build a Convolutional Neural Network (CNN) using TensorFlow in Python
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1. The document discusses CNN architecture and concepts like convolution, pooling, and fully connected layers. 2. Convolutional layers apply filters to input images to generate feature maps, capturing patterns like edges. Pooling layers downsample these to reduce parameters. 3. Fully connected layers at the end integrate learned features for classification tasks like image recognition. CNNs exploit spatial structure in images unlike regular neural networks.
![WHAT COMPUTERS ‘SEE’: IMAGES AS NUMBERS
What you see What you both see What the computer "sees"
Levin
Image
Processing
&
Computer
Vision
Input Image Input Image + values Pixel intensity values
(“pix-el”=picture-element)
An image is just a
matrix of numbers
[0,255]. i.e.,
1080x1080x3 for an
RGB image.
Question: is this
Lincoln? Washington?
Jefferson? Obama?
•How can the computer
answer this question?
Can I just do
classification on the
1,166400-long image
vector directly?
No. Instead: exploit
image spatial
structure. Learn
patches. Build them
up](https://image.slidesharecdn.com/buildyourcnnusingtensorflow-240225163903-78e83a63/85/build-a-Convolutional-Neural-Network-CNN-using-TensorFlow-in-Python-7-320.jpg)
![Handwritten Digit Recognition and performance of various modelsation[autosaved]](https://cdn.slidesharecdn.com/ss_thumbnails/presentationautosaved-210810075721-thumbnail.jpg?width=560&fit=bounds)
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build a Convolutional Neural Network (CNN) using TensorFlow in Python
- 1. Technical Skills-2(Tensorflow) Topic: CNN WITH TENSOR FLOW Department of IoT Exp- 3
- 2. AIM OF THE SESSION To familiarize students with the concept of CNN INSTRUCTIONAL OBJECTIVES This Session is designed to: 1. CNN application 2. CNN architecture LEARNING OUTCOMES At the end of this session, you should be able to: 1. Able to understand CNN learning model
- 3. The topics covered in the session are 1. What is need for CNN 2. Differences between CNN and ANN 3. CNN architecture 4. Convolution operation 5. Pooling techniques 3 SESSION Introduction
- 4. 1. WHAT DO YOU SEE, AND HOW? CAN WE TEACH MACHINES TO SEE?
- 5. WHAT DO YOU SEE?
- 6. HOW DO YOU SEE? How can we help computers see?
- 7. WHAT COMPUTERS ‘SEE’: IMAGES AS NUMBERS What you see What you both see What the computer "sees" Levin Image Processing & Computer Vision Input Image Input Image + values Pixel intensity values (“pix-el”=picture-element) An image is just a matrix of numbers [0,255]. i.e., 1080x1080x3 for an RGB image. Question: is this Lincoln? Washington? Jefferson? Obama? •How can the computer answer this question? Can I just do classification on the 1,166400-long image vector directly? No. Instead: exploit image spatial structure. Learn patches. Build them up
- 8. 2. CLASSICAL MACHINE VISION ROOTS IN STUDY OF HUMAN/ANIMAL BRAINS
- 9. INSPIRATION: HUMAN/ANIMAL VISUAL CORTEX • Layers of neurons: pixels, edges, shapes, primitives, scenes • E.g. Layer 4 responds to bands w/ given slant, contrasting edges
- 10. PRIMITIVES:NEURONS &ACTION POTENTIALS • Chemical accumulation across dendritic connections Pre-synaptic axon • post-synaptic dendrite • neuronal cell body • Each neuron receives multiple signals from its many dendrites When threshold crossed, it fires • Its axon then sends outgoing signal to downstream neurons • Weak stimuli ignored • Sufficiently strong cross activation threshold • Non-linearity within each neuronal level • Neurons connected into circuits (neural networks): emergent properties, learning, memory • Simple primitives arranged in simple, repetitive, and extremely large networks • 86 billion neurons, each connects to 10k neurons, 1 quadrillion (1012) connections
