RNN train, test 스텝

rnn_minist_04_train_and_test.py

# Merge all the summaries
merged = tf.summary.merge_all()

# Get a small test set
test_data = mnist.test.images[:batch_size].reshape((-1, time_steps, element_size))
test_label = mnist.test.labels[:batch_size]

with tf.Session() as sess:
    # Write summaries to LOG_DIR -- used by TensorBoard
    train_writer = tf.summary.FileWriter(LOG_DIR + '/train',
                                         graph=tf.get_default_graph())
    test_writer = tf.summary.FileWriter(LOG_DIR + '/test',
                                        graph=tf.get_default_graph())

    sess.run(tf.global_variables_initializer())

    for i in range(10000):

            batch_x, batch_y = mnist.train.next_batch(batch_size)
            # Reshape data to get 28 sequences of 28 pixels
            batch_x = batch_x.reshape((batch_size, time_steps, element_size))
            summary, _ = sess.run([merged, train_step],
                                  feed_dict={_inputs: batch_x, y: batch_y})
            # Add to summaries
            train_writer.add_summary(summary, i)

            if i % 1000 == 0:
                acc, loss, = sess.run([accuracy, cross_entropy],
                                      feed_dict={_inputs: batch_x,y: batch_y})
                print("Iter " + str(i) + ", Minibatch Loss= " +
                      "{:.6f}".format(loss) + ", Training Accuracy= " +
                      "{:.5f}".format(acc))
            if i % 100 == 0:
                # Calculate accuracy for 128 mnist test images and
                # add to summaries
                summary, acc = sess.run([merged, accuracy],
                                        feed_dict={_inputs: test_data,y: test_label})
                test_writer.add_summary(summary, i)

    test_acc = sess.run(accuracy, feed_dict={_inputs: test_data,y: test_label})
    print("Test Accuracy:", test_acc)