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+--- Thread: Deep Learning with TensorFlow and Keras (/Thread-Deep-Learning-with-TensorFlow-and-Keras)
Deep Learning with TensorFlow and Keras - vluzzy - 01-02-2024
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
# Build a simple neural network for image classification
model = keras.Sequential([
layers.Flatten(input_shape=(28, 28)),
layers.Dense(128, activation='relu'),
layers.Dense(10, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# Load and preprocess the MNIST dataset
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images, test_images = train_images / 255.0, test_images / 255.0
# Train the model
model.fit(train_images, train_labels, epochs=5)
# Evaluate the model
test_loss, test_acc = model.evaluate(test_images, test_labels)
print(f"Test accuracy: {test_acc}")