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Artificial Neural Network | Deep Neural Network Explained | Artificial Neural Network | Simplilearn

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The document provides an overview of neural networks, explaining their functionality, types, and applications, including image classification for recognizing cats and dogs. It also discusses the history of artificial intelligence, highlighting key milestones and various neural network structures such as feedforward and convolutional networks. Additionally, it presents a use case implementing a neural network classification model in Python, showcasing the code necessary for training and making predictions.

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Neural Networks
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What’s in it for you? APPLICATIONS OF NEURAL NETWORK TYPES OF NEURAL NETWORK USE CASE - HOW DOES NEURAL NETWORK WORK? What is NEURAL NETWORK?
What’s in it for you? TYPES OF NEURAL NETWORK USE CASE - to classify between the photos of dogs and cats HOW DOES NEURAL NETWORK WORK? What is NEURAL NETWORK? What can Neural network do?
Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 What is a Neural Network?
What is Neural Network? Hi Guys! I heard you want to know what Neural network is!
What is Neural Network? As a matter of fact, you have been using Neural network on a daily basis!
What is Neural Network? When you ask your mobile assistant to perform a search for you…..
What is Neural Network? Self driving cars use it…
What is Neural Network? Computer games use it too!
What is Neural Network? Also used in processing the map images on your phone..
What is Neural Network? Neural network is a system or hardware that is designed to operate like a human brain
Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 What can Neural Network do?
What can Neural Network do? Let’s list out the things Neural network can do for you..
What can Neural Network do? Translate text
What can Neural Network do? Identify faces Translate text
What can Neural Network do? Identify faces Translate text Recognize speech
What can Neural Network do? Identify faces Translate text Read handwritten text Recognize speech
What can Neural Network do? Identify faces Recognize speech Translate text Read handwritten text Control robots
What can Neural Network do? And a lot of other things…
Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 How does a Neural Network work?
How does a Neural Network work? Now we have come far enough to understand how Neural network works
How does a Neural Network work? These are the different layers of a neural network
How does a Neural Network work? Input layer Picks up input signals and passes them to the next LAYER
How does a Neural Network work? Hidden layer Does all kinds of calculations and feature extraction
How does a Neural Network work? Output layer This layer delivers the final result
How does a Neural Network work? Now let’s make use of this neural network and see how it works!
How does a Neural Network work? Wonder how traffic cameras identify vehicles Registration plate on the road to detect SpeedingVehicles and those breaking the LAW...!!
How does a Neural Network work? 28*28 pixels of the image is FEd as input to identify the Registration plate Let’s consider the image of this vehicle and find out what’s on the number plate 28 28
How does a Neural Network work? Each Neuron has a number called activation that represents the grayscale value of the corresponding pixel ranging from 0 to 1. 1 for white pixel and 0 for black pixel 28 28 0.82
How does a Neural Network work? Each neuron is lit up when its activation is close to 1 28 28 0.82
How does a Neural Network work? Pixels in the form of arrays are fed to the input layer.. 28 28 Pixels of car image fed as input
How does a Neural Network work? Lets name the inputs x1, x2 and x3 respectively.. x1 x2 x3 28 28 Pixels of car image fed as input
How does a Neural Network work? The input layer passes it to the hidden layer.. x1 x2 x3 28 28
How does a Neural Network work? x1 x2 x3 28 28 The interconnections are assigned weights at random.. W11 W12 W13 W14 W15 W16 W17 W18
How does a Neural Network work? x1 x2 x3 28 28 The weights are multiplied with the input signals and a bias is added to all of them.. i=1 n w x + b i i* W11 W12 W13 W14 W15 W16 W17 W18
How does a Neural Network work? x1 x2 x3 28 28 i=1 n w x + b i i* The weighted sum of inputs is fed as input to the activation function to decide which nodes to fire for feature extraction.. W11 W12 W13 W14 W15 W16 W17 W18
