of images through the networkwe print out the loss and accuracy of that batch note that we should not be expecting decreasing loss and increasing accuracy hereas the values are per batchnot for the entire model we use mini-batches of images rather than feeding them through individually to speed up the training process and allow the network to see number of different examples before updating the parameters once the training is completewe can run the session on the test images this time we are using keep_prob dropout rate to ensure all units are active in the testing process add this code to the filemain py test_accuracy sess run(accuracyfeed_dict={xmnist test imagesymnist test labelskeep_prob }print("\naccuracy on test set:"test_accuracyit' now time to run our program and see how accurately our neural network can recognize these handwritten digits save the main py file and execute the following command in the terminal to run the script(tensorflow-demopython main py you'll see an output similar to the followingalthough individual loss and accuracy results may vary slightlyoutput