We have compiled 27 Python artificial intelligence libraries. It is recommended to collect them!

1. Numpy

NumPy (Numerical Python) is Python An extended program library that supports a large number of dimensional array and matrix operations. In addition, it also provides a large number of mathematical function libraries for array operations. The bottom layer of Numpy is written in C language. Objects are directly stored in the array instead of object pointers, so its operation efficiency is much higher. higher than pure Python code.

We can compare the speed of calculating the sin value of a list between pure Python and using the Numpy library in the example:

import numpy as np
import math
import random
import time
start = time.time()
for i in range(10):
 list_1 = list(range(1,10000))
 for j in range(len(list_1)):
 list_1[j] = math.sin(list_1[j])
print("使用纯Python用时{}s".format(time.time()-start))
start = time.time()
for i in range(10):
 list_1 = np.array(np.arange(1,10000))
 list_1 = np.sin(list_1)
print("使用Numpy用时{}s".format(time.time()-start))

From the following running results, you can see that using the Numpy library Faster than code written in pure Python:

Using pure Python takes 0.017444372177124023s

Using Numpy takes 0.001619577407836914s

2, OpenCV

OpenCV is a cross-platform computer vision library that can run on Linux, Windows and Mac OS operating systems. It is lightweight and efficient - it consists of a series of C functions and a small number of C classes. It also provides a Python interface and implements many common algorithms in image processing and computer vision.

The following code attempts to use some simple filters, including image smoothing, Gaussian blur, etc.:

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
img = cv.imread('h89817032p0.png')
kernel = np.ones((5,5),np.float32)/25
dst = cv.filter2D(img,-1,kernel)
blur_1 = cv.GaussianBlur(img,(5,5),0)
blur_2 = cv.bilateralFilter(img,9,75,75)
plt.figure(figsize=(10,10))
plt.subplot(221),plt.imshow(img[:,:,::-1]),plt.title('Original')
plt.xticks([]), plt.yticks([])
plt.subplot(222),plt.imshow(dst[:,:,::-1]),plt.title('Averaging')
plt.xticks([]), plt.yticks([])
plt.subplot(223),plt.imshow(blur_1[:,:,::-1]),plt.title('Gaussian')
plt.xticks([]), plt.yticks([])
plt.subplot(224),plt.imshow(blur_1[:,:,::-1]),plt.title('Bilateral')
plt.xticks([]), plt.yticks([])
plt.show()

OpenCV

3. Scikit-image

scikit-image is an image processing library based on scipy, which processes images as numpy arrays.

For example, you can use scikit-image to change the image ratio. scikit-image provides functions such as rescale, resize, and downscale_local_mean.

from skimage import data, color, io
from skimage.transform import rescale, resize, downscale_local_mean
image = color.rgb2gray(io.imread('h89817032p0.png'))
image_rescaled = rescale(image, 0.25, anti_aliasing=False)
image_resized = resize(image, (image.shape[0] // 4, image.shape[1] // 4),
anti_aliasing=True)
image_downscaled = downscale_local_mean(image, (4, 3))
plt.figure(figsize=(20,20))
plt.subplot(221),plt.imshow(image, cmap='gray'),plt.title('Original')
plt.xticks([]), plt.yticks([])
plt.subplot(222),plt.imshow(image_rescaled, cmap='gray'),plt.title('Rescaled')
plt.xticks([]), plt.yticks([])
plt.subplot(223),plt.imshow(image_resized, cmap='gray'),plt.title('Resized')
plt.xticks([]), plt.yticks([])
plt.subplot(224),plt.imshow(image_downscaled, cmap='gray'),plt.title('Downscaled')
plt.xticks([]), plt.yticks([])
plt.show()

Scikit-image

4、PIL

Python Imaging Library (PIL) has become a Python fact Image processing standard library on the Internet, this is because PIL is very powerful, but the API is very simple and easy to use.

