Dog Vs Cat Prediction | Image Classification | CIFAR10
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#neuralnetworks #tensorflow #keras
we used TensorFlow with Keras to create our magical model and trained it on the Cidar10 dataset. The magical model can now recognize cute animals in pictures with pretty good accuracy, just like magic! 🦄🐶🐱🐰🐢
image classification, sequential neural network.
This is a classic AI task that involves training a model to classify images into different categories. For example, you could train a model to classify images of cats and dogs, or flowers and trees. This is a good beginner project because it is relatively simple to understand and implement.
This code will load the CIFAR-10 dataset, which contains images of cats and dogs. It will then create a model with two hidden layers, and train it for 10 epochs. Finally, it will evaluate the model on the test set.
Here is a real-world scenario where this AI task could be used:
A company that sells pet supplies could use this AI to classify images of pets that are uploaded to their website. This would help them to automatically categorize the images and make it easier for customers to find the products they are looking for.
A wildlife conservation organization could use this AI to classify images of animals in the wild. This would help them to track the population of different species and identify areas where conservation efforts are needed.
What is loss='sparse_categorical_crossentropy'?
Loss='sparse_categorical_crossentropy' is a loss function that is commonly used in machine learning algorithms to train classification models. It measures the difference between the predicted probabilities of a model and the actual labels. The lower the loss, the better the model is at predicting the labels.
How does it work?
Let's say we have a model that is trying to classify images of cats and dogs. The model will output a probability for each class (cat or dog). The loss function will calculate the difference between the predicted probabilities and the actual labels. For example, if the model predicts that an image is a cat with a probability of 0.9 and the actual label is "dog", then the loss will be high.
Cross-entropy is a loss function that is commonly used in machine learning algorithms to train classification models. It measures the difference between the predicted probabilities of a model and the actual labels. The lower the loss, the better the model is at predicting the labels.
let's say we have a model that is trying to classify images of cats and dogs. The model will output a probability for each class (cat or dog). The cross-entropy loss function will calculate the difference between the predicted probabilities and the actual labels. For example, if the model predicts that an image is a cat with a probability of 0.9 and the actual label is "dog", then the loss will be high.
The model will be trained to minimize the cross-entropy loss. This means that the model will learn to output probabilities that are closer to the actual labels. As the model learns, the cross-entropy loss will decrease.
In Keras, model.compile is a method that is used to configure a model for training. The method takes three arguments: the loss function, the optimizer, and the metrics.
The model.compile method is a necessary step before training a model. It is important to choose the right loss function, optimizer, and metrics for the specific problem that you are trying to solve.
Finally
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