TensorFlow

TensorFlow for machine learning

To compare the performance of TensorFlow on the Raspberry Pi 4 and the Arm cloud server, install it and run an example.

Install pip and python3.

    

        
        
            sudo apt install python-is-python3 python3-pip -y
        
    

Install TensorFlow.

    

        
        
            pip install tensorflow-aarch64 tensorflow_io 
        
    

You can now follow the instructions in the TensorFlow quickstart example or proceed to the steps in the Quickstart example below.

Quickstart example

To save time entering the commands from the TensorFlow example, the code is shared here.

Using a text editor of your choice, copy the contents below and save it in a file named example.py:

    

        
        
            import tensorflow as tf
print("TensorFlow version:", tf.__version__)

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])

predictions = model(x_train[:1]).numpy()
predictions

tf.nn.softmax(predictions).numpy()

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

model.compile(optimizer='adam',
              loss=loss_fn,
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)

model.evaluate(x_test,  y_test, verbose=2)

probability_model = tf.keras.Sequential([
  model,
  tf.keras.layers.Softmax()
])

probability_model(x_test[:5])
        
    

Run the example using python.

    

        
        
            time python ./example.py
        
    

Results

The results below show the time taken to run the tensorflow example on the Raspberry Pi and the Arm server. This gives you an idea of the relative performance.