人工智能深度学习入门练习之（28）TensorFlow – 例子：循环神经网络(

循环神经网络 (RNN) 是一种用于处理序列数据的人工神经网络，序列数据是相互依赖的（有限或无限）数据流，例如时间序列数据、信息字符串、对话等。

长短期记忆网络（LSTM）是一种特殊类型的循环神经网络，具有学习长期依赖的能力，是目前最常用的循环神经网络。

注意：有关循环神经网络的介绍，请参阅我们的教程深度学习 – 循环神经网络 (RNN)。

我们的例子是训练一个 LSTM 模型。在训练期间，模型会学习一段短文本。经过训练，模型可以在输入一个短句后预测下一个单词。

数据集

数据集是一个短文，《伊索寓言》中老鼠给猫挂铃铛的故事：

很久以前，老鼠有一种可以拿来对付敌人猫的东西。有人这么说，有人这么说，但最后一只小老鼠站了起来，说他有一个要做，他会遇到这个案子。你们都会同意的，他说，我们的首领在狡猾而敌人在我们当中。现在，如果我们可以从她那里得到一些，我们就可以从她那里得到。我，，，给那一只小铃铛，绕着猫的脖子。通过这种方式我们知道她什么时候在，并且可以在她在的时候。这一次见了面，直到一只老老鼠起身说，一切都很好，可是谁来给猫铃铛呢？老鼠们一齐说话。然后老老鼠说很容易。

这个短文本有 112 个不重复的符号，单词和标点符号都被认为是符号。

火车

如果我们输入 LSTM 3 个正确排序的符号和一个标签符号，模型最终将学会正确预测下一个符号（图 1).

图 1. 具有三个输入和一个输出的 LSTM 单元。

从技术上讲，LSTM 只理解数字。因此，需要对上述短文本进行一些处理，并将每个不重复的单词和标点符号替换为一个数字，总共112个不重复的符号。

我们将创建 2 个字典，一个可以从单词/标点符号映射到数字，另一个可以从数字映射到单词/标点符号（反向词典）。例如，上面的文本中有 112 个唯一符号，第一个字典包含以下条目 [ “,” : 0 ] [ “the” : 1 ], …, [ “” : 37 ],…,[ “spoke “：111]。还要构建一个逆字典，用于解码 LSTM 的输出。

LSTM输入输出都是数字，LSTM应该输出一个符号数字代码来表示符号。例如，如果输出为 37，则表示单词“”。

但是LSTM输出实际上是一个112元素的向量，每个元素值代表对应符号的概率，概率最高的符号就是最终的符号（图2).

图 2. 每个输入符号都替换为一个数字指示符。输出是一个向量，表示本次每个符号输出的概率。读取概率最高的符号代码，结合逆向字典，最终找到符号。

完成

下面是实现代码：

from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.contrib import rnn
import random
import collections
import time
start_time = time.time()
def elapsed(sec):
    if sec<60:
        return str(sec) + " sec"
    elif sec<(60*60):
        return str(sec/60) + " min"
    else:
        return str(sec/(60*60)) + " hr"
# 日志目录

logs_path = './train/rnn_words'
writer = tf.summary.FileWriter(logs_path)
# 训练用的短文
training_file = 'belling_the_cat.txt'
# 读取短文函数
def read_data(fname):
    with open(fname) as f:
        content = f.readlines()
    content = [x.strip() for x in content]
    content = [word for i in range(len(content)) for word in content[i].split()]
    content = np.array(content)
    return content
training_data = read_data(training_file)
print("Loaded training data...")
# 短文符号->数字字典，数字->短文符号反向字典
def build_dataset(words):
    count = collections.Counter(words).most_common()
    dictionary = dict()
    for word, _ in count:
        dictionary[word] = len(dictionary)
    reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
    return dictionary, reverse_dictionary
# 创建字典与反向字典
dictionary, reverse_dictionary = build_dataset(training_data)
# 符号数量
vocab_size = len(dictionary)
# 参数
learning_rate = 0.001
training_iters = 50000
display_step = 1000
n_input = 3
# RNN cell中的神经数量
n_hidden = 512

# tf Graph input
x = tf.placeholder("float", [None, n_input, 1])
y = tf.placeholder("float", [None, vocab_size])
# RNN 输出节点的 weights 与 biases
weights = {
    'out': tf.Variable(tf.random_normal([n_hidden, vocab_size]))
}
biases = {
    'out': tf.Variable(tf.random_normal([vocab_size]))
}
def RNN(x, weights, biases):
    # reshape 到 [-1, n_input]
    x = tf.reshape(x, [-1, n_input])
    # Generate a n_input-element sequence of inputs
    # (eg. [had] [a] [general] -> [20] [6] [33])
    x = tf.split(x,n_input,1)
    # 2-layer LSTM, each layer has n_hidden units.
    # Average Accuracy= 95.20% at 50k iter
    rnn_cell = rnn.MultiRNNCell([rnn.BasicLSTMCell(n_hidden),rnn.BasicLSTMCell(n_hidden)])
    # 1-layer LSTM with n_hidden units but with lower accuracy.
    # Average Accuracy= 90.60% 50k iter
    # Uncomment line below to test but comment out the 2-layer rnn.MultiRNNCell above
    # rnn_cell = rnn.BasicLSTMCell(n_hidden)
    # generate prediction
    outputs, states = rnn.static_rnn(rnn_cell, x, dtype=tf.float32)
    # there are n_input outputs but
    # we only want the last output
    return tf.matmul(outputs[-1], weights['out']) + biases['out']
pred = RNN(x, weights, biases)