- 11. ABSTRACTION LAYERS: EDGES, BARS, DIR., SHAPES, OBJECTS, SCENES LGN: Small dots V1: Orientation, disparity, some color V4: Color, basic shapes, 2D/3D, curvature VTC: Complex features and objects(VTC: ventral temporal cort e • Abstraction layers visual cortex layers • Complex concepts from simple parts, hierarchy • Primitives of visual concepts encoded in neuronal connection in early cortical layers
- 12. • Massive recent expanse of human brain has re-used a relatively simple but general learning architecture GENERAL“LEARNINGMACHINE”,REUSED WIDELY • Hearing, taste, smell, sight, touch all re- use similar learning architecture Visual Cortex Motor Cortex • Interchangeable circuitry • Auditory cortex learns to ‘see’ if sent visual signals • Injury area tasks shift to uninjured areas • Not fully-general learning, but well-adapted to our world • Humans co-opted this circuitry to many new applications • Modern tasks accessible to any homo sapiens (<70k years) • ML primitives not too different from animals: more to come? human chimp Hardware expansion
- 13. WHAT IS CNN? Convolutional Neural Networks (CNN) is a feed forward neural network that is generally used for Image recognition and object classification. CNNs are inspired from the visual cortex of the brain and have been widely applied in image and speech recognition. Cnn represent a huge breakthrough in image recognition
- 14. WHY CNN Until quite recently, computers were not good at tasks like recognizing puppy in a picture or recognizing spoken words, which humans excel at. • The CNN is essential when it comes to image classification problems since it has such a high degree of accuracy. • Prior to training an Artificial Neural Network, a 2D image is converted into a 1-dimensional vector. • Furthermore, as the image size increases, the number of training parameters increases exponentially, leading to storage loss. • ANNs do not capture the sequence information that sequence data requires.
- 15. CNN VS ANN 1. 2 For small images it might work, but for large images the no of pixels are so many leading to millions of connections between neurons, leading to intractable solutions 2. No of features /pixels are reduced thru convolution and pooling operations 15 Deep learning • In an ANN, each neuron in the network is connected to every other neuron in the adjacent hidden layers. 1. In a CNN, each neuron in the hidden layer is connected to a small region of the input neurons.
- 16. CNN ARCHITECTURE • A CNN typically has three layers: a convolutional layer, a pooling layer, and a fully connected layer. Deep learning 16
- 18. CNN ARCHITECTURE: TYPES OF LAYERS • Convolutional layer • a “filter” passes over the image, scanning a few pixels at a time and creating a feature map that predicts the class to which each feature belongs. • Pooling layer (downsampling) • reduces the amount of information in each feature obtained in the convolutional layer while maintaining the most important information . • there are usually several rounds of convolution and pooling. 2/25/2024 Deep learning 18
- 19. CNN ARCHITECTURE: TYPES OF LAYERS • Fully connected input layer (flatten) • It takes the output of the previous layers, “flattens” them and turns them into a single vector that can be an input for the next stage. • The first fully connected layer takes the inputs from the feature analysis and applies weights to predict the correct label. • Fully connected output layer gives the final probabilities for each label. 2/25/2024 Deep learning 19
- 21. CONVOLUTION OPERATION IS ELEMENT WISE MULTIPLY AND ADD Filter / Kernel
- 22. PRODUCING FEATURE MAPS Original Sharpen Edge Detect “Strong” Edge Detect
- 23. A SIMPLE PATTERN: EDGES HOW CAN WE DETECT EDGES WITH A KERNEL? Input 1 -1 Filter Output
- 24. SIMPLE KERNELS / FILTERS
- 25. X OR X? Image is represented as matrix of pixel values… and computers are literal! We want to be able to classify an X as an X even if it’s shifted, shrunk, rotated, deformed.
