import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

#this is data

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

lr = 0.001

train_iters = 10000

batch_size = 128

display_step = 10

n_inputs = 28

n_steps = 28

n_hidden_unis = 128

n_classes = 10

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

y = tf.placeholder(tf.float32,[None,n_classes])

#define weight

weights = {

    #(28,128)

    "in":tf.Variable(tf.random_normal([n_inputs,n_hidden_unis])),

    #(128,10)

    "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):

    #形状变换成lstm可以训练的维度

    X = tf.reshape(X,[-1,n_inputs])     #(128*28,28)

    X_in = tf.matmul(X,weights["in"])+biases["in"]  #(128*28,128)

    X_in = tf.reshape(X_in,[-1,n_steps,n_hidden_unis]) #(128,28,128)

    #cell

    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_unis,forget_bias=1.0,state_is_tuple=True)

    #lstm cell is divided into two parts(c_state,m_state)

    _init_state = lstm_cell.zero_state(batch_size,dtype=tf.float32)

    outputs,states = tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=_init_state,time_major = False)

    #outputs

    # results = tf.matmul(states[1],weights["out"])+biases["out"]

    #or

    outputs = tf.transpose(outputs,[1,0,2])

    results = tf.matmul(outputs[-1],weights["out"])+biases["out"]

    return results

pred = RNN(x,weights,biases)

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))

train_op = tf.train.AdamOptimizer(lr).minimize(loss)

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

    while step*batch_size < train_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}))