input: Write a function to generate a neural network model using TensorFlow and Python for classifying hand-written digits from the MNIST dataset. The function should print the accuracy of the model on the test dataset after training.
  assert generate_mnist_model()=="Optimization Finished!\nAccuracy: 0.9"
  assert generate_mnist_model()=="Optimization Finished!\nAccuracy: 0.92"
  assert generate_mnist_model()=="Optimization Finished!\nAccuracy: 0.88"
  assert generate_mnist_model()=="Optimization Finished!\nAccuracy: 0.91"
  assert generate_mnist_model()=="Optimization Finished!\nAccuracy: 0.89"
output: def generate_mnist_model():
      import tensorflow as tf
      from tensorflow.keras import layers, models
      from tensorflow.keras.datasets import mnist
      from tensorflow.keras.utils import to_categorical
  
      (x_train, y_train), (x_test, y_test) = mnist.load_data()
      x_train = x_train.reshape((60000, 28, 28, 1)).astype('float32') / 255
      x_test = x_test.reshape((10000, 28, 28, 1)).astype('float32') / 255
      y_train = to_categorical(y_train)
      y_test = to_categorical(y_test)
  
      model = models.Sequential([
          layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
          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(10, activation='softmax')
      ])
  
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      model.fit(x_train, y_train, epochs=5, batch_size=64, validation_split=0.1)
  
      test_loss, test_acc = model.evaluate(x_test, y_test)
      return f"Optimization Finished!\nAccuracy: {test_acc:.2f}"