How does a Neural Network work? x1 x2 x3 28 28 i=1 n w x + b i i* As the signal flows within the hidden layers, the weighted sum of inputs is calculated and is fed to the activation function in each layer to decide which nodes to fireW11 W12 W13 W14 W15 W16 W17 W18 W21 W22 W23 W24 W25 W26 W27 W28 Feature extraction of the number plate
How does a Neural Network work? Lets look at the different activation functions before we move ahead
How does a Neural Network work? 0 1 Y (X)= 1 1 + e-x SIGMOID FUNCTION USED WHEN THE MODEL IS PREDICTING PROBABILITY i=1 n w x + b i i*
How does a Neural Network work? 0 1 Y (X)= 1 1 + e-x SIGMOID FUNCTION USED WHEN THE MODEL IS PREDICTING PROBABILITY i=1 n w x + b i i* i= 1 n w x + b i i 0 1 Y (X)= 1, if x>=0 0, if x<0( ) USED WHEN THE OUTPUT DEPENDS ON A THRESHOLD VALUE * THRESHOLD FUNCTION
How does a Neural Network work? 0 1 Y (X)= 1 1 + e-x SIGMOID FUNCTION USED WHEN THE MODEL IS PREDICTING PROBABILITY i=1 n w x + b i i* i= 1 n w x + b i i 0 1 Y (X)= 1, if x>=0 0, if x<0( ) USED WHEN THE OUTPUT DEPENDS ON A THRESHOLD VALUE * THRESHOLD FUNCTION i= 1 n w x + b i i* 0 1 Y (X) = max(X,0) relu FUNCTION It gives an output X if X is positive, 0 otherwise
How does a Neural Network work? 0 1 Y (X)= 1 1 + e-x SIGMOID FUNCTION USED WHEN THE MODEL IS PREDICTING PROBABILITY i=1 n w x + b i i* i= 1 n w x + b i i 0 1 Y (X)= 1, if x>=0 0, if x<0( ) USED WHEN THE OUTPUT DEPENDS ON A THRESHOLD VALUE * THRESHOLD FUNCTION i= 1 n w x + b i i* 0 1 Y (X) = max(X,0) relu FUNCTION It gives an output X if X is positive, 0 otherwise Hyperbolic tangent function 0 1 Y (X)= 1 - e 1 + e-2x i=1 n w x + b i i* -1 -2x Similar to sigmoid function with a range of (- 1,1)
How does a Neural Network work? Now that you know what is an activation function, lets get back to the neural network..
How does a Neural Network work? x1 x2 x3 28 28 Finally, the model would predict the outcome by applying suitable activation function to the output layer Identifies the number on the plate W11 W12 W13 W14 W15 W16 W17 W18 W21 W22 W23 W24 W25 W26 W27 W28 W31 W32 W33 W34 08-D-03858 Optical Character Recognition (OCR) is used on the images to convert it into text in order to identify what’s written on the plate
How does a Neural Network work? x1 x2 x3 28 28 W11 W12 W13 W14 W15 W16 W17 W18 W21 W22 W23 W24 W25 W26 W27 W28 W31 W32 W33 W34 Error in the output is backpropagated through the network and weights are adjusted to minimize the error rate.This is calculated by a cost function Identifies the number on the plate Backpropagation
How does a Neural Network work? The output is compared with the original result and multiple iterations are done to get the maximum accuracy
How does a Neural Network work? With every iteration, the weights at every interconnections are adjusted based on the error…
Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 Types of Artificial Neural Network
Types of Artificial Neural Network Lets see the different types of neural network
Types of Artificial Neural Network Feedforward neural network Simplest form of ANN. Data travels only in one direction(input - output)
Types of Artificial Neural Network Feedforward neural network Simplest form of ANN. Data travels only in one direction(input - output) Applications Vision and speech recognition
Types of Artificial Neural Network Radial basis function neural network This model classifies the data point based on its distance from a center point
Types of Artificial Neural Network Radial basis function neural network This model classifies the data point based on its distance from a center point Applications Power restoration systems
Types of Artificial Neural Network Kohonen self organizing neural network Vectors of random dimensions are input to discrete map comprised of neurons
Types of Artificial Neural Network Kohonen self organizing neural network Vectors of random dimensions are input to discrete map comprised of neurons Used to recognize patterns in data like in medical analysis Applications
Types of Artificial Neural Network Recurrent neural network The hidden layer saves its output to be used for future prediction
Types of Artificial Neural Network Recurrent neural network The hidden layer saves its output to be used for future prediction Text to speech conversion model Applications
Types of Artificial Neural Network convolution neural network The input features are taken in batches like a filter.This allows the network to remember an image in parts!
