使用张量流来理解LSTM模型以进行情绪分析

Question

我正在尝试使用Tensorflow学习用于情绪分析的LSTM模型,我已经完成了LSTM模型.

下面的代码(create_sentiment_featuresets.py)生成5000个正面句子和5000个否定句子的词典.

import nltk
from nltk.tokenize import word_tokenize
import numpy as np
import random
from collections import Counter
from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()

def create_lexicon(pos, neg):
    lexicon = []
    with open(pos, 'r') as f:
        contents = f.readlines()
        for l in contents[:len(contents)]:
            l= l.decode('utf-8')
            all_words = word_tokenize(l)
            lexicon += list(all_words)
    f.close()

    with open(neg, 'r') as f:
        contents = f.readlines()    
        for l in contents[:len(contents)]:
            l= l.decode('utf-8')
            all_words = word_tokenize(l)
            lexicon += list(all_words)
    f.close()

    lexicon = [lemmatizer.lemmatize(i) for i in lexicon]
    w_counts = Counter(lexicon)
    l2 = []
    for w in w_counts:
        if 1000 > w_counts[w] > 50:
            l2.append(w)
    print("Lexicon length create_lexicon: ",len(lexicon))
    return l2

def sample_handling(sample, lexicon, classification):
    featureset = []
    print("Lexicon length Sample handling: ",len(lexicon))
    with open(sample, 'r') as f:
        contents = f.readlines()
        for l in contents[:len(contents)]:
            l= l.decode('utf-8')
            current_words = word_tokenize(l.lower())
            current_words= [lemmatizer.lemmatize(i) for i in current_words]
            features = np.zeros(len(lexicon))
            for word in current_words:
                if word.lower() in lexicon:
                    index_value = lexicon.index(word.lower())
                    features[index_value] +=1
            features = list(features)
            featureset.append([features, classification])
    f.close()
    print("Feature SET------")
    print(len(featureset))
    return featureset

def create_feature_sets_and_labels(pos, neg, test_size = 0.1):
    global m_lexicon
    m_lexicon = create_lexicon(pos, neg)
    features = []
    features += sample_handling(pos, m_lexicon, [1,0])
    features += sample_handling(neg, m_lexicon, [0,1])
    random.shuffle(features)
    features = np.array(features)

    testing_size = int(test_size * len(features))

    train_x = list(features[:,0][:-testing_size])
    train_y = list(features[:,1][:-testing_size])
    test_x = list(features[:,0][-testing_size:])
    test_y = list(features[:,1][-testing_size:])
    return train_x, train_y, test_x, test_y

def get_lexicon():
    global m_lexicon
    return m_lexicon

以下代码(sentiment_analysis.py)用于使用简单神经网络模型进行情感分析,并且工作正常

from create_sentiment_featuresets import create_feature_sets_and_labels
from create_sentiment_featuresets import get_lexicon
import tensorflow as tf
import numpy as np
# extras for testing
from nltk.tokenize import word_tokenize 
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
#- end extras

train_x, train_y, test_x, test_y = create_feature_sets_and_labels('pos.txt', 'neg.txt')


# pt A-------------

n_nodes_hl1 = 1500
n_nodes_hl2 = 1500
n_nodes_hl3 = 1500

n_classes = 2
batch_size = 100
hm_epochs = 10

x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)

hidden_1_layer = {'f_fum': n_nodes_hl1,
                'weight': tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),
                'bias': tf.Variable(tf.random_normal([n_nodes_hl1]))}
hidden_2_layer = {'f_fum': n_nodes_hl2,
                'weight': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                'bias': tf.Variable(tf.random_normal([n_nodes_hl2]))}
hidden_3_layer = {'f_fum': n_nodes_hl3,
                'weight': tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                'bias': tf.Variable(tf.random_normal([n_nodes_hl3]))}
output_layer = {'f_fum': None,
                'weight': tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
                'bias': tf.Variable(tf.random_normal([n_classes]))}


def nueral_network_model(data):
    l1 = tf.add(tf.matmul(data, hidden_1_layer['weight']), hidden_1_layer['bias'])
    l1 = tf.nn.relu(l1)
    l2 = tf.add(tf.matmul(l1, hidden_2_layer['weight']), hidden_2_layer['bias'])
    l2 = tf.nn.relu(l2)
    l3 = tf.add(tf.matmul(l2, hidden_3_layer['weight']), hidden_3_layer['bias'])
    l3 = tf.nn.relu(l3)
    output = tf.matmul(l3, output_layer['weight']) + output_layer['bias']
    return output

