cnn in python keras
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convolutional neural networks (cnns) are a powerful class of deep learning models, particularly effective for image recognition tasks. in this tutorial, we'll guide you through building a simple cnn using the keras library in python.
ensure you have python installed on your system. you can install the required libraries using pip:
open your python environment (e.g., jupyter notebook, python script) and start by importing the necessary libraries:
for this tutorial, we'll use the mnist dataset, a collection of 28x28 grayscale images of handwritten digits (0-9). keras provides a convenient way to load it:
now, let's create a simple cnn model:
compile the model by specifying the optimizer, loss function, and metrics:
train the model using the training data:
evaluate the model on the test set:
use the trained model to make predictions:
visualize some predictions and compare them with the actual labels:
this tutorial provides a basic introduction to building a cnn using keras in python. experiment with different architectures, hyperparameters, and datasets to enhance your understanding of cnns and their applications in computer vision.
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