However, since PIL only supports Python 2.7 and is in disrepair, a group of volunteers created a compatible version based on PIL, named Pillow, which supports the latest Python 3.x. , and many new features have been added, so we can skip PIL and install and use Pillow directly.

5. Pillow

Use Pillow to generate a letter verification code image:

from PIL import Image, ImageDraw, ImageFont, ImageFilter
import random
# 随机字母:
def rndChar():
 return chr(random.randint(65, 90))
# 随机颜色1:
def rndColor():
 return (random.randint(64, 255), random.randint(64, 255), random.randint(64, 255))
# 随机颜色2:
def rndColor2():
 return (random.randint(32, 127), random.randint(32, 127), random.randint(32, 127))
# 240 x 60:
width = 60 * 6
height = 60 * 6
image = Image.new('RGB', (width, height), (255, 255, 255))
# 创建Font对象:
font = ImageFont.truetype('/usr/share/fonts/wps-office/simhei.ttf', 60)
# 创建Draw对象:
draw = ImageDraw.Draw(image)
# 填充每个像素:
for x in range(width):
 for y in range(height):
 draw.point((x, y), fill=rndColor())
# 输出文字:
for t in range(6):
 draw.text((60 * t + 10, 150), rndChar(), font=font, fill=rndColor2())
# 模糊:
image = image.filter(ImageFilter.BLUR)
image.save('code.jpg', 'jpeg')

##Verification code

6. SimpleCV

SimpleCV is an open source framework for building computer vision applications. Using it, you can access high-performance computer vision libraries like OpenCV without having to first understand terms like bit depth, file format, color space, buffer management, eigenvalues, or matrices. However, its support for Python3 is very poor. Using the following code in Python3.7:

from SimpleCV import Image, Color, Display
# load an image from imgur
img = Image('http://i.imgur.com/lfAeZ4n.png')
# use a keypoint detector to find areas of interest
feats = img.findKeypoints()
# draw the list of keypoints
feats.draw(color=Color.RED)
# show theresulting image.
img.show()
# apply the stuff we found to the image.
output = img.applyLayers()
# save the results.
output.save('juniperfeats.png')

will report the following error, so it is not recommended to use it in Python3:

SyntaxError: Missing parentheses in call to 'print'. Did you mean print('unit test')?

7. Mahotas

Mahotas is a fast computer vision algorithm library built on Numpy. It currently has more than 100 image processing and computer vision functions and is constantly growing.

Use Mahotas to load the image and operate on the pixels:

import numpy as np
import mahotas
import mahotas.demos
from mahotas.thresholding import soft_threshold
from matplotlib import pyplot as plt
from os import path
f = mahotas.demos.load('lena', as_grey=True)
f = f[128:,128:]
plt.gray()
# Show the data:
print("Fraction of zeros in original image: {0}".format(np.mean(f==0)))
plt.imshow(f)
plt.show()

Mahotas

8, Ilastik

Ilastik can provide users with good machine learning-based bioinformation image analysis services, using machine learning algorithms to easily segment, classify, track and count cells or other experimental data. Most operations are interactive and do not require machine learning expertise.

9. Scikit-learn

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN, among many other machine learning algorithms.

Use Scikit-learn to implement the KMeans algorithm:

