While "googl'ing" and doing some research I were not able to find <strong>any</strong> serious/popular framework/sdk for scientific GPGPU-Computing and <em>OpenCL</em> on <em>AMD</em> hardware. Is there any literature and/or software I missed?

While "googl'ing" and doing some research I were not able to find **any** serious/popular framework/sdk for scientific GPGPU-Computing and *OpenCL* on *AMD* hardware. Is there any literature and/or software I missed?

Especially I am interested in **deep learning**.

For all I know *deeplearning.net* recommends *NVIDIA* hardware and *CUDA* frameworks. Additionally all big deep learning frameworks I know, such as *Caffe*, *Theano*, *Torch*, *DL4J*, ... are focussed on *CUDA* and do not plan to support *OpenCL/AMD*.

Furthermore one can find plenty of scientific papers as well as corresponding literature for *CUDA*based deep learning tasks but nearly nothing for *OpenCL/AMD* based solutions.

Is there any chance that new or existing scientific frameworks will show up for *OpenCL/AMD* based solutions in 2015/16?

What is a good start for *deep learning* with *OpenCL/AMD*? Any literature? Tutorials? Miscellaneous sources?

Python Tutorial for Computer Vision and Face Detection with OpenCV: Learn Vision Includes all OpenCV Image Processing Features with Simple Examples. Face Detection, Face Recognition

Computer Vision is an AI based, that is, Artificial Intelligence based technology that allows computers to understand and label images. Its now used in Convenience stores, Driver-less Car Testing, Security Access Mechanisms, Policing and Investigations Surveillance, Daily Medical Diagnosis monitoring health of crops and live stock and so on and so forth..

A common example will be face detection and unlocking mechanism that you use in your mobile phone. We use that daily. That is also a big application of Computer Vision. And today, top technology companies like Amazon, Google, Microsoft, Facebook etc are investing millions and millions of Dollars into Computer Vision based research and product development.

Computer vision allows us to analyze and leverage image and video data, with applications in a variety of industries, including self-driving cars, social network apps, medical diagnostics, and many more.

As the fastest growing language in popularity, Python is well suited to leverage the power of existing computer vision libraries to learn from all this image and video data.

In this course we'll teach you everything you need to know to become an expert in computer vision! This $20 billion dollar industry will be one of the most important job markets in the years to come.

**What you'll learn**

- Use OpenCV to work with image files
- Perform image manipulation with OpenCV, including smoothing, blurring, thresholding, and morphological operations.
- Create Face Detection Software
- Detect Objects, including corner, edge, and grid detection techniques with OpenCV and Python
- Use Python and Deep Learning to build image classifiers
- Use Python and OpenCV to draw shapes on images and videos
- Create Color Histograms with OpenCV
- Study from MIT notes and get Interview questions
- Crack image processing limits by developing Applications.

A Tutorial for PyTorch and Deep Learning Beginners

IntroductionToday deep learning is going viral and is applied to a variety of machine learning problems such as image recognition, speech recognition, machine translation, and others. There is a wide range of highly customizable neural network architectures, which can suit almost any problem when given enough data. Each neural network should be elaborated to suit the given problem well enough. You have to fine tune the hyperparameters of the network (the learning rate, dropout coefficients, weight decay, and many others) as well as the number of hidden layers, and the number of units in each layer. **Choosing the right activation function for each layer is also crucial and may have a significant impact on metric scores and the training speed of the model**.

The activation function is an essential building block for every neural network. We can choose from a huge list of popular activation functions from popular Deep Learning frameworks, like ReLU, Sigmoid, Tanh, and many others.

However, to create a state of the art model, customized particularly for your task, you may need to use a custom activation function, which is absent in Deep Learning framework you are using. Activation functions can be roughly classified into the following groups by complexity:

**Simple activation functions**like SiLU, Inverse Square Root Unit (ISRU). You can quickly implement these functions using any Deep Learning framework.**Activation functions with trainable parameters**like Soft Exponentialactivation or S-shaped Rectified Linear Unit (SReLU).**Activation functions, which are not differentiable at some points**and require the custom implementation of the backward step, for example, Bipolar Rectified Linear Unit (BReLU).

In this tutorial, I cover the implementation and demo examples for all of these types of functions with PyTorch framework. You can find all the code for this article on GitHub.

Setting UpTo go through the examples of the implementation of activation functions, you would require:

- To install PyTorch,
- To add the necessary imports to your script,

```
# Import basic libraries
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from collections import OrderedDict
# Import PyTorch
import torch # import main library
from torch.autograd import Variable
import torch.nn as nn # import modules
from torch.autograd import Function # import Function to create custom activations
from torch.nn.parameter import Parameter # import Parameter to create custom activations with learnable parameters
from torch import optim # import optimizers for demonstrations
import torch.nn.functional as F # import torch functions
from torchvision import datasets, transforms # import transformations to use for demo
```