# pt B--------------

def train_neural_network(x):
    prediction = nueral_network_model(x)
    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits= prediction, labels= y))
    optimizer = tf.train.AdamOptimizer(learning_rate= 0.001).minimize(cost)

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch in range(hm_epochs):
            epoch_loss = 0
            i = 0
            while i < len(train_x):
                start = i
                end = i+ batch_size
                batch_x = np.array(train_x[start: end])
                batch_y = np.array(train_y[start: end])
                _, c = sess.run([optimizer, cost], feed_dict= {x: batch_x, y: batch_y})
                epoch_loss += c
                i+= batch_size
            print('Epoch', epoch+ 1, 'completed out of ', hm_epochs, 'loss:', epoch_loss)

        correct= tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
        print('Accuracy:', accuracy.eval({x:test_x, y:test_y}))

        # testing --------------
        m_lexicon= get_lexicon()
        print('Lexicon length: ',len(m_lexicon))        
        input_data= "David likes to go out with Kary"       
        current_words= word_tokenize(input_data.lower())
        current_words = [lemmatizer.lemmatize(i) for i in current_words]
        features = np.zeros(len(m_lexicon))
        for word in current_words:
            if word.lower() in m_lexicon:
                index_value = m_lexicon.index(word.lower())
                features[index_value] +=1

        features = np.array(list(features)).reshape(1,-1)
        print('features length: ',len(features))
        result = sess.run(tf.argmax(prediction.eval(feed_dict={x:features}), 1))
        print(prediction.eval(feed_dict={x:features}))
        if result[0] == 0:
            print('Positive: ', input_data)
        elif result[0] == 1:
            print('Negative: ', input_data)

train_neural_network(x)

我正在尝试在TensorFlow和Python中 读取RNN w/LSTM单元格示例后修改LSTM模型的上述(sentiment_analysis.py),该示例用于mnist图像数据集上的LSTM :

一些如何通过许多命中和运行路径,我能够获得以下运行代码(sentiment_demo_lstm.py):

import tensorflow as tf
from tensorflow.contrib import rnn
from create_sentiment_featuresets import create_feature_sets_and_labels
from create_sentiment_featuresets import get_lexicon

import numpy as np

# extras for testing
from nltk.tokenize import word_tokenize 
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
#- end extras

train_x, train_y, test_x, test_y = create_feature_sets_and_labels('pos.txt', 'neg.txt')

n_steps= 100
input_vec_size= len(train_x[0])
hm_epochs = 8
n_classes = 2
batch_size = 128
n_hidden = 128

x = tf.placeholder('float', [None, input_vec_size, 1])
y = tf.placeholder('float')

def recurrent_neural_network(x):
    layer = {'weights': tf.Variable(tf.random_normal([n_hidden, n_classes])),   # hidden_layer, n_classes
            'biases': tf.Variable(tf.random_normal([n_classes]))}

    h_layer = {'weights': tf.Variable(tf.random_normal([1, n_hidden])), # hidden_layer, n_classes
            'biases': tf.Variable(tf.random_normal([n_hidden], mean = 1.0))}

    x = tf.transpose(x, [1,0,2])
    x = tf.reshape(x, [-1, 1])
    x= tf.nn.relu(tf.matmul(x, h_layer['weights']) + h_layer['biases'])

    x = tf.split(x, input_vec_size, 0)

    lstm_cell = rnn.BasicLSTMCell(n_hidden, state_is_tuple=True)
    outputs, states = rnn.static_rnn(lstm_cell, x, dtype= tf.float32)
    output = tf.matmul(outputs[-1], layer['weights']) + layer['biases']

    return output

def train_neural_network(x):
    prediction = recurrent_neural_network(x)
    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(logits= prediction, labels= y))
    optimizer = tf.train.AdamOptimizer(learning_rate= 0.001).minimize(cost)