import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import MiniBatchKMeans, KMeans
from sklearn.metrics.pairwise import pairwise_distances_argmin
from sklearn.datasets import make_blobs
# Generate sample data
np.random.seed(0)
batch_size = 45
centers = [[1, 1], [-1, -1], [1, -1]]
n_clusters = len(centers)
X, labels_true = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7)
# Compute clustering with Means
k_means = KMeans(init='k-means++', n_clusters=3, n_init=10)
t0 = time.time()
k_means.fit(X)
t_batch = time.time() - t0
# Compute clustering with MiniBatchKMeans
mbk = MiniBatchKMeans(init='k-means++', n_clusters=3, batch_size=batch_size,
 n_init=10, max_no_improvement=10, verbose=0)
t0 = time.time()
mbk.fit(X)
t_mini_batch = time.time() - t0
# Plot result
fig = plt.figure(figsize=(8, 3))
fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9)
colors = ['#4EACC5', '#FF9C34', '#4E9A06']
# We want to have the same colors for the same cluster from the
# MiniBatchKMeans and the KMeans algorithm. Let's pair the cluster centers per
# closest one.
k_means_cluster_centers = k_means.cluster_centers_
order = pairwise_distances_argmin(k_means.cluster_centers_,
 mbk.cluster_centers_)
mbk_means_cluster_centers = mbk.cluster_centers_[order]
k_means_labels = pairwise_distances_argmin(X, k_means_cluster_centers)
mbk_means_labels = pairwise_distances_argmin(X, mbk_means_cluster_centers)
# KMeans
for k, col in zip(range(n_clusters), colors):
 my_members = k_means_labels == k
 cluster_center = k_means_cluster_centers[k]
 plt.plot(X[my_members, 0], X[my_members, 1], 'w',
 markerfacecolor=col, marker='.')
 plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
 markeredgecolor='k', markersize=6)
plt.title('KMeans')
plt.xticks(())
plt.yticks(())
plt.show()

KMeans

10、SciPy

SciPy 库提供了许多用户友好和高效的数值计算，如数值积分、插值、优化、线性代数等。

SciPy 库定义了许多数学物理的特殊函数，包括椭圆函数、贝塞尔函数、伽马函数、贝塔函数、超几何函数、抛物线圆柱函数等等。

from scipy import special
import matplotlib.pyplot as plt
import numpy as np
def drumhead_height(n, k, distance, angle, t):
 kth_zero = special.jn_zeros(n, k)[-1]
 return np.cos(t) * np.cos(n*angle) * special.jn(n, distance*kth_zero)
theta = np.r_[0:2*np.pi:50j]
radius = np.r_[0:1:50j]
x = np.array([r * np.cos(theta) for r in radius])
y = np.array([r * np.sin(theta) for r in radius])
z = np.array([drumhead_height(1, 1, r, theta, 0.5) for r in radius])
fig = plt.figure()
ax = fig.add_axes(rect=(0, 0.05, 0.95, 0.95), projection='3d')
ax.plot_surface(x, y, z, rstride=1, cstride=1, cmap='RdBu_r', vmin=-0.5, vmax=0.5)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_xticks(np.arange(-1, 1.1, 0.5))
ax.set_yticks(np.arange(-1, 1.1, 0.5))
ax.set_zlabel('Z')
plt.show()

SciPy

11、NLTK

NLTK 是构建Python程序以处理自然语言的库。它为50多个语料库和词汇资源(如 WordNet )提供了易于使用的接口，以及一套用于分类、分词、词干、标记、解析和语义推理的文本处理库、工业级自然语言处理 (Natural Language Processing, NLP) 库的包装器。

NLTK被称为 “a wonderful tool for teaching, and working in, computational linguistics using Python”。

import nltk
from nltk.corpus import treebank
# 首次使用需要下载
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('maxent_ne_chunker')
nltk.download('words')
nltk.download('treebank')
sentence = """At eight o'clock on Thursday morning Arthur didn't feel very good."""
# Tokenize
tokens = nltk.word_tokenize(sentence)
tagged = nltk.pos_tag(tokens)
# Identify named entities
entities = nltk.chunk.ne_chunk(tagged)
# Display a parse tree
t = treebank.parsed_sents('wsj_0001.mrg')[0]
t.draw()

