/step - loss: 0.0527 - accuracy: 1.0000
Epoch 192/200
3/3 [==============================] - 0s 401us/step - loss: 0.0874 - accuracy: 0.9393
Epoch 193/200
3/3 [==============================] - 0s 355us/step - loss: 0.0199 - accuracy: 1.0000
Epoch 194/200
3/3 [==============================] - 0s 373us/step - loss: 0.1299 - accuracy: 0.9643
Epoch 195/200
3/3 [==============================] - 0s 360us/step - loss: 0.0929 - accuracy: 1.0000
Epoch 196/200
3/3 [==============================] - 0s 380us/step - loss: 0.0265 - accuracy: 1.0000
Epoch 197/200
3/3 [==============================] - 0s 358us/step - loss: 0.0843 - accuracy: 1.0000
Epoch 198/200
3/3 [==============================] - 0s 354us/step - loss: 0.0925 - accuracy: 1.0000
Epoch 199/200
3/3 [==============================] - 0s 327us/step - loss: 0.0770 - accuracy: 1.0000
Epoch 200/200
3/3 [==============================] - 0s 561us/step - loss: 0.0311 - accuracy: 1.0000
found in bag: 你好
[0 0 1 0 0 0 0 0 0 0 0 0 0 0]
found in bag: 请问
[('打招呼', '0.998965')]霎时执行结束，秒杀 intel 芯片的 mac，怎一个香字了得！
接下来，尝试装置另外一个在业界名声煊赫的深度学习框架 Pytorch！
因为以后 arm64 架构的只反对 3.9 版本，所以咱们来创立一个虚拟空间：
sudo conda create -n pytorch numpy matplotlib pandas python=3.9这里提前将须要的根底库都一一装置，因为如果不在创立虚拟空间时提前装置，之后应用 pip 是装置不上的，装置胜利后，激活环境：
(pytorch) ➜ conda activate pytorch
(pytorch) ➜随后下载 arm64 版本的 pytorch 安装包：https://github.com/wizyoung/A…\_11\_0\_arm64.whl
下载胜利后，执行装置命令：
sudo pip install torch-1.8.0a0-cp39-cp39-macosx_11_0_arm64.whl让咱们来试试 Pytorch 在 M1 芯片加持后的性能，编写 test\_torch.py:
from tqdm import tqdm
import torch
@torch.jit.script
def foo():
x = torch.ones((1024 * 12, 1024 * 12), dtype=torch.float32)
y = torch.ones((1024 * 12, 1024 * 12), dtype=torch.float32)
z = x + y
return z
if __name__ == '__main__':
z0 = None
for _ in tqdm(range(10000000000)):
zz = foo()
if z0 is None:
z0 = zz
else:
z0 += zz后果仍然秒杀 Intel mac，达到了 320 it/s，你敢信？
最初，有没有 arm64 架构的编辑器呢？答案是有的，vscode 值得领有，下载地址：https://code.visualstudio.com… 肯定要抉择 arm64 版的：
解压后间接运行即可，能够在插件商店抉择 Python 和 Code Runner，即可开启 M1 的 Python 代码编写之旅。
结语：M1 芯片的 Mac 和 Python3，几乎就是金风玉露，绝配天成。只有撩开 M1 和开发者们之间的那一层帷幔，期待咱们的，就是纵享丝滑的开发感触，还等什么？犹豫只会败北，是时候焚烧灵魂，献出钱包了。
原文转载自「刘悦的技术博客」https://v3u.cn/a_id_189