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        for epoch in range(hm_epochs):
            epoch_loss = 0
            i = 0
            while (i+ batch_size) < len(train_x):
                start = i
                end = i+ batch_size
                batch_x = np.array(train_x[start: end])
                batch_y = np.array(train_y[start: end])
                batch_x = batch_x.reshape(batch_size ,input_vec_size, 1)
                _, c = sess.run([optimizer, cost], feed_dict= {x: batch_x, y: batch_y})
                epoch_loss += c
                i+= batch_size
            print('--------Epoch', epoch+ 1, 'completed out of ', hm_epochs, 'loss:', epoch_loss)

        correct= tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))

        print('Accuracy:', accuracy.eval({x:np.array(test_x).reshape(-1, input_vec_size, 1), y:test_y}))

        # testing --------------
        m_lexicon= get_lexicon()
        print('Lexicon length: ',len(m_lexicon))
        input_data= "Mary does not like pizza"  #"he seems to to be healthy today"  #"David likes to go out with Kary"

        current_words= word_tokenize(input_data.lower())
        current_words = [lemmatizer.lemmatize(i) for i in current_words]
        features = np.zeros(len(m_lexicon))
        for word in current_words:
            if word.lower() in m_lexicon:
                index_value = m_lexicon.index(word.lower())
                features[index_value] +=1
        features = np.array(list(features)).reshape(-1, input_vec_size, 1)
        print('features length: ',len(features))

        result = sess.run(tf.argmax(prediction.eval(feed_dict={x:features}), 1))
        print('RESULT: ', result)
        print(prediction.eval(feed_dict={x:features}))
        if result[0] == 0:
            print('Positive: ', input_data)
        elif result[0] == 1:
            print('Negative: ', input_data)

train_neural_network(x)

输出

print(train_x[0])
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

print(train_y[0])
[0, 1]

len(train_x)= 9596,len(train_x[0]) = 423 意思train_x是9596x423的列表？

我现在有一个正在运行的代码,我仍然有很多疑问.

1. 在sentiment_demo_lstm中,我无法理解以下部分

   x = tf.transpose(x, [1,0,2])
   x = tf.reshape(x, [-1, 1])
   x = tf.split(x, input_vec_size, 0)
   

   我打印了以下形状:

   x = tf.placeholder('float', [None, input_vec_size, 1]) ==> TensorShape([Dimension(None), Dimension(423), Dimension(1)]))
   x = tf.transpose(x, [1,0,2]) ==> TensorShape([Dimension(423), Dimension(None), Dimension(1)]))
   x = tf.reshape(x, [-1, 1]) ==> TensorShape([Dimension(None), Dimension(1)]))
   x = tf.split(x, input_vec_size, 0) ==> ?
   

2. 在这里,我将隐藏层的数量设为128,是否需要与输入数量相同,即 len(train_x)= 9596

3. 值1

   x = tf.placeholder('float', [None, input_vec_size, 1])
   

   和

   x = tf.reshape(x, [-1, 1])
   

   是因为train_x[0]是428x 1 ？

4. 以下是为了匹配占位符

   batch_x = np.array(train_x[start: end]) ==> (128, 423)
   batch_x = batch_x.reshape(batch_size ,input_vec_size, 1) ==> (128, 423, 1)
   

   x = tf.placeholder('float', [None, input_vec_size, 1]) 尺寸,对吧？

5. 如果我修改了代码:

   while (i+ batch_size) < len(train_x):
   

   如

   while i < len(train_x):
   

   我收到以下错误:

   Traceback (most recent call last):
     File "sentiment_demo_lstm.py", line 131, in <module>
       train_neural_network(x)
     File "sentiment_demo_lstm.py", line 86, in train_neural_network
       batch_x = batch_x.reshape(batch_size ,input_vec_size, 1)
   ValueError: cannot reshape array of size 52452 into shape (128,423,1)
   

=>训练时我不能包含最后124个记录/功能集？

Answer 1

这是加载的问题.让我试着用简单的英语隐藏所有复杂的内部细节:

具有3个步骤的简单展开LSTM模型如下所示.每个LSTM单元获取前一个LSTM单元的输入向量和隐藏输出向量,并为下一个LSTM单元产生输出向量和隐藏输出.