NLTK

12、spaCy

spaCy 是一个免费的开源库，用于 Python 中的高级 NLP。它可以用于构建处理大量文本的应用程序；也可以用来构建信息提取或自然语言理解系统，或者对文本进行预处理以进行深度学习。

import spacy
 texts = [
 "Net income was $9.4 million compared to the prior year of $2.7 million.",
 "Revenue exceeded twelve billion dollars, with a loss of $1b.",
 ]
 nlp = spacy.load("en_core_web_sm")
 for doc in nlp.pipe(texts, disable=["tok2vec", "tagger", "parser", "attribute_ruler", "lemmatizer"]):
 # Do something with the doc here
 print([(ent.text, ent.label_) for ent in doc.ents])

nlp.pipe 生成 Doc 对象，因此我们可以对它们进行迭代并访问命名实体预测：

[('$9.4 million', 'MONEY'), ('the prior year', 'DATE'), ('$2.7 million', 'MONEY')]
[('twelve billion dollars', 'MONEY'), ('1b', 'MONEY')]

13、LibROSA

librosa 是一个用于音乐和音频分析的 Python 库，它提供了创建音乐信息检索系统所必需的功能和函数。

# Beat tracking example
import librosa
# 1. Get the file path to an included audio example
filename = librosa.example('nutcracker')
# 2. Load the audio as a waveform `y`
#Store the sampling rate as `sr`
y, sr = librosa.load(filename)
# 3. Run the default beat tracker
tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
print('Estimated tempo: {:.2f} beats per minute'.format(tempo))
# 4. Convert the frame indices of beat events into timestamps
beat_times = librosa.frames_to_time(beat_frames, sr=sr)

14、Pandas

Pandas 是一个快速、强大、灵活且易于使用的开源数据分析和操作工具， Pandas 可以从各种文件格式比如 CSV、JSON、SQL、Microsoft Excel 导入数据，可以对各种数据进行运算操作，比如归并、再成形、选择，还有数据清洗和数据加工特征。Pandas 广泛应用在学术、金融、统计学等各个数据分析领域。

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
ts = ts.cumsum()
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list("ABCD"))
df = df.cumsum()
df.plot()
plt.show()

Pandas

15、Matplotlib

Matplotlib 是Python的绘图库，它提供了一整套和 matlab 相似的命令 API，可以生成出版质量级别的精美图形，Matplotlib 使绘图变得非常简单，在易用性和性能间取得了优异的平衡。

使用 Matplotlib 绘制多曲线图：

# plot_multi_curve.py
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0.1, 2 * np.pi, 100)
y_1 = x
y_2 = np.square(x)
y_3 = np.log(x)
y_4 = np.sin(x)
plt.plot(x,y_1)
plt.plot(x,y_2)
plt.plot(x,y_3)
plt.plot(x,y_4)
plt.show()

Matplotlib

16、Seaborn

Seaborn 是在 Matplotlib 的基础上进行了更高级的API封装的Python数据可视化库，从而使得作图更加容易，应该把 Seaborn 视为 Matplotlib 的补充，而不是替代物。

import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="ticks")
df = sns.load_dataset("penguins")
sns.pairplot(df, hue="species")
plt.show()

seaborn

17、Orange

Orange 是一个开源的数据挖掘和机器学习软件，提供了一系列的数据探索、可视化、预处理以及建模组件。Orange 拥有漂亮直观的交互式用户界面，非常适合新手进行探索性数据分析和可视化展示；同时高级用户也可以将其作为 Python 的一个编程模块进行数据操作和组件开发。

使用 pip 即可安装 Orange，好评～

$ pip install orange3

安装完成后，在命令行输入 orange-canvas 命令即可启动 Orange 图形界面：

$ orange-canvas

启动完成后，即可看到 Orange 图形界面，进行各种操作。

Orange

18、PyBrain

PyBrain 是 Python 的模块化机器学习库。它的目标是为机器学习任务和各种预定义的环境提供灵活、易于使用且强大的算法来测试和比较算法。PyBrain 是 Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library 的缩写。

我们将利用一个简单的例子来展示 PyBrain 的用法，构建一个多层感知器 (Multi Layer Perceptron, MLP)。

首先，我们创建一个新的前馈网络对象：

from pybrain.structure import FeedForwardNetwork
n = FeedForwardNetwork()

