The following python code is RNN with the long short-term memory (BasicLSTMCell) for the MNIST data:

import tensorflow as tf

n_steps = 28 n_inputs = 28 n_neurons = 150 n_outputs = 10 n_layers = 3

learning_rate = 0.001

X = tf.placeholder(tf.float32, [None, n_steps, n_inputs]) y = tf.placeholder(tf.int32, [None])

lstm_cells = [tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons) for layer in range(n_layers)] multi_cell = tf.contrib.rnn.MultiRNNCell(lstm_cells) outputs, states = tf.nn.dynamic_rnn(multi_cell, X, dtype=tf.float32) top_layer_h_state = states[-1][1] logits = tf.layers.dense(top_layer_h_state, n_outputs, name="softmax") xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) loss = tf.reduce_mean(xentropy, name="loss") optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) training_op = optimizer.minimize(loss) correct = tf.nn.in_top_k(logits, y, 1) accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) init = tf.global_variables_initializer()

from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/") X_test = mnist.test.images.reshape((-1, n_steps, n_inputs)) y_test = mnist.test.labels

n_epochs = 10 batch_size = 150

with tf.Session() as sess: init.run() for epoch in range(n_epochs): for iteration in range(mnist.train.num_examples // batch_size): X_batch, y_batch = mnist.train.next_batch(batch_size) X_batch = X_batch.reshape((batch_size, n_steps, n_inputs)) sess.run(training_op, feed_dict={X: X_batch, y: y_batch}) acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch}) acc_test = accuracy.eval(feed_dict={X: X_test, y: y_test}) print("Epoch", epoch, "Train accuracy =", acc_train, "Test accuracy =", acc_test)

Replace BasicLSTMCell with GRUCell. Run the program and submit a screenshot of the execution and result.