The necessary imports

- To prepare a dataset for the demonstration. We will use the well known Fashion MNIST dataset.

```
# Define a transform
transform = transforms.Compose([transforms.ToTensor()])
# Download and load the training data for Fashion MNIST
trainset = datasets.FashionMNIST('~/.pytorch/F_MNIST_data/', download=True, train=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
```

Prepare the dataset

The last thing is to set up a sample function, which runs the model training process and prints out the training loss for each epoch:

```
# helper function to train a model
def train_model(model, trainloader):
'''
Function trains the model and prints out the training log.
INPUT:
model - initialized PyTorch model ready for training.
trainloader - PyTorch dataloader for training data.
'''
#setup training
#define loss function
criterion = nn.NLLLoss()
#define learning rate
learning_rate = 0.003
#define number of epochs
epochs = 5
#initialize optimizer
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
#run training and print out the loss to make sure that we are actually fitting to the training set
print('Training the model. Make sure that loss decreases after each epoch.\n')
for e in range(epochs):
running_loss = 0
for images, labels in trainloader:
images = images.view(images.shape[0], -1)
log_ps = model(images)
loss = criterion(log_ps, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
else:
# print out the loss to make sure it is decreasing
print(f"Training loss: {running_loss}")
```

A sample model training function

Now everything is ready for the creation of models with custom activation functions.

Implementing Simple Activation FunctionsThe most simple common activation functions

**are differentiable**and don’t need the manual implementation of the backward step,**don’t have any trainable parameters**. All their parameters should be set in advance.

One of the examples of such simple functions is Sigmoid Linear Unit or just SiLU, also known as Swish-1:

SiLU

Such a simple activation function can be implemented just as easy as a Python function:

```
# simply define a silu function
def silu(input):
'''
Applies the Sigmoid Linear Unit (SiLU) function element-wise:
SiLU(x) = x * sigmoid(x)
'''
return input * torch.sigmoid(input) # use torch.sigmoid to make sure that we created the most efficient implemetation based on builtin PyTorch functions
# create a class wrapper from PyTorch nn.Module, so
# the function now can be easily used in models
class SiLU(nn.Module):
'''
Applies the Sigmoid Linear Unit (SiLU) function element-wise:
SiLU(x) = x * sigmoid(x)
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
References:
- Related paper:
https://arxiv.org/pdf/1606.08415.pdf
Examples:
>>> m = silu()
>>> input = torch.randn(2)
>>> output = m(input)
'''
def __init__(self):
'''
Init method.
'''
super().__init__() # init the base class
def forward(self, input):
'''
Forward pass of the function.
'''
return silu(input) # simply apply already implemented SiLU
```

So now SiLU can be used in models created with nn.Sequential:

```
# use SiLU with model created with Sequential
# initialize activation function
activation_function = SiLU()
# Initialize the model using nn.Sequential
model = nn.Sequential(OrderedDict([
('fc1', nn.Linear(784, 256)),
('activation1', activation_function), # use SiLU
('fc2', nn.Linear(256, 128)),
('bn2', nn.BatchNorm1d(num_features=128)),
('activation2', activation_function), # use SiLU
('dropout', nn.Dropout(0.3)),
('fc3', nn.Linear(128, 64)),
('bn3', nn.BatchNorm1d(num_features=64)),
('activation3', activation_function), # use SiLU
('logits', nn.Linear(64, 10)),
('logsoftmax', nn.LogSoftmax(dim=1))]))
# Run training
train_model(model)
```

Or in a simple model, which extends nn.Module class:

```
# create class for basic fully-connected deep neural network
class ClassifierSiLU(nn.Module):
'''
Demo classifier model class to demonstrate SiLU
'''
def __init__(self):
super().__init__()
# initialize layers
self.fc1 = nn.Linear(784, 256)
self.fc2 = nn.Linear(256, 128)
self.fc3 = nn.Linear(128, 64)
self.fc4 = nn.Linear(64, 10)
def forward(self, x):
# make sure the input tensor is flattened
x = x.view(x.shape[0], -1)
# apply silu function
x = silu(self.fc1(x))
# apply silu function
x = silu(self.fc2(x))
# apply silu function
x = silu(self.fc3(x))
x = F.log_softmax(self.fc4(x), dim=1)
return x
# Create demo model
model = ClassifierSiLU()
# Run training
train_model(model)
```

Implementing Activation Function with Trainable Parameters
There are lots of activation functions with parameters, which can be trained with gradient descent while training the model. A great example for one of these is Soft Exponential function:

Soft Exponential

To implement an activation function with trainable parameters we have to:

- derive a class from nn.Module and make the parameter one of its members,
- wrap the parameter as a PyTorch Parameter and set requiresGrad attribute to True.

