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


mnist = input_data.read_data_sets("MNIST_data",one_hot=True)


#hyperparameters
lr = 0.001
training_iters = 100000
batch_size = 128
display_step = 10

n_inputs = 28 #MNIST data input(image size 28*28)
n_steps = 28 # time step
n_hidden_unis = 128 #neurons in hidden layer
n_classes = 10 #MNIST classes(0-9)


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


#define weights
Weights = {
    # 28 x 128
    'in':tf.Variable(tf.random_normal([n_inputs,n_hidden_unis])),
    # 128 x 10
    'out':tf.Variable(tf.random_normal([n_hidden_unis,n_classes]))
}


biases = {
    # 128
    'in':tf.Variable(tf.constant(0.1,shape=[n_hidden_unis,])),
    #10
    'out':tf.Variable(tf.constant(0.1,shape=[n_classes,]))
}

def RNN(X,weights,biases):
    #hidden layer for input to cell
    #X   (128 batch x 28 steps x 28 inputs)
    # ==> (128*28,28)
    X = tf.reshape(X,[-1,n_inputs])
    X_in = tf.matmul(X,weights['in']) + biases['in']

    X_in = tf.reshape(X_in,[-1,n_steps,n_hidden_unis])
    #cell
    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_unis,forget_bias=1.0)
    _init_state = lstm_cell.zero_state(batch_size,tf.float32)
    outputs,states = tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=_init_state,time_major=False)
    #hidden layer for output as the final results
    # results = tf.matmul(states[1],weights['out']) + biases['out']
    # or
    outputs = tf.unpack(tf.transpose(outputs,[1,0,2]))
    results = tf.matmul(outputs[-1],weights['out']) + biases['out']

    return results


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

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


init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)
    step = 0
    test_images = mnist.test.images
    print type(test_images)
    test_labels = mnist.test.labels
    test_images = test_images.reshape([-1,n_steps,n_inputs])

    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:test_images,y:test_labels})
        step = step + 1