# 计算损失
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
# 优化
optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate).minimize(cost)
# 模型评估
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# 初始化变量
init = tf.global_variables_initializer()
# 执行图
with tf.Session() as session:
    session.run(init)
    step = 0
    offset = random.randint(0,n_input+1)
    end_offset = n_input + 1
    acc_total = 0
    loss_total = 0
    writer.add_graph(session.graph)
    while step < training_iters:
        # Generate a minibatch. Add some randomness on selection process.
        if offset > (len(training_data)-end_offset):
            offset = random.randint(0, n_input+1)
        # 准备样本数据和标签
        symbols_in_keys = [ [dictionary[ str(training_data[i])]] for i in range(offset, offset+n_input) ]
        symbols_in_keys = np.reshape(np.array(symbols_in_keys), [-1, n_input, 1])
        symbols_out_onehot = np.zeros([vocab_size], dtype=float)
        symbols_out_onehot[dictionary[str(training_data[offset+n_input])]] = 1.0
        symbols_out_onehot = np.reshape(symbols_out_onehot,[1,-1])
        # 执行optimizer, accuracy, cost, pred
        _, acc, loss, onehot_pred = session.run([optimizer, accuracy, cost, pred], 
                                                feed_dict={x: symbols_in_keys, y: symbols_out_onehot})
        loss_total += loss

        acc_total += acc
        # 每过一定步数(display_step)，打印信息
        if (step+1) % display_step == 0:
            print("Iter= " + str(step+1) + ", Average Loss= " + 
                  "{:.6f}".format(loss_total/display_step) + ", Average Accuracy= " + 
                  "{:.2f}%".format(100*acc_total/display_step))
            acc_total = 0
            loss_total = 0
            symbols_in = [training_data[i] for i in range(offset, offset + n_input)]
            symbols_out = training_data[offset + n_input]
            symbols_out_pred = reverse_dictionary[int(tf.argmax(onehot_pred, 1).eval())]
            print("%s - [%s] vs [%s]" % (symbols_in,symbols_out,symbols_out_pred))
        # 递增step, offset
        step += 1
        offset += (n_input+1)
    # 训练完成，打印输出
    print("Optimization Finished!")
    print("Elapsed time: ", elapsed(time.time() - start_time))
    print("Run on command line.")
    print("ttensorboard --logdir=%s" % (logs_path))
    print("Point your web browser to: http://localhost:6006/")
    # 测试：接受用户输入，生成输出
    while True:
        prompt = "%s words: " % n_input
        sentence = input(prompt)
        sentence = sentence.strip()
        words = sentence.split(' ')
        if len(words) != n_input:
            continue
        try:
            symbols_in_keys = [dictionary[str(words[i])] for i in range(len(words))]
            # 连续进行32次
            for i in range(32):
                keys = np.reshape(np.array(symbols_in_keys), [-1, n_input, 1])
                onehot_pred = session.run(pred, feed_dict={x: keys})

                onehot_pred_index = int(tf.argmax(onehot_pred, 1).eval())
                sentence = "%s %s" % (sentence,reverse_dictionary[onehot_pred_index])
                symbols_in_keys = symbols_in_keys[1:]
                symbols_in_keys.append(onehot_pred_index)
            print(sentence)
        except:
            print("Word not in dictionary")

输出

Iter= 1000, Average Loss= 4.428141, Average Accuracy= 5.10%
['nobody', 'spoke', '.'] - [then] vs [then]
Iter= 2000, Average Loss= 2.937925, Average Accuracy= 17.60%
['?', 'the', 'mice'] - [looked] vs [looked]
Iter= 3000, Average Loss= 2.401870, Average Accuracy= 31.00%
['an', 'old', 'mouse'] - [got] vs [got]
Iter= 4000, Average Loss= 2.079050, Average Accuracy= 46.10%
['when', 'she', 'was'] - [about] vs [about]
Iter= 5000, Average Loss= 1.756826, Average Accuracy= 52.40%
['a', 'small', 'bell'] - [be] vs [be]
Iter= 6000, Average Loss= 1.620517, Average Accuracy= 57.80%
['from', 'her', '.'] - [i] vs [cat]
Iter= 7000, Average Loss= 1.410994, Average Accuracy= 61.50%
['some', 'signal', 'of'] - [her] vs [be]
Iter= 8000, Average Loss= 1.340336, Average Accuracy= 65.60%
['enemy', 'approaches', 'us'] - [.] vs [,]
...
Iter= 48000, Average Loss= 0.447481, Average Accuracy= 90.60%
['general', 'council', 'to'] - [consider] vs [consider]
Iter= 49000, Average Loss= 0.527762, Average Accuracy= 89.30%
['ago', ',', 'the'] - [mice] vs [mice]
Iter= 50000, Average Loss= 0.375872, Average Accuracy= 91.50%
['spoke', '.', 'then'] - [the] vs [the]
Optimization Finished!
Elapsed time:  31.482173871994018 min
Run on command line.
        tensorboard --logdir=./train/rnn_words
Point your web browser to: http://localhost:6006/
3 words: the mice had
the mice had a general council to said that is all a consider what measures they enemy , the cat . by this means we should always know when when when when when when when