Here is an example for Soft Exponential:

```
class soft_exponential(nn.Module):
'''
Implementation of soft exponential activation.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Parameters:
- alpha - trainable parameter
References:
- See related paper:
https://arxiv.org/pdf/1602.01321.pdf
Examples:
>>> a1 = soft_exponential(256)
>>> x = torch.randn(256)
>>> x = a1(x)
'''
def __init__(self, in_features, alpha = None):
'''
Initialization.
INPUT:
- in_features: shape of the input
- aplha: trainable parameter
aplha is initialized with zero value by default
'''
super(soft_exponential,self).__init__()
self.in_features = in_features
# initialize alpha
if alpha == None:
self.alpha = Parameter(torch.tensor(0.0)) # create a tensor out of alpha
else:
self.alpha = Parameter(torch.tensor(alpha)) # create a tensor out of alpha
self.alpha.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
'''
Forward pass of the function.
Applies the function to the input elementwise.
'''
if (self.alpha == 0.0):
return x
if (self.alpha < 0.0):
return - torch.log(1 - self.alpha * (x + self.alpha)) / self.alpha
if (self.alpha > 0.0):
return (torch.exp(self.alpha * x) - 1)/ self.alpha + self.alpha
```

And now we can use Soft Exponential in our models as follows:

```
# create class for basic fully-connected deep neural network
class ClassifierSExp(nn.Module):
'''
Basic fully-connected network to test Soft Exponential activation.
'''
def __init__(self):
super().__init__()
# initialize layers
self.fc1 = nn.Linear(784, 256)
self.fc2 = nn.Linear(256, 128)
self.fc3 = nn.Linear(128, 64)
self.fc4 = nn.Linear(64, 10)
# initialize Soft Exponential activation
self.a1 = soft_exponential(256)
self.a2 = soft_exponential(128)
self.a3 = soft_exponential(64)
def forward(self, x):
# make sure the input tensor is flattened
x = x.view(x.shape[0], -1)
# apply Soft Exponential unit
x = self.a1(self.fc1(x))
x = self.a2(self.fc2(x))
x = self.a3(self.fc3(x))
x = F.log_softmax(self.fc4(x), dim=1)
return x
model = ClassifierSExp()
train_model(model)
```

Implementing Activation Function with Custom Backward Step
The perfect example of an activation function, which needs implementation of a custom backward step is BReLU (Bipolar Rectified Linear Unit):

*BReLU*

This function is not differentiable at 0, so automatic gradient computation might fail. That’s why we should provide a custom backward step to ensure stable computation.

To impement custom activation function with backward step we should:

- create a class which, inherits Function from torch.autograd,
- override static forward and backward methods. Forward method just applies the function to the input. Backward method computes the gradient of the loss function with respect to the input given the gradient of the loss function with respect to the output.

Let’s see an example for *BReLU*:

```
class brelu(Function):
'''
Implementation of BReLU activation function.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
References:
- See BReLU paper:
https://arxiv.org/pdf/1709.04054.pdf
Examples:
>>> brelu_activation = brelu.apply
>>> t = torch.randn((5,5), dtype=torch.float, requires_grad = True)
>>> t = brelu_activation(t)
'''
#both forward and backward are @staticmethods
@staticmethod
def forward(ctx, input):
"""
In the forward pass we receive a Tensor containing the input and return
a Tensor containing the output. ctx is a context object that can be used
to stash information for backward computation. You can cache arbitrary
objects for use in the backward pass using the ctx.save_for_backward method.
"""
ctx.save_for_backward(input) # save input for backward pass
# get lists of odd and even indices
input_shape = input.shape[0]
even_indices = [i for i in range(0, input_shape, 2)]
odd_indices = [i for i in range(1, input_shape, 2)]
# clone the input tensor
output = input.clone()
# apply ReLU to elements where i mod 2 == 0
output[even_indices] = output[even_indices].clamp(min=0)
# apply inversed ReLU to inversed elements where i mod 2 != 0
output[odd_indices] = 0 - output[odd_indices] # reverse elements with odd indices
output[odd_indices] = - output[odd_indices].clamp(min = 0) # apply reversed ReLU
return output
@staticmethod
def backward(ctx, grad_output):
"""
In the backward pass we receive a Tensor containing the gradient of the loss
with respect to the output, and we need to compute the gradient of the loss
with respect to the input.
"""
grad_input = None # set output to None
input, = ctx.saved_tensors # restore input from context
# check that input requires grad
# if not requires grad we will return None to speed up computation
if ctx.needs_input_grad[0]:
grad_input = grad_output.clone()
# get lists of odd and even indices
input_shape = input.shape[0]
even_indices = [i for i in range(0, input_shape, 2)]
odd_indices = [i for i in range(1, input_shape, 2)]
# set grad_input for even_indices
grad_input[even_indices] = (input[even_indices] >= 0).float() * grad_input[even_indices]
# set grad_input for odd_indices
grad_input[odd_indices] = (input[odd_indices] < 0).float() * grad_input[odd_indices]
return grad_input
```

We can now use *BReLU *in our models as follows:

```
class ClassifierBReLU(nn.Module):
'''
Simple fully-connected classifier model to demonstrate BReLU activation.
'''
def __init__(self):
super(ClassifierBReLU, self).__init__()
# initialize layers
self.fc1 = nn.Linear(784, 256)
self.fc2 = nn.Linear(256, 128)
self.fc3 = nn.Linear(128, 64)
self.fc4 = nn.Linear(64, 10)
# create shortcuts for BReLU
self.a1 = brelu.apply
self.a2 = brelu.apply
self.a3 = brelu.apply
def forward(self, x):
# make sure the input tensor is flattened
x = x.view(x.shape[0], -1)
# apply BReLU
x = self.a1(self.fc1(x))
x = self.a2(self.fc2(x))
x = self.a3(self.fc3(x))
x = F.log_softmax(self.fc4(x), dim=1)
return x
model = ClassifierBReLU()
train_model(model)
```

Conclusion
In this tutorial I covered:

- How to create a
**simple custom activation function**with PyTorch, - How to create an** activation function with trainable parameters**, which can be trained using gradient descent,
- How to create an
**activation function with a custom backward step**.