Types of Artificial Neural Network convolution neural network The input features are taken in batches like a filter.This allows the network to remember an image in parts! Used in signal and image processing Applications
Types of Artificial Neural Network modular neural networkIt has a collection of different neural networks working together to get the output
Types of Artificial Neural Network modular neural networkIt has a collection of different neural networks working together to get the output Still undergoing research Applications
Types of Artificial Neural Network Feedforward neural network Radial basis function neural network Kohonen self organizing neural network Recurrent neural network convolution neural network modular neural network
Use Case – Problem Statement Let’s use the system to identify between a cat and a dog!
Use Case – Problem Statement Classify photos of cats and dogs using Neural Network Problem statement
Use Case – Problem Statement The use case will be implemented on python
Use Case – Implementation #Import the required packages from keras.models import Sequential from keras.layers import Conv2D from keras.layers import MaxPooling2D from keras.layers import Flatten from keras.layers import Dense
Use Case – Implementation # Initialising the CNN classifier = Sequential() # Convolution classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu’)) # Pooling classifier.add(MaxPooling2D(pool_size = (2, 2))) # Adding a second convolutional layer classifier.add(Conv2D(32, (3, 3), activation = 'relu')) classifier.add(MaxPooling2D(pool_size = (2, 2)))
Use Case – Implementation # Flattening classifier.add(Flatten()) # Full connection classifier.add(Dense(units = 128, activation = 'relu')) classifier.add(Dense(units = 1, activation = 'sigmoid’)) # Compiling the CNN classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
Use Case – Implementation # Fitting the CNN to the images test_datagen = ImageDataGenerator(rescale = 1./255) training_set = train_datagen.flow_from_directory('dataset/training_set', target_size = (64, 64), batch_size = 32, class_mode = 'binary')
Use Case – Implementation # Fitting the CNN to the images test_set = test_datagen.flow_from_directory('dataset/test_set', target_size = (64, 64), batch_size = 32, class_mode = 'binary')
Use Case – Implementation # Training the model classifier.fit_generator(training_set, steps_per_epoch = 8000, epochs = 1, validation_data = test_set, validation_steps = 2000)
Use Case – Implementation # Part 3 - Making new predictions import numpy as np from keras.preprocessing import image test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size = (64, 64)) test_image = image.img_to_array(test_image) test_image = np.expand_dims(test_image, axis = 0) result = classifier.predict(test_image) training_set.class_indices if result[0][0] == 1: prediction = 'dog' else: prediction = 'cat’
Use Case – Implementation # Part 3 - Making new predictions prediction
Use Case – Implementation So, we have successfully built a neural network that can distinguish between photos of a cat and a dog..
Use Case – Implementation The accuracy of the model is around 80% which is quite good…
Key Takeaways
So what’s your next step?

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Artificial Neural Network | Deep Neural Network Explained | Artificial Neural Network | Simplilearn

  • 1. What’s in it for you? APPLICATIONS OF NEURAL NETWORK TYPES OF NEURAL NETWORK USE CASE - HOW DOES NEURAL NETWORK WORK? What is NEURAL NETWORK?
  • 2. What’s in it for you? TYPES OF NEURAL NETWORK USE CASE - to classify between the photos of dogs and cats HOW DOES NEURAL NETWORK WORK? What is NEURAL NETWORK? What can Neural network do?
