├── Example Code python
└── README.md


/Example Code python:
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 1 | # Example of training a neural network for image classification with TensorFlow
 2 | import tensorflow as tf
 3 | from tensorflow.keras import datasets, layers, models
 4 | 
 5 | # Load CIFAR-10 dataset
 6 | (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
 7 | 
 8 | # Normalize data
 9 | train_images, test_images = train_images / 255.0, test_images / 255.0
10 | 
11 | # Define CNN model
12 | model = models.Sequential([
13 |     layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
14 |     layers.MaxPooling2D((2, 2)),
15 |     layers.Conv2D(64, (3, 3), activation='relu'),
16 |     layers.MaxPooling2D((2, 2)),
17 |     layers.Conv2D(64, (3, 3), activation='relu'),
18 |     layers.Flatten(),
19 |     layers.Dense(64, activation='relu'),
20 |     layers.Dense(10)
21 | ])
22 | 
23 | # Compile and train the model
24 | model.compile(optimizer='adam',
25 |               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
26 |               metrics=['accuracy'])
27 | 
28 | history = model.fit(train_images, train_labels, epochs=10, 
29 |                     validation_data=(test_images, test_labels))
30 | 
31 | # Evaluate model accuracy
32 | test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
33 | print(f'Test accuracy: {test_acc}')
34 | 


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/README.md:
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1 | # Machine-Learning-Project-with-TensorFlow-in-Python
2 | This repository includes a machine learning project using TensorFlow for image classification
3 | 


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