All code from this tutorial is available on GitHub. Other examples of implemented custom activation functions for PyTorch and Keras you can find in this GitHub repository.

In this tutorial, you will discover how to develop face detection and recognition systems for face identification and verification using the MTCNN model and VGGFace2 deep learning model in Keras

**If you’re a regular user of Google Photos, you may have noticed how the application automatically extracts and groups faces of people from the photos that you back up to the cloud.**

A photo application such as Google’s achieves this through the detection of faces of humans (and pets too!) in your photos and by then grouping similar faces together. Detection and then classification of faces in images is a common task in deep learning with neural networks.

In the first step of this tutorial, we’ll use a pre-trained MTCNN model in Keras to detect faces in images. Once we’ve extracted the faces from an image, we’ll compute a similarity score between these faces to find if they belong to the same person.

PrerequisitesBefore you start with detecting and recognizing faces, you need to set up your development environment. First, you need to “read” images through Python before doing any processing on them. We’ll use the plotting library `matplotlib`

to read and manipulate images. Install the latest version through the installer `pip`

:

```
pip3 install matplotlib
```

To use any implementation of a CNN algorithm, you need to install `keras`

. Download and install the latest version using the command below:

```
pip3 install keras
```

The algorithm that we’ll use for face detection is MTCNN (Multi-Task Convoluted Neural Networks), based on the paper Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks (Zhang et al., 2016). An implementation of the MTCNN algorithm for TensorFlow in Python3.4 is available as a package. Run the following command to install the package through `pip`

:

```
pip3 install mtcnn
```

To compare faces after extracting them from images, we’ll use the VGGFace2 algorithm developed by the Visual Geometry Group at the University of Oxford. A TensorFlow-based Keras implementation of the VGG algorithm is available as a package for you to install:

```
pip3 install keras_vggface
```

While you may feel the need to build and train your own model, you’d need a huge training dataset and vast processing power. Since this tutorial focuses on the utility of these models, it uses existing, trained models by experts in the field.

Now that you’ve successfully installed the prerequisites, let’s jump right into the tutorial!

Step 1: Face Detection with the MTCNN ModelThe objectives in this step are as follows:

- retrieve images hosted externally to a local server
- read images through
`matplotlib`

‘s`imread()`

function - detect and explore faces through the MTCNN algorithm
- extract faces from an image.

You may often be doing an analysis from images hosted on external servers. For this example, we’ll use two images of Lee Iacocca, the father of the Mustang, hosted on the BBC and The Detroit News sites.

To temporarily store the images locally for our analysis, we’ll retrieve each from its URL and write it to a local file. Let’s define a function `store_image`

for this purpose:

```
import urllib.request
def store_image(url, local_file_name):
with urllib.request.urlopen(url) as resource:
with open(local_file_name, 'wb') as f:
f.write(resource.read())
```

You can now simply call the function with the URL and the local file in which you’d like to store the image:

```
store_image('https://ichef.bbci.co.uk/news/320/cpsprodpb/5944/production/_107725822_55fd57ad-c509-4335-a7d2-bcc86e32be72.jpg',
'iacocca_1.jpg')
store_image('https://www.gannett-cdn.com/presto/2019/07/03/PDTN/205798e7-9555-4245-99e1-fd300c50ce85-AP_080910055617.jpg?width=540&height=&fit=bounds&auto=webp',
'iacocca_2.jpg')
```

After successfully retrieving the images, let’s detect faces in them.

For this purpose, we’ll make two imports — `matplotlib`

for reading images, and `mtcnn`

for detecting faces within the images:

```
from matplotlib import pyplot as plt
from mtcnn.mtcnn import MTCNN
```

Use the `imread()`

function to read an image:

```
image = plt.imread('iacocca_1.jpg')
```

Next, initialize an `MTCNN()`

object into the `detector`

variable and use the `.detect_faces()`

method to detect the faces in an image. Let’s see what it returns:

```
detector = MTCNN()
faces = detector.detect_faces(image)
for face in faces:
print(face)
```

For every face, a Python dictionary is returned, which contains three keys. The `box`

key contains the boundary of the face within the image. It has four values: x- and y- coordinates of the top left vertex, width, and height of the rectangle containing the face. The other keys are `confidence`

and `keypoints`

. The `keypoints`

key contains a dictionary containing the features of a face that were detected, along with their coordinates:

```
{'box': [160, 40, 35, 44], 'confidence': 0.9999798536300659, 'keypoints': {'left_eye': (172, 57), 'right_eye': (188, 57), 'nose': (182, 64), 'mouth_left': (173, 73), 'mouth_right': (187, 73)}}
```

Now that we’ve successfully detected a face, let’s draw a rectangle over it to highlight the face within the image to verify if the detection was correct.