  • 3. Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 What is a Neural Network?
  • 4. What is Neural Network? Hi Guys! I heard you want to know what Neural network is!
  • 5. What is Neural Network? As a matter of fact, you have been using Neural network on a daily basis!
  • 6. What is Neural Network? When you ask your mobile assistant to perform a search for you…..
  • 7. What is Neural Network? Self driving cars use it…
  • 8. What is Neural Network? Computer games use it too!
  • 9. What is Neural Network? Also used in processing the map images on your phone..
  • 10. What is Neural Network? Neural network is a system or hardware that is designed to operate like a human brain
  • 11. Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 What can Neural Network do?
  • 12. What can Neural Network do? Let’s list out the things Neural network can do for you..
  • 13. What can Neural Network do? Translate text
  • 14. What can Neural Network do? Identify faces Translate text
  • 15. What can Neural Network do? Identify faces Translate text Recognize speech
  • 16. What can Neural Network do? Identify faces Translate text Read handwritten text Recognize speech
  • 17. What can Neural Network do? Identify faces Recognize speech Translate text Read handwritten text Control robots
  • 18. What can Neural Network do? And a lot of other things…
  • 19. Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 How does a Neural Network work?
  • 20. How does a Neural Network work? Now we have come far enough to understand how Neural network works
  • 21. How does a Neural Network work? These are the different layers of a neural network
  • 22. How does a Neural Network work? Input layer Picks up input signals and passes them to the next LAYER
  • 23. How does a Neural Network work? Hidden layer Does all kinds of calculations and feature extraction
  • 24. How does a Neural Network work? Output layer This layer delivers the final result
  • 25. How does a Neural Network work? Now let’s make use of this neural network and see how it works!
  • 26. How does a Neural Network work? Wonder how traffic cameras identify vehicles Registration plate on the road to detect SpeedingVehicles and those breaking the LAW...!!
  • 27. How does a Neural Network work? 28*28 pixels of the image is FEd as input to identify the Registration plate Let’s consider the image of this vehicle and find out what’s on the number plate 28 28
  • 28. How does a Neural Network work? Each Neuron has a number called activation that represents the grayscale value of the corresponding pixel ranging from 0 to 1. 1 for white pixel and 0 for black pixel 28 28 0.82
  • 29. How does a Neural Network work? Each neuron is lit up when its activation is close to 1 28 28 0.82
  • 30. How does a Neural Network work? Pixels in the form of arrays are fed to the input layer.. 28 28 Pixels of car image fed as input
  • 31. How does a Neural Network work? Lets name the inputs x1, x2 and x3 respectively.. x1 x2 x3 28 28 Pixels of car image fed as input
  • 32. How does a Neural Network work? The input layer passes it to the hidden layer.. x1 x2 x3 28 28
  • 33. How does a Neural Network work? x1 x2 x3 28 28 The interconnections are assigned weights at random.. W11 W12 W13 W14 W15 W16 W17 W18
  • 34. How does a Neural Network work? x1 x2 x3 28 28 The weights are multiplied with the input signals and a bias is added to all of them.. i=1 n w x + b i i* W11 W12 W13 W14 W15 W16 W17 W18
  • 35. How does a Neural Network work? x1 x2 x3 28 28 i=1 n w x + b i i* The weighted sum of inputs is fed as input to the activation function to decide which nodes to fire for feature extraction.. W11 W12 W13 W14 W15 W16 W17 W18
  • 36. How does a Neural Network work? x1 x2 x3 28 28 i=1 n w x + b i i* As the signal flows within the hidden layers, the weighted sum of inputs is calculated and is fed to the activation function in each layer to decide which nodes to fireW11 W12 W13 W14 W15 W16 W17 W18 W21 W22 W23 W24 W25 W26 W27 W28 Feature extraction of the number plate
  • 37. How does a Neural Network work? Lets look at the different activation functions before we move ahead
  • 38. How does a Neural Network work? 0 1 Y (X)= 1 1 + e-x SIGMOID FUNCTION USED WHEN THE MODEL IS PREDICTING PROBABILITY i=1 n w x + b i i*