接下来，构建输入、隐藏和输出层：

from pybrain.structure import LinearLayer, SigmoidLayer
inLayer = LinearLayer(2)
hiddenLayer = SigmoidLayer(3)
outLayer = LinearLayer(1)

为了使用所构建的层，必须将它们添加到网络中：

n.addInputModule(inLayer)
n.addModule(hiddenLayer)
n.addOutputModule(outLayer)

可以添加多个输入和输出模块。为了向前计算和反向误差传播，网络必须知道哪些层是输入、哪些层是输出。

这就需要明确确定它们应该如何连接。为此，我们使用最常见的连接类型，全连接层，由 FullConnection 类实现：

from pybrain.structure import FullConnection
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)

与层一样，我们必须明确地将它们添加到网络中：

n.addConnection(in_to_hidden)
n.addConnection(hidden_to_out)

所有元素现在都已准备就位，最后，我们需要调用.sortModules()方法使MLP可用：

n.sortModules()

这个调用会执行一些内部初始化，这在使用网络之前是必要的。

19、Milk

MILK(MACHINE LEARNING TOOLKIT) 是 Python 语言的机器学习工具包。它主要是包含许多分类器比如 SVMS、K-NN、随机森林以及决策树中使用监督分类法，它还可执行特征选择，可以形成不同的例如无监督学习、密切关系传播和由 MILK 支持的 K-means 聚类等分类系统。

使用 MILK 训练一个分类器：

import numpy as np
import milk
features = np.random.rand(100,10)
labels = np.zeros(100)
features[50:] += .5
labels[50:] = 1
learner = milk.defaultclassifier()
model = learner.train(features, labels)
# Now you can use the model on new examples:
example = np.random.rand(10)
print(model.apply(example))
example2 = np.random.rand(10)
example2 += .5
print(model.apply(example2))

20、TensorFlow

TensorFlow 是一个端到端开源机器学习平台。它拥有一个全面而灵活的生态系统，一般可以将其分为 TensorFlow1.x 和 TensorFlow2.x，TensorFlow1.x 与 TensorFlow2.x 的主要区别在于 TF1.x 使用静态图而 TF2.x 使用Eager Mode动态图。

这里主要使用TensorFlow2.x作为示例，展示在 TensorFlow2.x 中构建卷积神经网络 (Convolutional Neural Network, CNN)。

import tensorflow as tf
from tensorflow.keras import datasets, layers, models
# 数据加载
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
# 数据预处理
train_images, test_images = train_images / 255.0, test_images / 255.0
# 模型构建
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
# 模型编译与训练
model.compile(optimizer='adam',
 loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
 metrics=['accuracy'])
history = model.fit(train_images, train_labels, epochs=10,
 validation_data=(test_images, test_labels))

21、PyTorch

PyTorch 的前身是 Torch，其底层和 Torch 框架一样，但是使用 Python 重新写了很多内容，不仅更加灵活，支持动态图，而且提供了 Python 接口。

# 导入库
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda, Compose
import matplotlib.pyplot as plt
# 模型构建
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))
# Define model
class NeuralNetwork(nn.Module):
 def __init__(self):
 super(NeuralNetwork, self).__init__()
 self.flatten = nn.Flatten()
 self.linear_relu_stack = nn.Sequential(
 nn.Linear(28*28, 512),
 nn.ReLU(),
 nn.Linear(512, 512),
 nn.ReLU(),
 nn.Linear(512, 10),
 nn.ReLU()
 )
 def forward(self, x):
 x = self.flatten(x)
 logits = self.linear_relu_stack(x)
 return logits
model = NeuralNetwork().to(device)
# 损失函数和优化器
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
# 模型训练
def train(dataloader, model, loss_fn, optimizer):
 size = len(dataloader.dataset)
 for batch, (X, y) in enumerate(dataloader):
 X, y = X.to(device), y.to(device)
 # Compute prediction error
 pred = model(X)
 loss = loss_fn(pred, y)
 # Backpropagation
 optimizer.zero_grad()
 loss.backward()
 optimizer.step()
 if batch % 100 == 0:
 loss, current = loss.item(), batch * len(X)
 print(f"loss: {loss:>7f}[{current:>5d}/{size:>5d}]")