To draw a rectangle, import the `Rectangle`

object from `matplotlib.patches`

:

```
from matplotlib.patches import Rectangle
```

Let’s define a function `highlight_faces`

to first display the image and then draw rectangles over faces that were detected. First, read the image through `imread()`

and plot it through `imshow()`

. For each face that was detected, draw a rectangle using the `Rectangle()`

class.

Finally, display the image and the rectangles using the `.show()`

method. If you’re using Jupyter notebooks, you may use the `%matplotlib inline`

magic command to show plots inline:

```
def highlight_faces(image_path, faces):
# display image
image = plt.imread(image_path)
plt.imshow(image)
ax = plt.gca()
# for each face, draw a rectangle based on coordinates
for face in faces:
x, y, width, height = face['box']
face_border = Rectangle((x, y), width, height,
fill=False, color='red')
ax.add_patch(face_border)
plt.show()
```

Let’s now display the image and the detected face using the `highlight_faces()`

function:

```
highlight_faces('iacocca_1.jpg', faces)
```

Let’s display the second image and the face(s) detected in it:

```
image = plt.imread('iacocca_2.jpg')
faces = detector.detect_faces(image)
highlight_faces('iacocca_2.jpg', faces)
```

In these two images, you can see that the MTCNN algorithm correctly detects faces. Let’s now extract this face from the image to perform further analysis on it.

At this point, you know the coordinates of the faces from the detector. Extracting the faces is a fairly easy task using list indices. However, the VGGFace2 algorithm that we use needs the faces to be resized to 224 x 224 pixels. We’ll use the PIL library to resize the images.

The function `extract_face_from_image()`

extracts all faces from an image:

```
from numpy import asarray
from PIL import Image
def extract_face_from_image(image_path, required_size=(224, 224)):
# load image and detect faces
image = plt.imread(image_path)
detector = MTCNN()
faces = detector.detect_faces(image)
face_images = []
for face in faces:
# extract the bounding box from the requested face
x1, y1, width, height = face['box']
x2, y2 = x1 + width, y1 + height
# extract the face
face_boundary = image[y1:y2, x1:x2]
# resize pixels to the model size
face_image = Image.fromarray(face_boundary)
face_image = face_image.resize(required_size)
face_array = asarray(face_image)
face_images.append(face_array)
return face_images
extracted_face = extract_face_from_image('iacocca_1.jpg')
# Display the first face from the extracted faces
plt.imshow(extracted_face[0])
plt.show()
```

Here is how the extracted face looks from the first image.

Step 2: Face Recognition with VGGFace2 ModelIn this section, let’s first test the model on the two images of Lee Iacocca that we’ve retrieved. Then, we’ll move on to compare faces from images of the starting eleven of the Chelsea football team in 2018 and 2019. You’ll then be able to assess if the algorithm identifies faces of common players between the images.

In this section, you need to import three modules: `VGGFace`

to prepare the extracted faces to be used in the face recognition models, and the `cosine`

function from SciPy to compute the distance between two faces:

```
from keras_vggface.utils import preprocess_input
from keras_vggface.vggface import VGGFace
from scipy.spatial.distance import cosine
```

Let’s define a function that takes the extracted faces as inputs and returns the computed model scores. The model returns a vector, which represents the features of a face:

```
def get_model_scores(faces):
samples = asarray(faces, 'float32')
# prepare the data for the model
samples = preprocess_input(samples, version=2)
# create a vggface model object
model = VGGFace(model='resnet50',
include_top=False,
input_shape=(224, 224, 3),
pooling='avg')
# perform prediction
return model.predict(samples)
faces = [extract_face_from_image(image_path)
for image_path in ['iacocca_1.jpg', 'iacocca_2.jpg']]
model_scores = get_model_scores(faces)
```

Since the model scores for each face are vectors, we need to find the similarity between the scores of two faces. We can typically use a Euclidean or Cosine function to calculate the similarity.

Vector representation of faces are suited to the cosine similarity. Here’s a detailed comparison between cosine and Euclidean distances with an example.

The `cosine()`

function computes the cosine distance between two vectors. The lower this number, the better match your faces are. In our case, we’ll put the threshold at `0.4`

distance. This threshold is debatable and would vary with your use case. You should set this threshold based on case studies on your dataset:

```
if cosine(model_scores[0], model_scores[1]) <= 0.4:
print("Faces Matched")
```

In this case, the two faces of Lee Iacocca matched.

```
Faces Matched
```

Let’s put the model to good use in this section of the tutorial. We’ll compare the faces in two images of starting elevens of the Chelsea Football Club in a Europa League match vs Slavia Prague in the 2018–19 season and the UEFA Super Cup match vs Liverpool in the 2019–20 season. While many of the players feature in both match day squads, let’s see if the algorithm is able to detect all common players.