  • 39. How does a Neural Network work? 0 1 Y (X)= 1 1 + e-x SIGMOID FUNCTION USED WHEN THE MODEL IS PREDICTING PROBABILITY i=1 n w x + b i i* i= 1 n w x + b i i 0 1 Y (X)= 1, if x>=0 0, if x<0( ) USED WHEN THE OUTPUT DEPENDS ON A THRESHOLD VALUE * THRESHOLD FUNCTION
  • 40. How does a Neural Network work? 0 1 Y (X)= 1 1 + e-x SIGMOID FUNCTION USED WHEN THE MODEL IS PREDICTING PROBABILITY i=1 n w x + b i i* i= 1 n w x + b i i 0 1 Y (X)= 1, if x>=0 0, if x<0( ) USED WHEN THE OUTPUT DEPENDS ON A THRESHOLD VALUE * THRESHOLD FUNCTION i= 1 n w x + b i i* 0 1 Y (X) = max(X,0) relu FUNCTION It gives an output X if X is positive, 0 otherwise
  • 41. How does a Neural Network work? 0 1 Y (X)= 1 1 + e-x SIGMOID FUNCTION USED WHEN THE MODEL IS PREDICTING PROBABILITY i=1 n w x + b i i* i= 1 n w x + b i i 0 1 Y (X)= 1, if x>=0 0, if x<0( ) USED WHEN THE OUTPUT DEPENDS ON A THRESHOLD VALUE * THRESHOLD FUNCTION i= 1 n w x + b i i* 0 1 Y (X) = max(X,0) relu FUNCTION It gives an output X if X is positive, 0 otherwise Hyperbolic tangent function 0 1 Y (X)= 1 - e 1 + e-2x i=1 n w x + b i i* -1 -2x Similar to sigmoid function with a range of (- 1,1)
  • 42. How does a Neural Network work? Now that you know what is an activation function, lets get back to the neural network..
  • 43. How does a Neural Network work? x1 x2 x3 28 28 Finally, the model would predict the outcome by applying suitable activation function to the output layer Identifies the number on the plate W11 W12 W13 W14 W15 W16 W17 W18 W21 W22 W23 W24 W25 W26 W27 W28 W31 W32 W33 W34 08-D-03858 Optical Character Recognition (OCR) is used on the images to convert it into text in order to identify what’s written on the plate
  • 44. How does a Neural Network work? x1 x2 x3 28 28 W11 W12 W13 W14 W15 W16 W17 W18 W21 W22 W23 W24 W25 W26 W27 W28 W31 W32 W33 W34 Error in the output is backpropagated through the network and weights are adjusted to minimize the error rate.This is calculated by a cost function Identifies the number on the plate Backpropagation
  • 45. How does a Neural Network work? The output is compared with the original result and multiple iterations are done to get the maximum accuracy
  • 46. How does a Neural Network work? With every iteration, the weights at every interconnections are adjusted based on the error…
  • 47. Brief History of Artificial Intelligence The word ‘Artificial Intelligence’ coined by John McCarthy ‘Shakey’ was the first general purpose mobile robot built Supercomputer ‘Deep blue’ was designed which defeated the world Chess champion in a game First commercially successful robotic vacuum cleaner created Speech recognition, RPA, dancing robots, smart homes and many more to come from AI 1956 1969 1997 2002 2005-2018 Types of Artificial Neural Network
  • 48. Types of Artificial Neural Network Lets see the different types of neural network
  • 49. Types of Artificial Neural Network Feedforward neural network Simplest form of ANN. Data travels only in one direction(input - output)
  • 50. Types of Artificial Neural Network Feedforward neural network Simplest form of ANN. Data travels only in one direction(input - output) Applications Vision and speech recognition
  • 51. Types of Artificial Neural Network Radial basis function neural network This model classifies the data point based on its distance from a center point
  • 52. Types of Artificial Neural Network Radial basis function neural network This model classifies the data point based on its distance from a center point Applications Power restoration systems
  • 53. Types of Artificial Neural Network Kohonen self organizing neural network Vectors of random dimensions are input to discrete map comprised of neurons
  • 54. Types of Artificial Neural Network Kohonen self organizing neural network Vectors of random dimensions are input to discrete map comprised of neurons Used to recognize patterns in data like in medical analysis Applications
  • 55. Types of Artificial Neural Network Recurrent neural network The hidden layer saves its output to be used for future prediction
  • 56. Types of Artificial Neural Network Recurrent neural network The hidden layer saves its output to be used for future prediction Text to speech conversion model Applications
  • 57. Types of Artificial Neural Network convolution neural network The input features are taken in batches like a filter.This allows the network to remember an image in parts!
