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tensorflow.py
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tensorflow.py
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# Import TensorFlow library
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Flatten, Dense
# Load the MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Preprocess the data
x_train, x_test = x_train / 255.0, x_test / 255.0
# Build a simple neural network model
model = Sequential([
Flatten(input_shape=(28, 28)),
Dense(128, activation='relu'),
Dense(10, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Train the model
model.fit(x_train, y_train, epochs=5)
# Evaluate the model
loss, accuracy = model.evaluate(x_test, y_test)
print(f'Test accuracy: {accuracy}')