34、深度学习-自学之路-深入理解-NLP自然语言处理-RNN一个简单的程序，可以从程序中理解RNN的基本思想。

import sys,random,math
from collections import Counter
import numpy as np

f = open('tasks_1-20_v1/en/qa1_single-supporting-fact_train.txt','r')
raw = f.readlines()
f.close()

tokens = list()
for line in raw[0:1000]:
    tokens.append(line.lower().replace("\n","").split(" ")[1:])

print(tokens[0:3])

vocab = set()
for sent in tokens:
    for word in sent:
        vocab.add(word)

vocab = list(vocab)

word2index = {}
for i, word in enumerate(vocab):
    word2index[word] = i


def words2indices(sentence):
    idx = list()
    for word in sentence:
        idx.append(word2index[word])
    return idx


def softmax(x):
    e_x = np.exp(x - np.max(x))
    return e_x / e_x.sum(axis=0)

np.random.seed(1)
embed_size = 10

# word embeddings
embed = (np.random.rand(len(vocab),embed_size) - 0.5) * 0.1

# embedding -> embedding (initially the identity matrix)
recurrent = np.eye(embed_size)

# sentence embedding for empty sentence
start = np.zeros(embed_size)

# embedding -> output weights
decoder = (np.random.rand(embed_size, len(vocab)) - 0.5) * 0.1

# one hot lookups (for loss function)
one_hot = np.eye(len(vocab))


def predict(sent):
    layers = list()
    layer = {}
    layer['hidden'] = start
    layers.append(layer)

    loss = 0

    # forward propagate
    preds = list()
    for target_i in range(len(sent)):
        layer = {}

        # try to predict the next term
        layer['pred'] = softmax(layers[-1]['hidden'].dot(decoder))

        loss += -np.log(layer['pred'][sent[target_i]])

        # generate the next hidden state
        layer['hidden'] = layers[-1]['hidden'].dot(recurrent) + embed[sent[target_i]]
        layers.append(layer)

    return layers, loss

# forward
for iter in range(30000):
    alpha = 0.001
    sent = words2indices(tokens[iter%len(tokens)][1:])
    layers,loss = predict(sent)

    # back propagate
    for layer_idx in reversed(range(len(layers))):
        layer = layers[layer_idx]
        target = sent[layer_idx-1]

        if(layer_idx > 0):  # if not the first layer
            layer['output_delta'] = layer['pred'] - one_hot[target]
            new_hidden_delta = layer['output_delta'].dot(decoder.transpose())

            # if the last layer - don't pull from a later one becasue it doesn't exist
            if(layer_idx == len(layers)-1):
                layer['hidden_delta'] = new_hidden_delta
            else:
                layer['hidden_delta'] = new_hidden_delta + layers[layer_idx+1]['hidden_delta'].dot(recurrent.transpose())
        else: # if the first layer
            layer['hidden_delta'] = layers[layer_idx+1]['hidden_delta'].dot(recurrent.transpose())

# forward
for iter in range(30000):
    alpha = 0.001
    sent = words2indices(tokens[iter%len(tokens)][1:])

    layers,loss = predict(sent)

    # back propagate
    for layer_idx in reversed(range(len(layers))):
        layer = layers[layer_idx]
        target = sent[layer_idx-1]

        if(layer_idx > 0):
            layer['output_delta'] = layer['pred'] - one_hot[target]
            new_hidden_delta = layer['output_delta'].dot(decoder.transpose())

            # if the last layer - don't pull from a
            # later one becasue it doesn't exist
            if(layer_idx == len(layers)-1):
                layer['hidden_delta'] = new_hidden_delta
            else:
                layer['hidden_delta'] = new_hidden_delta + layers[layer_idx+1]['hidden_delta'].dot(recurrent.transpose())
        else:
            layer['hidden_delta'] = layers[layer_idx+1]['hidden_delta'].dot(recurrent.transpose())
    # update weights
    start -= layers[0]['hidden_delta'] * alpha / float(len(sent))
    for layer_idx, layer in enumerate(layers[1:]):
        decoder -= np.outer(layers[layer_idx]['hidden'], layer['output_delta']) * alpha / float(len(sent))

        embed_idx = sent[layer_idx]
        embed[embed_idx] -= layers[layer_idx]['hidden_delta'] * alpha / float(len(sent))
        recurrent -= np.outer(layers[layer_idx]['hidden'], layer['hidden_delta']) * alpha / float(len(sent))

    if (iter % 1000 == 0):
        print("Perplexity:" + str(np.exp(loss / len(sent))))

sent_index = 4

l,_ = predict(words2indices(tokens[sent_index]))

print(tokens[sent_index])

for i,each_layer in enumerate(l[1:-1]):
    input = tokens[sent_index][i]
    true = tokens[sent_index][i+1]
    pred = vocab[each_layer['pred'].argmax()]
    print("Prev Input:" + input + (' ' * (12 - len(input))) +\
          "True:" + true + (" " * (15 - len(true))) + "Pred:" + pred)

运行结果：

'''

[['mary', 'moved', 'to', 'the', 'bathroom.'], ['john', 'went', 'to', 'the', 'hallway.'], ['where', 'is', 'mary?', '\tbathroom\t1']]
Perplexity:82.00628585171349
Perplexity:81.85211883471023
Perplexity:81.65770644998716
Perplexity:81.35306453182142
Perplexity:80.81390886064067
Perplexity:79.77590929663761
Perplexity:77.56372974860284
Perplexity:71.83277995805608
Perplexity:48.417320803100246
Perplexity:24.839039726192727
Perplexity:19.975487836618008
Perplexity:18.460238985588635
Perplexity:17.029965398985233
Perplexity:14.893859382635188
Perplexity:11.770260202396468
Perplexity:8.635593964846068
Perplexity:6.948911974251331
Perplexity:6.020200624240951
Perplexity:5.409527179381012
Perplexity:5.0520385889435815
Perplexity:4.836185470740281
Perplexity:4.699695331198478
Perplexity:4.619284223100641
Perplexity:4.5672934716850975
Perplexity:4.516766550556743
Perplexity:4.453346872739982
Perplexity:4.37458647209406
Perplexity:4.283212137192868
Perplexity:4.180822355576833
Perplexity:4.0651542349348615
['sandra', 'moved', 'to', 'the', 'garden.']
Prev Input:sandra      True:moved          Pred:is
Prev Input:moved       True:to             Pred:to
Prev Input:to          True:the            Pred:the
Prev Input:the         True:garden.        Pred:bedroom.
 

'''