22、Theano

Theano 是一个 Python 库，它允许定义、优化和有效地计算涉及多维数组的数学表达式，建在 NumPy 之上。

在 Theano 中实现计算雅可比矩阵：

import theano
import theano.tensor as T
x = T.dvector('x')
y = x ** 2
J, updates = theano.scan(lambda i, y,x : T.grad(y[i], x), sequences=T.arange(y.shape[0]), non_sequences=[y,x])
f = theano.function([x], J, updates=updates)
f([4, 4])

23、Keras

Keras 是一个用 Python 编写的高级神经网络 API，它能够以 TensorFlow, CNTK, 或者 Theano 作为后端运行。Keras 的开发重点是支持快速的实验，能够以最小的时延把想法转换为实验结果。

from keras.models import Sequential
from keras.layers import Dense
# 模型构建
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))
# 模型编译与训练
model.compile(loss='categorical_crossentropy',
 optimizer='sgd',
 metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5, batch_size=32)

24、Caffe

在 Caffe2 官方网站上，这样说道：Caffe2 现在是 PyTorch 的一部分。虽然这些 api 将继续工作，但鼓励使用 PyTorch api。

25、MXNet

MXNet 是一款设计为效率和灵活性的深度学习框架。它允许混合符号编程和命令式编程，从而最大限度提高效率和生产力。

使用 MXNet 构建手写数字识别模型：

import mxnet as mx
from mxnet import gluon
from mxnet.gluon import nn
from mxnet import autograd as ag
import mxnet.ndarray as F
# 数据加载
mnist = mx.test_utils.get_mnist()
batch_size = 100
train_data = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)
val_data = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
# CNN模型
class Net(gluon.Block):
 def __init__(self, **kwargs):
 super(Net, self).__init__(**kwargs)
 self.conv1 = nn.Conv2D(20, kernel_size=(5,5))
 self.pool1 = nn.MaxPool2D(pool_size=(2,2), strides = (2,2))
 self.conv2 = nn.Conv2D(50, kernel_size=(5,5))
 self.pool2 = nn.MaxPool2D(pool_size=(2,2), strides = (2,2))
 self.fc1 = nn.Dense(500)
 self.fc2 = nn.Dense(10)
 def forward(self, x):
 x = self.pool1(F.tanh(self.conv1(x)))
 x = self.pool2(F.tanh(self.conv2(x)))
 # 0 means copy over size from corresponding dimension.
 # -1 means infer size from the rest of dimensions.
 x = x.reshape((0, -1))
 x = F.tanh(self.fc1(x))
 x = F.tanh(self.fc2(x))
 return x
net = Net()
# 初始化与优化器定义
# set the context on GPU is available otherwise CPU
ctx = [mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()]
net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.03})
# 模型训练
# Use Accuracy as the evaluation metric.
metric = mx.metric.Accuracy()
softmax_cross_entropy_loss = gluon.loss.SoftmaxCrossEntropyLoss()
for i in range(epoch):
 # Reset the train data iterator.
 train_data.reset()
 for batch in train_data:
 data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
 label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
 outputs = []
 # Inside training scope
 with ag.record():
 for x, y in zip(data, label):
 z = net(x)
 # Computes softmax cross entropy loss.
 loss = softmax_cross_entropy_loss(z, y)
 # Backpropogate the error for one iteration.
 loss.backward()
 outputs.append(z)
 metric.update(label, outputs)
 trainer.step(batch.data[0].shape[0])
 # Gets the evaluation result.
 name, acc = metric.get()
 # Reset evaluation result to initial state.
 metric.reset()
 print('training acc at epoch %d: %s=%f'%(i, name, acc))

