Tensorflow-Test-Accuracy.py

import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.optimizers import SGD

# Load the MNIST dataset
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Normalize the images
train_images = train_images / 255.0
test_images = test_images / 255.0

# Create a sequential model
model = Sequential([
    Flatten(input_shape=(28, 28)),  # Flatten the input
    Dense(128, activation='relu'),  # First dense layer
    Dense(10, activation='softmax') # Output layer
])

# Compile the model
model.compile(optimizer=SGD(),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 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('Test accuracy:', test_acc)