# -*- coding: utf-8 -*-

# Recurrent Neural Network (RNN)

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

# load data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

# hyper parameters
lr = 0.001
training_iters = 100000
batch_size = 128
#display_step = 10

n_inputs = 28   # mnist data input (img shape: 28*28)
n_steps = 28    # time steps
n_hidden_units = 128    # neurons in hidden layer
n_classes = 10  # mnist classes (0-9 digits)

# tf graph input
x = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
y = tf.placeholder(tf.float32, [None, n_classes])

# define weights & biases
weights = {
        # (28, 128)
        'in': tf.Variable(tf.random_normal([n_inputs, n_hidden_units])),
        # (128, 10)
        'out': tf.Variable(tf.random_normal([n_hidden_units, n_classes]))
        }
biases = {
        # (128,)
        'in': tf.Variable(tf.constant(0.1, shape=[n_hidden_units, ])),
        'out': tf.Variable(tf.constant(0.1, shape=[n_classes, ]))
        }


### define RNN ###
def RNN(X, weights, biases):
    ## hidden layer for input to cell
    # X ( 128 batch, 28 steps, 28 inputs ) -> ( 128 * 28, 28 inputs )
    X = tf.reshape(X, [-1, n_inputs])
    X_in = tf.matmul(X, weights['in']) + biases['in']
    # X_in -> ( 128 batch * 28 steps, 128 hidden )
    X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units])
    
    ## cell
    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(
            num_units=n_hidden_units, forget_bias=1.0, state_is_tuple=True)
    # LSTM cell is divided into 2 parts ( c_state, m_state )
    _init_state = lstm_cell.zero_state(batch_size, tf.float32)
    outputs, states = tf.nn.dynamic_rnn(
            cell=lstm_cell, inputs=X_in, initial_state=_init_state, time_major=False)
    
    ## hidden layer for outputs as results
    results = tf.matmul(states[1], weights['out']) + biases['out']
    return results
    

pred = RNN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred))
train_op = tf.train.AdamOptimizer(lr).minimize(cost)

correct_pred = tf.equal(tf.argmax(pred, axis=1), tf.argmax(y, axis=1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# start session
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    step = 0
    while step * batch_size < training_iters:
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        batch_xs = batch_xs.reshape([batch_size, n_steps, n_inputs])
        sess.run([train_op], feed_dict={x:batch_xs, y:batch_ys})
        if step % 20 == 0:
            print(sess.run(accuracy, feed_dict={x:batch_xs, y:batch_ys}))
        step += 1
