cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_units)  

init_state = cell.zero_state(batch_size, dtype=tf.float32)

outputs, final_state = tf.nn.dynamic_rnn(cell, X_in, initial_state=init_state, time_major=False)

outputs為【80,28,128】       80為batch_size   28為steps

final_state形狀為【2,80,128】，包括（c_n,h_n），其中c_n為長線記憶【80,128】，h_n為短線記憶【80,128】

results = tf.matmul(final_state[1], weights['out']) + biases['out']

舊的例子：

# View more python learning tutorial on my Youtube and Youku channel!!!

# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg
# Youku video tutorial: http://i.youku.com/pythontutorial

"""
This code is a modified version of the code from this link:
https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py
His code is a very good one for RNN beginners. Feel free to check it out.
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# set random seed for comparing the two result calculations
tf.set_random_seed(1)

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

# hyperparameters
lr = 0.001
training_iters = 100000
batch_size = 80

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
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, ])),
    # (10, )
    'out': tf.Variable(tf.constant(0.1, shape=[n_classes, ]))
}


def RNN(X, weights, biases):
    # hidden layer for input to cell
    # transpose the inputs shape from
    # X (80,28,28) ==> (80 batch * 28 steps, 28 inputs)
    X = tf.reshape(X, [-1, n_inputs])

    # into hidden
    # X_in = (80 batch * 28 steps, 128 hidden)
    X_in = tf.matmul(X, weights['in']) + biases['in']
    # X_in ==> (80 batch, 28 steps, 128 hidden)
    X_in = tf.reshape(X_in, [-1, n_steps, n_hidden_units])


    # basic LSTM Cell. 
    cell = tf.contrib.rnn.BasicLSTMCell(n_hidden_units)   #128
    # lstm cell is divided into two parts (c_state, h_state)
    init_state = cell.zero_state(batch_size, dtype=tf.float32)

    # 下一步有兩個選擇
    # 1: tf.nn.rnn(cell, inputs);
    # 2: tf.nn.dynamic_rnn(cell, inputs).
    # If use option 1, you have to modified the shape of X_in, go and check out this:
    # https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py
    # In here, we go for option 2.
    # dynamic_rnn的輸入X_in為 (batch, steps, inputs) 或 (steps, batch, inputs)
    # 這裡為 (batch, steps, inputs)
    # time_major的意思是：是否steps為第一個引數，這裡不是，則false.
    outputs, final_state = tf.nn.dynamic_rnn(cell, X_in, initial_state=init_state, time_major=False)
    
    #############################################
    #print(sess.run(tf.shape(outputs)))      #[ 80  28 128]
    #print(sess.run(tf.shape(final_state)))  # [ 2  80 128]
    #final_state包括(c_state, h_state)長線記憶和短線記憶
    #############################################
    
    # hidden layer for output as the final results
    #第一種方法
    #final_state為[2,80,128]     則final_state[1]為[80,128] 
    #results = tf.matmul(final_state[1], weights['out']) + biases['out']
    #第二種方法
    #outputs = tf.transpose(outputs, [1,0,2])   這條語句也可以的
    outputs = tf.unstack(tf.transpose(outputs, [1,0,2]))
    results = tf.matmul(outputs[-1], weights['out']) + biases['out']    # shape = (128, 10)

    return results


pred = RNN(x, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=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))

with tf.Session() as sess:
    init = tf.global_variables_initializer()
    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

 

新的例子：

"""
Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/
My Youtube Channel: https://www.youtube.com/user/MorvanZhou

Dependencies:
tensorflow: 1.1.0
matplotlib
numpy
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt

tf.set_random_seed(1)
np.random.seed(1)

# Hyper Parameters
BATCH_SIZE = 80
TIME_STEP = 28          # rnn time step / image height
INPUT_SIZE = 28         # rnn input size / image width
LR = 0.01               # learning rate

# data
mnist = input_data.read_data_sets('./mnist', one_hot=True)              # they has been normalized to range (0,1)
test_x = mnist.test.images[:2000]
test_y = mnist.test.labels[:2000]
print(mnist.train.images.shape)     # (55000, 28 * 28)
print(mnist.train.labels.shape)     # (55000, 10)


# tensorflow placeholders
tf_x = tf.placeholder(tf.float32, [None, TIME_STEP * INPUT_SIZE])       # shape(batch, 784)
image = tf.reshape(tf_x, [-1, TIME_STEP, INPUT_SIZE])                   # (batch, height, width, channel)
tf_y = tf.placeholder(tf.int32, [None, 10])                             # input y

# RNN
rnn_cell = tf.contrib.rnn.BasicLSTMCell(num_units=64)
outputs, (h_c, h_n) = tf.nn.dynamic_rnn(
    rnn_cell,                   # cell you have chosen
    image,                      # input
    initial_state=None,         # the initial hidden state
    dtype=tf.float32,           # must given if set initial_state = None
    time_major=False,           # False: (batch, time step, input); True: (time step, batch, input)
)
output = tf.layers.dense(outputs[:, -1, :], 10)              # output based on the last output step

loss = tf.losses.softmax_cross_entropy(onehot_labels=tf_y, logits=output)           # compute cost
train_op = tf.train.AdamOptimizer(LR).minimize(loss)

accuracy = tf.metrics.accuracy(          # return (acc, update_op), and create 2 local variables
    labels=tf.argmax(tf_y, axis=1), predictions=tf.argmax(output, axis=1),)[1]

sess = tf.Session()
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) # the local var is for accuracy_op
sess.run(init_op)     # initialize var in graph

for step in range(1200):    # training
    b_x, b_y = mnist.train.next_batch(BATCH_SIZE)
    _, loss_ = sess.run([train_op, loss], {tf_x: b_x, tf_y: b_y})
    if step % 50 == 0:      # testing
        accuracy_ = sess.run(accuracy, {tf_x: test_x, tf_y: test_y})
        print('train loss: %.4f' % loss_, '| test accuracy: %.2f' % accuracy_)

# print 10 predictions from test data
test_output = sess.run(output, {tf_x: test_x[:10]})
pred_y = np.argmax(test_output, 1)
print(pred_y, 'prediction number')
print(np.argmax(test_y[:10], 1), 'real number')