转自：http://blog.topspeedsnail.com/archives/10542
RNN不像传统的神经网络-它们的输出输出是固定的，而RNN允许我们输入输出向量序列。RNN是为了对序列数据进行建模而产生的。
样本序列性：样本间存在顺序关系，每个样本和它之前的样本存在关联。比如说，在文本中，一个词和它前面的词是有关联的；在气象数据中，一天的气温和前几天的气温是有关联的。
例如本帖要使用RNN生成古诗，你给它输入一堆古诗词，它会学着生成和前面相关联的字词。如果你给它输入一堆姓名，它会学着生成姓名；给它输入一堆古典乐/歌词，它会学着生成古典乐/歌词，甚至可以给它输入源代码。
关于RNN：
TensorFlow练习3: RNN, Recurrent Neural Networks http://karpathy.github.io/2015/05/21/rnn-effectiveness/本帖代码移植自char-rnn，它是基于Torch的洋文模型，稍加修改即可应用于中文。char-rnn使用文本文件做为输入、训练RNN模型，然后使用它生成和训练数据类似的文本。
使用的数据集：全唐诗(43030首)：https://pan.baidu.com/s/1o7QlUhO
训练：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 import collections import numpy as np import tensorflow as tf #-------------------------------数据预处理---------------------------# poetry_file = 'poetry.txt' # 诗集 poetrys = [ ] with open ( poetry_file , "r" , encoding = 'utf-8' , ) as f : for line in f : try : title , content = line . strip ( ) . split ( ':' ) content = content . replace ( ' ' , '' ) if '_' in content or '(' in content or '（' in content or '《' in content or '[' in content : continue if len ( content ) < 5 or len ( content ) > 79 : continue content = '[' + content + ']' poetrys . append ( content ) except Exception as e : pass # 按诗的字数排序 poetrys = sorted ( poetrys , key = lambda line : len ( line ) ) print ( '唐诗总数: ' , len ( poetrys ) ) # 统计每个字出现次数 all_words = [ ] for poetry in poetrys : all_words += [ word for word in poetry ] counter = collections . Counter ( all_words ) count_pairs = sorted ( counter . items ( ) , key = lambda x : - x [ 1 ] ) words , _ = zip ( * count_pairs ) # 取前多少个常用字 words = words [ : len ( words ) ] + ( ' ' , ) # 每个字映射为一个数字ID word_num_map = dict ( zip ( words , range ( len ( words ) ) ) ) # 把诗转换为向量形式，参考TensorFlow练习1 to_num = lambda word : word_num_map . get ( word , len ( words ) ) poetrys_vector = [ list ( map ( to_num , poetry ) ) for poetry in poetrys ] #[[314, 3199, 367, 1556, 26, 179, 680, 0, 3199, 41, 506, 40, 151, 4, 98, 1], #[339, 3, 133, 31, 302, 653, 512, 0, 37, 148, 294, 25, 54, 833, 3, 1, 965, 1315, 377, 1700, 562, 21, 37, 0, 2, 1253, 21, 36, 264, 877, 809, 1] #....] # 每次取64首诗进行训练 batch_size = 64 n_chunk = len ( poetrys_vector ) / / batch_size x_batches = [ ] y_batches = [ ] for i in range ( n_chunk ) : start_index = i * batch_size end_index = start_index + batch_size batches = poetrys_vector [ start_index : end_index ] length = max ( map ( len , batches ) ) xdata = np . full ( ( batch_size , length ) , word_num_map [ ' ' ] , np . int32 ) for row in range ( batch_size ) : xdata [ row , : len ( batches [ row ] ) ] = batches [ row ] ydata = np . copy ( xdata ) ydata [ : , : - 1 ] = xdata [ : , 1 : ] """ xdata ydata [6,2,4,6,9] [2,4,6,9,9] [1,4,2,8,5] [4,2,8,5,5] """ x_batches . append ( xdata ) y_batches . append ( ydata ) #---------------------------------------RNN--------------------------------------# input_data = tf . placeholder ( tf . int32 , [ batch_size , None ] ) output_targets = tf . placeholder ( tf . int32 , [ batch_size , None ] ) # 定义RNN def neural_network ( model = 'lstm' , rnn_size = 128 , num_layers = 2 ) : if model == 'rnn' : cell_fun = tf . nn . rnn_cell . BasicRNNCell elif model == 'gru' : cell_fun = tf . nn . rnn_cell . GRUCell elif model == 'lstm' : cell_fun = tf . nn . rnn_cell . BasicLSTMCell cell = cell_fun ( rnn_size , state_is_tuple = True ) cell = tf . nn . rnn_cell . MultiRNNCell ( [ cell ] * num_layers , state_is_tuple = True ) initial_state = cell . zero_state ( batch_size , tf . float32 ) with tf . variable_scope ( 'rnnlm' ) : softmax_w = tf . get_variable ( "softmax_w" , [ rnn_size , len ( words ) + 1 ] ) softmax_b = tf . get_variable ( "softmax_b" , [ len ( words ) + 1 ] ) with tf . device ( "/cpu:0" ) : embedding = tf . get_variable ( "embedding" , [ len ( words ) + 1 , rnn_size ] ) inputs = tf . nn . embedding_lookup ( embedding , input_data ) outputs , last_state = tf . nn . dynamic_rnn ( cell , inputs , initial_state = initial_state , scope = 'rnnlm' ) output = tf . reshape ( outputs , [ - 1 , rnn_size ] ) logits = tf . matmul ( output , softmax_w ) + softmax_b probs = tf . nn . softmax ( logits ) return logits , last_state , probs , cell , initial_state #训练 def train_neural_network ( ) : logits , last_state , _ , _ , _ = neural_network ( ) targets = tf . reshape ( output_targets , [ - 1 ] ) loss = tf . nn . seq2seq . sequence_loss_by_example ( [ logits ] , [ targets ] , [ tf . ones_like ( targets , dtype = tf . float32 ) ] , len ( words ) ) cost = tf . reduce_mean ( loss ) learning_rate = tf . Variable ( 0.0 , trainable = False ) tvars = tf . trainable_variables ( ) grads , _ = tf . clip_by_global_norm ( tf . gradients ( cost , tvars ) , 5 ) optimizer = tf . train . AdamOptimizer ( learning_rate ) train_op = optimizer . apply_gradients ( zip ( grads , tvars ) ) with tf . Session ( ) as sess : sess . run ( tf . initialize_all_variables ( ) ) saver = tf . train . Saver ( tf . all_variables ( ) ) for epoch in range ( 50 ) : sess . run ( tf . assign ( learning_rate , 0.002 * ( 0.97 * * epoch ) ) ) n = 0 for batche in range ( n_chunk ) : train_loss , _ , _ = sess . run ( [ cost , last_state , train_op ] , feed_dict = { input_data : x_batches [ n ] , output_targets : y_batches [ n ] } ) n += 1 print ( epoch , batche , train_loss ) if epoch % 7 == 0 : saver . save ( sess , 'poetry.module' , global_step = epoch ) train_neural_network ( )使用训练好的模型生成古诗：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 import collections import numpy as np import tensorflow as tf #-------------------------------数据预处理---------------------------# poetry_file = 'poetry.txt' # 诗集 poetrys = [ ] with open ( poetry_file , "r" , encoding = 'utf-8' , ) as f : for line in f : try : title , content = line . strip ( ) . split ( ':' ) content = content . replace ( ' ' , '' ) if '_' in content or '(' in content or '（' in content or '《' in content or '[' in content : continue if len ( content ) < 5 or len ( content ) > 79 : continue content = '[' + content + ']' poetrys . append ( content ) except Exception as e : pass # 按诗的字数排序 poetrys = sorted ( poetrys , key = lambda line : len ( line ) ) print ( '唐诗总数: ' , len ( poetrys ) ) # 统计每个字出现次数 all_words = [ ] for poetry in poetrys : all_words += [ word for word in poetry ] counter = collections . Counter ( all_words ) count_pairs = sorted ( counter . items ( ) , key = lambda x : - x [ 1 ] ) words , _ = zip ( * count_pairs ) # 取前多少个常用字 words = words [ : len ( words ) ] + ( ' ' , ) # 每个字映射为一个数字ID word_num_map = dict ( zip ( words , range ( len ( words ) ) ) ) # 把诗转换为向量形式，参考TensorFlow练习1 to_num = lambda word : word_num_map . get ( word , len ( words ) ) poetrys_vector = [ list ( map ( to_num , poetry ) ) for poetry in poetrys ] #[[314, 3199, 367, 1556, 26, 179, 680, 0, 3199, 41, 506, 40, 151, 4, 98, 1], #[339, 3, 133, 31, 302, 653, 512, 0, 37, 148, 294, 25, 54, 833, 3, 1, 965, 1315, 377, 1700, 562, 21, 37, 0, 2, 1253, 21, 36, 264, 877, 809, 1] #....] batch_size = 1 n_chunk = len ( poetrys_vector ) / / batch_size x_batches = [ ] y_batches = [ ] for i in range ( n_chunk ) : start_index = i * batch_size end_index = start_index + batch_size batches = poetrys_vector [ start_index : end_index ] length = max ( map ( len , batches ) ) xdata = np . full ( ( batch_size , length ) , word_num_map [ ' ' ] , np . int32 ) for row in range ( batch_size ) : xdata [ row , : len ( batches [ row ] ) ] = batches [ row ] ydata = np . copy ( xdata ) ydata [ : , : - 1 ] = xdata [ : , 1 : ] """ xdata ydata [6,2,4,6,9] [2,4,6,9,9] [1,4,2,8,5] [4,2,8,5,5] """ x_batches . append ( xdata ) y_batches . append ( ydata ) #---------------------------------------RNN--------------------------------------# input_data = tf . placeholder ( tf . int32 , [ batch_size , None ] ) output_targets = tf . placeholder ( tf . int32 , [ batch_size , None ] ) # 定义RNN def neural_network ( model = 'lstm' , rnn_size = 128 , num_layers = 2 ) : if model == 'rnn' : cell_fun = tf . nn . rnn_cell . BasicRNNCell elif model == 'gru' : cell_fun = tf . nn . rnn_cell . GRUCell elif model == 'lstm' : cell_fun = tf . nn . rnn_cell . BasicLSTMCell cell = cell_fun ( rnn_size , state_is_tuple = True ) cell = tf . nn . rnn_cell . MultiRNNCell ( [ cell ] * num_layers , state_is_tuple = True ) initial_state = cell . zero_state ( batch_size , tf . float32 ) with tf . variable_scope ( 'rnnlm' ) : softmax_w = tf . get_variable ( "softmax_w" , [ rnn_size , len ( words ) + 1 ] ) softmax_b = tf . get_variable ( "softmax_b" , [ len ( words ) + 1 ] ) with tf . device ( "/cpu:0" ) : embedding = tf . get_variable ( "embedding" , [ len ( words ) + 1 , rnn_size ] ) inputs = tf . nn . embedding_lookup ( embedding , input_data ) outputs , last_state = tf . nn . dynamic_rnn ( cell , inputs , initial_state = initial_state , scope = 'rnnlm' ) output = tf . reshape ( outputs , [ - 1 , rnn_size ] ) logits = tf . matmul ( output , softmax_w ) + softmax_b probs = tf . nn . softmax ( logits ) return logits , last_state , probs , cell , initial_state #-------------------------------生成古诗---------------------------------# # 使用训练完成的模型 def gen_poetry ( ) : def to_word ( weights ) : t = np . cumsum ( weights ) s = np . sum ( weights ) sample = int ( np . searchsorted ( t , np . random . rand ( 1 ) * s ) ) return words [ sample ] _ , last_state , probs , cell , initial_state = neural_network ( ) with tf . Session ( ) as sess : sess . run ( tf . initialize_all_variables ( ) ) saver = tf . train . Saver ( tf . all_variables ( ) ) saver . restore ( sess , 'poetry.module-49' ) state_ = sess . run ( cell . zero_state ( 1 , tf . float32 ) ) x = np . array ( [ list ( map ( word_num_map . get , '[' ) ) ] ) [ probs_ , state_ ] = sess . run ( [ probs , last_state ] , feed_dict = { input_data : x , initial_state : state_ } ) word = to_word ( probs_ ) #word = words[np.argmax(probs_)] poem = '' while word != ']' : poem += word x = np . zeros ( ( 1 , 1 ) ) x [ 0 , 0 ] = word_num_map [ word ] [ probs_ , state_ ] = sess . run ( [ probs , last_state ] , feed_dict = { input_data : x , initial_state : state_ } ) word = to_word ( probs_ ) #word = words[np.argmax(probs_)] return poem print ( gen_poetry ( ) )生成的“诗词”（至少格式对上了）：
1 2 3 4 5 6 7 8 9 10 11 12 13 新犬随风起，一璃迹阵悲。 浅昏罢庄哉，清插去园空。 双叶坐成鉴，王妓水正苑。 鸟声不成影，胙滩朱瓮声。 无斑红芜踏，那期日正闲。 吾燕登无士，无处得赵名。 并灭图微蒿，淮头水十荔。 晴花尚乘望，官宽留可求。 最忆青州守，英仍临阳峰。 生人隔天道，在国思山田。 登临闭石土，阵下一欢娱。 林暝今又少，孙频唯在愁。生成藏头诗：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def gen_poetry_with_head ( head ) : def to_word ( weights ) : t = np . cumsum ( weights ) s = np . sum ( weights ) sample = int ( np . searchsorted ( t , np . random . rand ( 1 ) * s ) ) return words [ sample ] _ , last_state , probs , cell , initial_state = neural_network ( ) with tf . Session ( ) as sess : sess . run ( tf . initialize_all_variables ( ) ) saver = tf . train . Saver ( tf . all_variables ( ) ) saver . restore ( sess , 'poetry.module-49' ) state_ = sess . run ( cell . zero_state ( 1 , tf . float32 ) ) poem = '' i = 0 for word in head : while word != '，' and word != '。' : poem += word x = np . array ( [ list ( map ( word_num_map . get , word ) ) ] ) [ probs_ , state_ ] = sess . run ( [ probs , last_state ] , feed_dict = { input_data : x , initial_state : state_ } ) word = to_word ( probs_ ) time . sleep ( 1 ) if i % 2 == 0 : poem += '，' else : poem += '。' i += 1 return poem print ( gen_poetry_with_head ( '一二三四' ) )上面使用的TensroFlow版本为0.11，0.12版本貌似不能直接运行，简单修改如下代码：
读取模型的方法：
1 2 3 module_file = tf . train . latest_checkpoint ( '.' ) #print(module_file) saver . restore ( sess , module_file )tf.initialize_all_variables() deprecated，使用tf.global_variables_initializer()替代。
https://github.com/ryankiros/neural-storyteller（根据图片生成故事）