First, let’s retrieve the resources from the URLs, detect the faces in each image and highlight them:

```
store_image('https://cdn.vox-cdn.com/thumbor/Ua2BXGAhneJHLQmLvj-ZzILK-Xs=/0x0:4872x3160/1820x1213/filters:focal(1877x860:2655x1638):format(webp)/cdn.vox-cdn.com/uploads/chorus_image/image/63613936/1143553317.jpg.5.jpg',
'chelsea_1.jpg')
image = plt.imread('chelsea_1.jpg')
faces_staring_xi = detector.detect_faces(image)
highlight_faces('chelsea_1.jpg', faces_staring_xi)
store_image('https://cdn.vox-cdn.com/thumbor/mT3JHQtZIyInU8_uGxVH-TCbF50=/0x415:5000x2794/1820x1213/filters:focal(1878x1176:2678x1976):format(webp)/cdn.vox-cdn.com/uploads/chorus_image/image/65171515/1161847141.jpg.0.jpg',
'chelsea_2.jpg')
image = plt.imread('chelsea_2.jpg')
faces = detector.detect_faces(image)
highlight_faces('chelsea_2.jpg', faces)
```

![Comparison of faces](https://dab1nmslvvntp.cloudfront.net/wp-content/uploads/2019/10/1572502261comparison-838x1024.jpg)[SBNation](https://weaintgotnohistory.sbnation.com/)

Before we proceed further, here are the starting elevens from both matches:

- Slavia Prague Match Starting XI: Kepa, Azpilicueta, Luiz, Christensen, Emerson, Kante, Barkley, Kovacic, Hazard, Pedro, Giroud
- Slavia Prague Match Starting XI: Kepa, Azpilicueta, Christensen, Zouma, Emerson, Kante, Jorginho, Kovacic, Pedro, Giroud, Pulisic

We have eight players who are common to both starting XIs and who ideally should be matched by the algorithm.

Let’s first compute scores:

```
slavia_faces = extract_face_from_image('chelsea_1.jpg')
liverpool_faces = extract_face_from_image('chelsea_2.jpg')
model_scores_starting_xi_slavia = get_model_scores(slavia_faces)
model_scores_starting_xi_liverpool = get_model_scores(liverpool_faces)
``
for idx, face_score_1 in enumerate(model_scores_starting_xi_slavia):
for idy, face_score_2 in enumerate(model_scores_starting_xi_liverpool):
score = cosine(face_score_1, face_score_2)
if score <= 0.4:
# Printing the IDs of faces and score
print(idx, idy, score)
# Displaying each matched pair of faces
plt.imshow(slavia_faces[idx])
plt.show()
plt.imshow(liverpool_faces[idy])
plt.show()
```

Here’s the list of pairs of faces that the algorithm matched. Notice that it has been able to match all eight pairs of faces.

While we were successfully able to match each face in our images, I’d like to take a step back to discuss ramifications of the scores. As discussed earlier, there’s no universal threshold that would match two images together. You may need to re-define these thresholds with new data coming into the analysis. For instance, even Google Photos takes your inputs when it’s unable to programmatically determine the best threshold for a pair.

The best way forward is to carefully assess cases when matching different types of faces. Emotions of the faces and their angles play a role in determining the precision too. In our use case, notice how I have deliberately used photos of starting elevens as players are staring right into the camera! You can try matching the starting eleven faces with those of a trophy celebration and I’m pretty sure the accuracy would drop.

ConclusionIn this tutorial, we first detected faces in images using the MTCNN model and highlighted them in the images to determine if the model worked correctly. Next, we used the VGGFace2 algorithm to extract features from faces in the form of a vector and matched different faces to group them together.

In this tutorial, we are going to be covering some basics on what TensorFlow is, and how to begin using it.

TensorFlow is a Python library for fast numerical computing created and released by Google.

It is a foundation library that can be used to create Deep Learning models directly or by using wrapper libraries that simplify the process built on top of TensorFlow.

In this post you will discover the TensorFlow library for Deep Learning.

Discover how to develop deep learning models for a range of predictive modeling problems with just a few lines of code in my new book, with 18 step-by-step tutorials and 9 projects.

TensorFlow is an open source library for fast numerical computing.

It was created and is maintained by Google and released under the Apache 2.0 open source license. The API is nominally for the Python programming language, although there is access to the underlying C++ API.

Unlike other numerical libraries intended for use in Deep Learning like Theano, TensorFlow was designed for use both in research and development and in production systems, not least RankBrain in Google search and the fun DeepDream project.

It can run on single CPU systems, GPUs as well as mobile devices and large scale distributed systems of hundreds of machines.

You may also like: Deep Learning Using TensorFlow

Installation of TensorFlow is straightforward if you already have a Python SciPy environment.

