Let's first import the Tensorflow package and give it a short name 'tf'
import tensorflow as tf
The handwritten images cal be loaded in a variable by following code
mnist = tf.keras.datasets.mnist
The data can be divided into two parts- training and test and can be normalized
(x_train, y_train),(x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0
Now we can build a sequential model that will be used to train the network. Also a dropout can be added so that the network doesn't remember the data bilndly
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, activation='softmax') ])
No the model can be compiled and define the loss function
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
Now the model can be trained using the trained data and the prediction accuracy can be computed on the test data.
model.fit(x_train, y_train, epochs=5) model.evaluate(x_test, y_test)