import numpy as np

from tensorflow.keras import utils

from tensorflow.keras.datasets import cifar10

from tensorflow.keras import layers, models

​

# Load the CIFAR-10 dataset

(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()

​

# Normalize pixel values between 0 and 1

train_images = train_images.astype('float32') / 255.0

test_images = test_images.astype('float32') / 255.0

​

# Convert labels to one-hot encoding

num_classes = 10

train_labels = utils.to_categorical(train_labels, num_classes)

test_labels = utils.to_categorical(test_labels, num_classes)

​

# Define the CNN model

model = models.Sequential([

    # Convolutional layers

    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(),

    # Fully connected layers

    layers.Dense(64, activation='relu'),

    layers.Dense(num_classes, activation='softmax')

])

​

# Compile the model

model.compile(optimizer='adam',

              loss=tf.keras.losses.CategoricalCrossentropy(),

              metrics=['accuracy'])

​

# Train the model

model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

​

# Evaluate the model

test_loss, test_acc = model.evaluate(test_images, test_labels)

print('Test accuracy:', test_acc)

import numpy as np

import tensorflow as tf

from tensorflow.keras import layers

from tensorflow.keras.datasets import cifar10

from tensorflow.keras import utils

​

# Load the CIFAR-10 dataset

(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()

​

# Normalize pixel values between 0 and 1

train_images = train_images.astype('float32') / 255.0

test_images = test_images.astype('float32') / 255.0

​

# Convert labels to one-hot encoding

num_classes = 10

train_labels = utils.to_categorical(train_labels, num_classes)

test_labels = utils.to_categorical(test_labels, num_classes)

​

# Define the CNN model

model = tf.keras.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(num_classes, activation='softmax')

])

​

# Compile the model

model.compile(optimizer='adam',

              loss=tf.keras.losses.CategoricalCrossentropy(),

              metrics=['accuracy'])

​

# Train the model

model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

​

# Evaluate the model

test_loss, test_acc = model.evaluate(test_images, test_labels)

print('Test accuracy:', test_acc)