TensorFlow works with Python 2.7 and Python 3.3+. You can follow the Download and Setup instructions on the TensorFlow website. Installation is probably simplest via PyPI and specific instructions of the pip command to use for your Linux or Mac OS X platform are on the Download and Setup webpage.

There are also virtualenv and docker images that you can use if you prefer.

To make use of the GPU, only Linux is supported and it requires the Cuda Toolkit.

Computation is described in terms of data flow and operations in the structure of a directed graph.

- Nodes: Nodes perform computation and have zero or more inputs and outputs. Data that moves between nodes are known as tensors, which are multi-dimensional arrays of real values.
- Edges: The graph defines the flow of data, branching, looping and updates to state. Special edges can be used to synchronize behavior within the graph, for example waiting for computation on a number of inputs to complete.
- Operation: An operation is a named abstract computation which can take input attributes and produce output attributes. For example, you could define an add or multiply operation.

This first example is a modified version of the example on the TensorFlow website. It shows how you can create a session, define constants and perform computation with those constants using the session.

```
import tensorflow as tf
sess = tf.Session()
a = tf.constant(10)
b = tf.constant(32)
print(sess.run(a+b))
```

Running this example displays:

```
42
```

This next example comes from the introduction on the TensorFlow tutorial.

This examples shows how you can define variables (e.g. W and b) as well as variables that are the result of computation (y).

We get some sense of TensorFlow separates the definition and declaration of the computation from the execution in the session and the calls to run.

```
import tensorflow as tf
import numpy as np
# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3
# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but Tensorflow will
# figure that out for us.)
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b
# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
# Before starting, initialize the variables. We will 'run' this first.
init = tf.initialize_all_variables()
# Launch the graph.
sess = tf.Session()
sess.run(init)
# Fit the line.
for step in xrange(201):
sess.run(train)
if step % 20 == 0:
print(step, sess.run(W), sess.run(b))
# Learns best fit is W: [0.1], b: [0.3]
```

Running this example prints the following output:

```
(0, array([ 0.2629351], dtype=float32), array([ 0.28697217], dtype=float32))
(20, array([ 0.13929555], dtype=float32), array([ 0.27992988], dtype=float32))
(40, array([ 0.11148042], dtype=float32), array([ 0.2941364], dtype=float32))
(60, array([ 0.10335406], dtype=float32), array([ 0.29828694], dtype=float32))
(80, array([ 0.1009799], dtype=float32), array([ 0.29949954], dtype=float32))
(100, array([ 0.10028629], dtype=float32), array([ 0.2998538], dtype=float32))
(120, array([ 0.10008363], dtype=float32), array([ 0.29995731], dtype=float32))
(140, array([ 0.10002445], dtype=float32), array([ 0.29998752], dtype=float32))
(160, array([ 0.10000713], dtype=float32), array([ 0.29999638], dtype=float32))
(180, array([ 0.10000207], dtype=float32), array([ 0.29999897], dtype=float32))
(200, array([ 0.1000006], dtype=float32), array([ 0.29999971], dtype=float32))
```

You can learn more about the mechanics of TensorFlow in the Basic Usage guide.

Your TensorFlow installation comes with a number of Deep Learning models that you can use and experiment with directly.

Firstly, you need to find out where TensorFlow was installed on your system. For example, you can use the following Python script:

```
python -c 'import os; import inspect; import tensorflow; print(os.path.dirname(inspect.getfile(tensorflow)))'
```

For example, this could be:

```
/usr/lib/python2.7/site-packages/tensorflow
```

Change to this directory and take note of the models subdirectory. Included are a number of deep learning models with tutorial-like comments, such as:

- Multi-threaded word2vec mini-batched skip-gram model.
- Multi-threaded word2vec unbatched skip-gram model.
- CNN for the CIFAR-10 network.
- Simple, end-to-end, LeNet-5-like convolutional MNIST model example.
- Sequence-to-sequence model with an attention mechanism.

Also check the examples directory as it contains an example using the MNIST dataset.

There is also an excellent list of tutorials on the main TensorFlow website. They show how to use different network types, different datasets and how to use the framework in various different ways.

Finally, there is the TensorFlow playground where you can experiment with small networks right in your web browser.

In this post you discovered the TensorFlow Python library for deep learning.

You learned that it is a library for fast numerical computation, specifically designed for the types of operations that are required in the development and evaluation of large deep learning models.

Deploying a Keras Deep Learning Model as a Web Application in Python

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Downloadable PDF of Best AI Cheat Sheets in Super High DefinitionLet’s begin.

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Data Science in HD

Neural Networks Cheat Sheets

Neural Networks Basics Cheat Sheet

An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.

- Input Layer (All the inputs are fed in the model through this layer)
- Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
- Output Layer (The data after processing is made available at the output layer)

Neural Networks Graphs Cheat Sheet

Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

Machine Learning Cheat Sheets

Machine Learning with Emojis Cheat Sheet

Scikit Learn Cheat Sheet

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 is a simple and efficient tools for data mining and data analysis. It’s built on NumPy, SciPy, and matplotlib an open source, commercially usable — BSD license

Scikit-learn Algorithm Cheat Sheet

This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.

