import tensorflow  as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data

n_steps = 28
n_inputs = 28
n_neurons = 150
n_outputs = 10
lr = 0.001

mnist = input_data.read_data_sets('/tmp/data/')
x_test = mnist.test.images.reshape((-1, n_steps, n_inputs))
y_test = mnist.test.labels






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

basic_cell = tf.contrib.rnn.BasicRNNCell(num_units = n_neurons)
outputs, states = tf.nn.dynamic_rnn(basic_cell, x, dtype = tf.float32)

logits = tf.layers.dense(states, n_outputs)
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels = y, logits = logits)

loss = tf.reduce_mean(xentropy)
optimizer = tf.train.AdamOptimizer(learning_rate = lr)
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()

e = 100
batch = 150

with tf.Session() as sess:
	init.run()
	for epoch in range(e):
		for iteration in range(mnist.train.num_examples // batch):
			x_batch, y_batch = mnist.train.next_batch(batch)
			x_batch = x_batch.reshape((-1, 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_tess = accuracy.eval(feed_dict = {x: x_test, y: y_test})
		print(epoch, acc_train, acc_tess)