- 26. CONVOLUTION PROCESS • Assume the filter/kernel is weight matrix wk . For example, lets assume a 3 x 3 weighted matrix. • The kernel is a filter to extract some particular features from the original images. • It could be for extracting curves , identifying a specific colour, or recognizing a particular voice. Deep learning 26
- 27. CONVOLUTION OPERATION • Kernel/Filter, K = • 1 0 1 0 1 0 1 0 1 27
- 28. PROCESS OF CONVOLUTION (CONTD.) 0 1 1 1 0 0 1 0 1 Input layer Filter/Kernel (Weighted matrix) 515 • For example, when the weighted matrix starts from the top left corner of the input layer, the output value is calculated as: (81x0+2x1+209x1)+(24x1+56x0+108X0)+(91x1+0x0+189x1) = 515 81 2 209 44 71 58 24 56 108 98 12 112 91 0 189 65 79 232 12 0 0 5 1 71 2 32 23 58 8 209 49 98 81 112 54 9 EXAMPLE • As the filter/kernel is slided across the input layer, the convolved layer is obtained by adding the values obtained by element wise multiplication of the weight matrix. Output
- 29. PROCESS OF CONVOLUTION (CONTD.) 0 1 1 1 0 0 1 0 1 Input layer Filter/Kernel (Weighted matrix) Output (Activation/Feature Map) 515 374 • Dimension of output=n-f+1 • The distance between two consecutive receptive fields is called the stride. • In this example stride is 1 since the receptive field was moved by 1 pixel at a time. 81 2 209 44 71 58 24 56 108 98 12 112 91 0 189 65 79 232 12 0 0 5 1 71 2 32 23 58 8 209 49 98 81 112 54 9 EXAMPLE • The filter then moves by 1 pixel to the next receptive field and the process is repeated. The output layer obtained after the filter slides over the entire image would be a 4X4 matrix.This is called an activation map/ feature map.
- 30. 30 Calculating output dimension of Convolution layer
- 31. ReLU Layer 31 • ReLU stands for Rectified Linear Unit and is a non-linear operation. • ReLU is an element wise operation (applied per pixel) and replaces all negative pixel values in the feature map by zero. Output = Max(zero, Input)
- 32. Pooling Layer (Sub-sampling or Down-sampling) • Pooling layer reduce the size of feature maps by using some functions to summarize sub- regions, such as taking the average or the maximum value
- 33. FULLY CONNECTED LAYER After completion of series of convolutional, nonlinear and pooling layers, it is necessary to attach a fully connected layer. This layer takes the output information from convolutional networks. Attaching a fully connected layer to the end of the network results in an N dimensional vector, where N is the number of classes from which the model selects the desired class. 33 Deep learning
- 34. EXAMPLE
- 35. AN IMAGE CLASSIFICATION CNN
- 36. CNNS FOR CLASSIFICATION • Train model with image data. • Learn weights of filters in convolutional layers. D tf.keras.activations. * tf.keras.layers.MaxPool2 D Convolution:Apply filters to generate feature maps. Non-linearity: Often ReLU. Pooling: Downsampling operation on each feature map. tf.keras.layers.Conv2
- 37. CNNS FOR CLASSIFICATION: FEATURE LEARNING Learn features in input image through convolution Learn Introduce non-linearity through activation function (real-world data is non-linear!) Introduce Reduce dimensionality and preserve spatial invariance with pooling Reduce
- 38. CNNS FOR CLASSIFICATION: CLASS PROBABILITIES CONV and POOL layers output high-level features of input Fully connected layer uses these features for classifying input image Express output as probability of image belonging to a particular class
- 39. REPRESENTATION LEARNING IN DEEP CNNS Mid level features Low level features High level features Edges, dark spots Conv Layer 1 Eyes, ears,nose Conv Layer 2 Facial structure Conv Layer 3
- 40. EXAMPLE – SIX CONVOLUTIONAL LAYERS
- 41. CONVOLUTIONAL LAYERS: LOCAL CONNECTIVITY For a neuron in hidden layer: • Take inputs from patch • Compute weighted sum • Apply bias tf.keras.layers. Conv2D
- 42. CONVOLUTIONAL LAYERS: LOCAL CONNECTIVITY For a neuron in hidden layer: • Take inputs from patch • Compute weighted sum • Apply bias 4x4 filter: matrix of weights wij for neuron (p,q) in hidden layer 1) applying a window of weights 2) computing linear combinations 3) activating with non-linear function tf.keras.layers.Conv2D
- 43. OTHER ARCHITECTURES OFCNN LeNet-5 (1998) • AlexNet (2012) ZFNet (2013) GoogLeNet (2014) VGGNet(2014) ResNet (2015) • Over the years, many variants of this basic architecture have been developed such as: • These models can be used in applications by developers.