26、PaddlePaddle

飞桨 (PaddlePaddle) 以百度多年的深度学习技术研究和业务应用为基础，集深度学习核心训练和推理框架、基础模型库、端到端开发套件、丰富的工具组件于一体。是中国首个自主研发、功能完备、开源开放的产业级深度学习平台。

使用 PaddlePaddle 实现 LeNtet5：

# 导入需要的包
import paddle
import numpy as np
from paddle.nn import Conv2D, MaxPool2D, Linear
## 组网
import paddle.nn.functional as F
# 定义 LeNet 网络结构
class LeNet(paddle.nn.Layer):
 def __init__(self, num_classes=1):
 super(LeNet, self).__init__()
 # 创建卷积和池化层
 # 创建第1个卷积层
 self.conv1 = Conv2D(in_channels=1, out_channels=6, kernel_size=5)
 self.max_pool1 = MaxPool2D(kernel_size=2, stride=2)
 # 尺寸的逻辑：池化层未改变通道数；当前通道数为6
 # 创建第2个卷积层
 self.conv2 = Conv2D(in_channels=6, out_channels=16, kernel_size=5)
 self.max_pool2 = MaxPool2D(kernel_size=2, stride=2)
 # 创建第3个卷积层
 self.conv3 = Conv2D(in_channels=16, out_channels=120, kernel_size=4)
 # 尺寸的逻辑：输入层将数据拉平[B,C,H,W] -> [B,C*H*W]
 # 输入size是[28,28]，经过三次卷积和两次池化之后，C*H*W等于120
 self.fc1 = Linear(in_features=120, out_features=64)
 # 创建全连接层，第一个全连接层的输出神经元个数为64， 第二个全连接层输出神经元个数为分类标签的类别数
 self.fc2 = Linear(in_features=64, out_features=num_classes)
 # 网络的前向计算过程
 def forward(self, x):
 x = self.conv1(x)
 # 每个卷积层使用Sigmoid激活函数，后面跟着一个2x2的池化
 x = F.sigmoid(x)
 x = self.max_pool1(x)
 x = F.sigmoid(x)
 x = self.conv2(x)
 x = self.max_pool2(x)
 x = self.conv3(x)
 # 尺寸的逻辑：输入层将数据拉平[B,C,H,W] -> [B,C*H*W]
 x = paddle.reshape(x, [x.shape[0], -1])
 x = self.fc1(x)
 x = F.sigmoid(x)
 x = self.fc2(x)
 return x

27、CNTK

CNTK(Cognitive Toolkit) 是一个深度学习工具包，通过有向图将神经网络描述为一系列计算步骤。在这个有向图中，叶节点表示输入值或网络参数，而其他节点表示对其输入的矩阵运算。CNTK 可以轻松地实现和组合流行的模型类型，如 CNN 等。

CNTK 用网络描述语言 (network description language, NDL) 描述一个神经网络。简单的说，要描述输入的 feature，输入的 label，一些参数，参数和输入之间的计算关系，以及目标节点是什么。

NDLNetworkBuilder=[
 run=ndlLR
 ndlLR=[
 # sample and label dimensions
 SDim=$dimension$
 LDim=1
 features=Input(SDim, 1)
 labels=Input(LDim, 1)
 # parameters to learn
B0 = Parameter(4)
 W0 = Parameter(4, SDim)
 B = Parameter(LDim)
 W = Parameter(LDim, 4)
 # operations
 t0 = Times(W0, features)
 z0 = Plus(t0, B0)
s0 = Sigmoid(z0)
 t = Times(W, s0)
 z = Plus(t, B)
 s = Sigmoid(z)
 LR = Logistic(labels, s)
 EP = SquareError(labels, s)
 # root nodes
 FeatureNodes=(features)
 LabelNodes=(labels)
 CriteriaNodes=(LR)
 EvalNodes=(EP)
 OutputNodes=(s,t,z,s0,W0)
]

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