If you like these cheat sheets, you can let me know here.### Machine Learning: Scikit-Learn Algorythm for Azure Machine Learning Studios

Scikit-Learn Algorithm for Azure Machine Learning Studios Cheat Sheet

Data Science with Python Cheat Sheets

TensorFlow Cheat Sheet

TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.

If you like these cheat sheets, you can let me know here.### Data Science: Python Basics Cheat Sheet

Python Basics Cheat Sheet

Python is one of the most popular data science tool due to its low and gradual learning curve and the fact that it is a fully fledged programming language.

PySpark RDD Basics Cheat Sheet

“At a high level, every Spark application consists of a *driver program* that runs the user’s `main`

function and executes various *parallel operations* on a cluster. The main abstraction Spark provides is a *resilient distributed dataset* (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to *persist* an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.” via Spark.Aparche.Org

NumPy Basics Cheat Sheet

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

***If you like these cheat sheets, you can let me know *****here.**

Bokeh Cheat Sheet

“Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.” from Bokeh.Pydata.com

Karas Cheat Sheet

Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.

Padas Basics Cheat Sheet

Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.

If you like these cheat sheets, you can let me know here.### Pandas Cheat Sheet: Data Wrangling in Python

Pandas Cheat Sheet: Data Wrangling in Python

The term “data wrangler” is starting to infiltrate pop culture. In the 2017 movie Kong: Skull Island, one of the characters, played by actor Marc Evan Jackson is introduced as “Steve Woodward, our data wrangler”.

Data Wrangling with Pandas Cheat Sheet

- Although many fundamental data processing functions exist in R, they have been a bit convoluted to date and have lacked consistent coding and the ability to easily
*flow*together → leads to difficult-to-read nested functions and/or*choppy*code. - R Studio is driving a lot of new packages to collate data management tasks and better integrate them with other analysis activities → led by Hadley Wickham & the R Studio team → Garrett Grolemund, Winston Chang, Yihui Xie among others.
- As a result, a lot of data processing tasks are becoming packaged in more cohesive and consistent ways → leads to:
- More efficient code
- Easier to remember syntax
- Easier to read syntax” via Rstudios

Data Wrangling with ddyr and tidyr Cheat Sheet

If you like these cheat sheets, you can let me know here.### Data Science: Scipy Linear Algebra

Scipy Linear Algebra Cheat Sheet

SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.[3]

Matplotlib Cheat Sheet

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented APIfor embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural “pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged. SciPy makes use of matplotlib.

Pyplot is a matplotlib module which provides a MATLAB-like interface matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.

Data Visualization with ggplot2 Cheat Sheet

Big-O Cheat Sheet

Special thanks to DataCamp, Asimov Institute, RStudios and the open source community for their content contributions. You can see originals here:

Big-O Algorithm Cheat Sheet: http://bigocheatsheet.com/

Bokeh Cheat Sheet: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Bokeh_Cheat_Sheet.pdf

Data Science Cheat Sheet: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics

Data Wrangling Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

Data Wrangling: https://en.wikipedia.org/wiki/Data_wrangling

Ggplot Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

Keras Cheat Sheet: https://www.datacamp.com/community/blog/keras-cheat-sheet#gs.DRKeNMs

Keras: https://en.wikipedia.org/wiki/Keras

Machine Learning Cheat Sheet: https://ai.icymi.email/new-machinelearning-cheat-sheet-by-emily-barry-abdsc/

Machine Learning Cheat Sheet: https://docs.microsoft.com/en-in/azure/machine-learning/machine-learning-algorithm-cheat-sheet

ML Cheat Sheet:: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html

Matplotlib Cheat Sheet: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet#gs.uEKySpY

Matpotlib: https://en.wikipedia.org/wiki/Matplotlib

Neural Networks Cheat Sheet: http://www.asimovinstitute.org/neural-network-zoo/

Neural Networks Graph Cheat Sheet: http://www.asimovinstitute.org/blog/

Neural Networks: https://www.quora.com/Where-can-find-a-cheat-sheet-for-neural-network

Numpy Cheat Sheet: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.AK5ZBgE

NumPy: https://en.wikipedia.org/wiki/NumPy

Pandas Cheat Sheet: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.oundfxM

Pandas: https://en.wikipedia.org/wiki/Pandas_(software)

Pandas Cheat Sheet: https://www.datacamp.com/community/blog/pandas-cheat-sheet-python#gs.HPFoRIc

Pyspark Cheat Sheet: https://www.datacamp.com/community/blog/pyspark-cheat-sheet-python#gs.L=J1zxQ

Scikit Cheat Sheet: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet

Scikit-learn: https://en.wikipedia.org/wiki/Scikit-learn

Scikit-learn Cheat Sheet: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html

Scipy Cheat Sheet: https://www.datacamp.com/community/blog/python-scipy-cheat-sheet#gs.JDSg3OI

SciPy: https://en.wikipedia.org/wiki/SciPy

TesorFlow Cheat Sheet: https://www.altoros.com/tensorflow-cheat-sheet.html