下面显示了相同模型的简明表示.

LSTM模型是序列到序列模型,即,当序列必须用另一个序列标记时,它们用于问题,例如句子中每个单词的POS标记或NER标记.

您似乎将其用于分类问题.使用LSTM模型进行分类有两种可能的方法

1)获取所有状态的输出(在我们的示例中为O1,O2和O3)并应用softmax层,softmax层输出大小等于类的数量(在您的情况下为2)

2)获取最后一个状态(O3)的输出并对其应用softmax图层.(这是你在鳕鱼中做的事情.输出[-1]返回输出中的最后一行)

因此我们在softmax输出的误差上反向传播(Backpropagation Through Time - BTT).

使用Tensorflow实现实现,让我们看看LSTM模型的输入和输出是什么.

每个LSTM都接受一个输入,但是我们有3个这样的LSTM单元,因此输入(X占位符)应该是大小的(输入大小*时间步长).但我们不计算单输入和BTT的误差,而是我们在一批输入 - 输出组合上进行计算.所以LSTM的输入将是(batchsize*inputsize*时间步长).

LSTM单元定义为隐藏状态的大小.输出的大小和LSTM单元的隐藏输出向量将与隐藏状态的大小相同(检查LSTM内部计算的原因!).然后,我们使用这些LSTM单元的列表定义LSTM模型,其中列表的大小将等于模型的展开次数.因此,我们定义了每次展开时要完成的展开次数和输入的大小.

我已经跳过很多东西,比如如何处理变长序列,序列到序列误差计算,LSTM如何计算输出和隐藏输出等.

在实现之后,您将在每个LSTM单元的输入之前应用relu层.我不明白为什么你这样做,但我猜你是这样做的,你的输入大小映射到LSTM输入大小.

来你的问题:

1. x是大小为[None,input_vec_size,1]的占位符(tensor/matrix/ndarray).即它可以采用可变数量的行,但每行使用input_vec_size列,每个元素是一个向量是大小1.通常占位符在行中定义为"无",以便我们可以改变输入的批量大小.

让我们说input_vec_size = 3

你正在传递一个大小为[128*3*1]的ndarray

x = tf.transpose(x,[1,0,2]) - > [3*128*1]

x = tf.reshape(x,[ - 1,1]) - > [384*1]

h_layer ['weight'] - > [1,128]

x = tf.nn.relu(tf.matmul(x,h_layer ['weights'])+ h_layer ['biases']) - > [384*128]

2. 没有输入大小的隐藏大小是不同的.LSTM对输入和先前的隐藏输出执行一组操作,并给出输出和下一个隐藏输出,这两个输出都是隐藏大小的.

3. x = tf.placeholder('float',[None,input_vec_size,1])

它定义了一个张量或ndarray或可变数量的行,每行有input_vec_size列a,每个值都是单值向量.

x = tf.reshape(x,[ - 1,1]) - >将输入x重新整形为一个大小固定为1列和任意行数的矩阵.

5. batch_x = batch_x.reshape(batch_size,input_vec_size,1)

如果batch_x!= batch_size*input_vec_size*1中的值数量,batch_x.reshape将失败.这可能是最后一批的情况,因为len(train_x)可能不是batch_size的倍数,导致未完全填充的最后一批.

您可以通过使用来避免此问题

batch_x = batch_x.reshape(-1 ,input_vec_size, 1)

但我仍然不确定你为什么在输入层前面使用Relu.

您正在最后一个单元格的输出处应用逻辑回归,这很好.

您可以查看我的玩具示例,该示例是使用双向LSTM的分类器,用于对序列是增加还是减少或混合进行分类.

在Tensorflow中使用LSTM玩具sequence_classifier