  • 58. Types of Artificial Neural Network convolution neural network The input features are taken in batches like a filter.This allows the network to remember an image in parts! Used in signal and image processing Applications
  • 59. Types of Artificial Neural Network modular neural networkIt has a collection of different neural networks working together to get the output
  • 60. Types of Artificial Neural Network modular neural networkIt has a collection of different neural networks working together to get the output Still undergoing research Applications
  • 61. Types of Artificial Neural Network Feedforward neural network Radial basis function neural network Kohonen self organizing neural network Recurrent neural network convolution neural network modular neural network
  • 62. Use Case – Problem Statement Let’s use the system to identify between a cat and a dog!
  • 63. Use Case – Problem Statement Classify photos of cats and dogs using Neural Network Problem statement
  • 64. Use Case – Problem Statement The use case will be implemented on python
  • 65. Use Case – Implementation #Import the required packages from keras.models import Sequential from keras.layers import Conv2D from keras.layers import MaxPooling2D from keras.layers import Flatten from keras.layers import Dense
  • 66. Use Case – Implementation # Initialising the CNN classifier = Sequential() # Convolution classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu’)) # Pooling classifier.add(MaxPooling2D(pool_size = (2, 2))) # Adding a second convolutional layer classifier.add(Conv2D(32, (3, 3), activation = 'relu')) classifier.add(MaxPooling2D(pool_size = (2, 2)))
  • 67. Use Case – Implementation # Flattening classifier.add(Flatten()) # Full connection classifier.add(Dense(units = 128, activation = 'relu')) classifier.add(Dense(units = 1, activation = 'sigmoid’)) # Compiling the CNN classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
  • 68. Use Case – Implementation # Fitting the CNN to the images test_datagen = ImageDataGenerator(rescale = 1./255) training_set = train_datagen.flow_from_directory('dataset/training_set', target_size = (64, 64), batch_size = 32, class_mode = 'binary')
  • 69. Use Case – Implementation # Fitting the CNN to the images test_set = test_datagen.flow_from_directory('dataset/test_set', target_size = (64, 64), batch_size = 32, class_mode = 'binary')
  • 70. Use Case – Implementation # Training the model classifier.fit_generator(training_set, steps_per_epoch = 8000, epochs = 1, validation_data = test_set, validation_steps = 2000)
  • 71. Use Case – Implementation # Part 3 - Making new predictions import numpy as np from keras.preprocessing import image test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size = (64, 64)) test_image = image.img_to_array(test_image) test_image = np.expand_dims(test_image, axis = 0) result = classifier.predict(test_image) training_set.class_indices if result[0][0] == 1: prediction = 'dog' else: prediction = 'cat’
  • 72. Use Case – Implementation # Part 3 - Making new predictions prediction
  • 73. Use Case – Implementation So, we have successfully built a neural network that can distinguish between photos of a cat and a dog..
  • 74. Use Case – Implementation The accuracy of the model is around 80% which is quite good…

Editor's Notes