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 What is Keras?

Keras is a popular and user-friendly open-source library for building and training neural networks. It is widely used in the field of machine learning and deep learning. Think of Keras as a powerful toolkit that helps you create models that can learn from data and make predictions or decisions.

Keras is designed to be simple and easy to use, making it accessible even for beginners. It provides a high-level interface for defining and training complex neural networks, allowing you to focus on designing your model rather than getting bogged down in complex code. With Keras, you can quickly build models for tasks like image recognition, language processing, and more.

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Training a Convolutional Neural Network on CIFAR-10 Using Keras

import tensorflow as tf from tensorflow.keras import layers, models from tensorflow.keras.datasets import cifar10 from tensorflow.keras.utils import to_categorical # Load and preprocess CIFAR-10 dataset (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 # Normalize images y_train, y_test = to_categorical(y_train, 10), to_categorical(y_test, 10) # One-hot encode labels # Build the CNN model model = models.Sequential([ layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, (3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, (3, 3), activation='relu'), layers.Flatten(), layers.Dense(64, activation='relu'), layers.Dense(10, activation='softmax') ]) # Compile the model model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) # Train the model history = model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test)) # Evaluate the model test_loss, test_acc = model.evaluate(x_test, y_test)

This code helps students understand the basics of building a Convolutional Neural Network, including layers like Conv2D and MaxPooling2D, and how to stack them to form a network. Students learn how to preprocess image data by normalizing and one-hot encoding, which is crucial for training neural networks effectively.

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