import tensorflow as tf

# hyperparameters

n_neurons = 128

learning_rate = 0.001

batch_size = 128

n_epochs = 10

# parameters

n_steps = 28 # 28 rows

n_inputs = 28 # 28 cols

n_outputs = 10 # 10 classes

# build a rnn model

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

y = tf.placeholder(tf.int32, [None])

cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)

output, state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)

logits = tf.layers.dense(state, n_outputs)

cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)

loss = tf.reduce_mean(cross_entropy)

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)

prediction = tf.nn.in_top_k(logits, y, 1)

accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))

# input data

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/")

X_test = mnist.test.images # X_test shape: [num_test, 28*28]

X_test = X_test.reshape([-1, n_steps, n_inputs])

y_test = mnist.test.labels

# initialize the variables

init = tf.global_variables_initializer()

# train the model

with tf.Session() as sess:

sess.run(init)

n_batches = mnist.train.num_examples // batch_size

for epoch in range(n_epochs):

for batch in range(n_batches):

X_train, y_train = mnist.train.next_batch(batch_size)

X_train = X_train.reshape([-1, n_steps, n_inputs])

sess.run(optimizer, feed_dict={X: X_train, y: y_train})

loss_train, acc_train = sess.run(

[loss, accuracy], feed_dict={X: X_train, y: y_train})

print('Epoch: {}, Train Loss: {:.3f}, Train Acc: {:.3f}'.format(

epoch + 1, loss_train, acc_train))

loss_test, acc_test = sess.run(

[loss, accuracy], feed_dict={X: X_test, y: y_test})

print('Test Loss: {:.3f}, Test Acc: {:.3f}'.format(loss_test, acc_test))