Hands-On Deep Learning Algorithms with Python

MNIST digit classification using TensorFlow 2.0

Now, we will see how we can perform MNIST handwritten digit classification, using TensorFlow 2.0. It requires only a few lines of code compared to TensorFlow 1.x. As we have learned, TensorFlow 2.0 uses Keras as its high-level API; we just need to add tf.keras to the Keras code.

Let's start by loading the dataset:

mnist = tf.keras.datasets.mnist

Create a train and test set with the following code:

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

Normalize the train and test sets by dividing the values of x by the maximum value of x; that is, 255.0:

x_train, x_test = tf.cast(x_train/255.0, tf.float32), tf.cast(x_test/255.0, tf.float32)y_train, y_test = tf.cast(y_train,tf.int64),tf.cast(y_test,tf.int64) ...