Now, we'll define a loop that iterates num_iterations times. And for each loop, it runs training, feeding in values from input_values_train and target_values_train using feed_dict.
In order to calculate accuracy, it will test the model against the unseen data in input_values_test :
for i in range(num_iterations+1): sess.run(train, feed_dict={input_values: input_values_train, output_values: target_values_train}) if i%100 == 0: print('Training Step:' + str(i) + ' Accuracy = ' + str(sess.run(model_accuracy, feed_dict={input_values: input_values_test, output_values: target_values_test})) + ' Loss = ' + str(sess.run(model_cross_entropy, {input_values: input_values_train, output_values: target_values_train})))Output:Training Step:0 ...
