Keras之注意力模型实现

注意力往往与encoder-decoder（seq2seq）框架搭在一起，假设我们编码前与解码后的序列如下：

编码时，我们将source通过非线性变换到中间语义：

则我们解码时，第i个输出为：

可以看到，不管i为多少，都是基于相同的中间语义C进行解码的，也就是说，我们的注意力对所有输出都是相同的。所以，注意力机制的任务就是突出重点，也就是说，我们的中间语义C对不同i应该有不同的侧重点，即上式变为：

常见的有Bahdanau Attention

e(h,s)代表一层全连接层。

及Luong Attention

---

学习的一个github上的代码，分析了一下实现过程。代码下载链接：https://github.com/Choco31415/Attention_Network_With_Keras

代码的主要目标是通过一个描述时间的字符串，预测为数字形式的字符串。如“ten before ten o'clock a.m”预测为09:50

在jupyter上运行，代码如下：

1，导入模块，好像并没有全部使用到，如Permute，Multiply，Reshape，LearningRateScheduler等

 from keras.layers import Bidirectional, Concatenate, Permute, Dot, Input, LSTM, Multiply, Reshape
 from keras.layers import RepeatVector, Dense, Activation, Lambda
 from keras.optimizers import Adam
 #from keras.utils import to_categorical
 from keras.models import load_model, Model
 #from keras.callbacks import LearningRateScheduler
 import keras.backend as K

 import matplotlib.pyplot as plt
 %matplotlib inline

 import random
 #import math

 import json
 import numpy as np

2，加载数据集，以及翻译前和翻译后的词典

 with open('data/Time Dataset.json','r') as f:
     dataset = json.loads(f.read())
 with open('data/Time Vocabs.json','r') as f:
     human_vocab, machine_vocab = json.loads(f.read())

 human_vocab_size = len(human_vocab)
 machine_vocab_size = len(machine_vocab)

这里human_vocab词典是将每个字符映射到索引，machine_vocab是将翻译后的字符映射到索引，因为翻译后的时间只包含0-9以及冒号：

3，定义数据处理方法

 def preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty):
     """
     A method for tokenizing data.

     Inputs:
     dataset - A list of sentence data pairs.
     human_vocab - A dictionary of tokens (char) to id's.
     machine_vocab - A dictionary of tokens (char) to id's.
     Tx - X data size
     Ty - Y data size

     Outputs:
     X - Sparse tokens for X data
     Y - Sparse tokens for Y data
     Xoh - One hot tokens for X data
     Yoh - One hot tokens for Y data
     """

     # Metadata
     m = len(dataset)

     # Initialize
     X = np.zeros([m, Tx], dtype='int32')
     Y = np.zeros([m, Ty], dtype='int32')

     # Process data
     for i in range(m):
         data = dataset[i]
         X[i] = np.array(tokenize(data[0], human_vocab, Tx))
         Y[i] = np.array(tokenize(data[1], machine_vocab, Ty))

     # Expand one hots
     Xoh = oh_2d(X, len(human_vocab))
     Yoh = oh_2d(Y, len(machine_vocab))

     return (X, Y, Xoh, Yoh)

 def tokenize(sentence, vocab, length):
     """
     Returns a series of id's for a given input token sequence.

     It is advised that the vocab supports <pad> and <unk>.

     Inputs:
     sentence - Series of tokens
     vocab - A dictionary from token to id
     length - Max number of tokens to consider

     Outputs:
     tokens -
     """
     tokens = [0]*length
     for i in range(length):
         char = sentence[i] if i < len(sentence) else "<pad>"
         char = char if (char in vocab) else "<unk>"
         tokens[i] = vocab[char]

     return tokens

 def ids_to_keys(sentence, vocab):
     """
     Converts a series of id's into the keys of a dictionary.
     """
     reverse_vocab = {v: k for k, v in vocab.items()}

     return [reverse_vocab[id] for id in sentence]

 def oh_2d(dense, max_value):
     """
     Create a one hot array for the 2D input dense array.
     """
     # Initialize
     oh = np.zeros(np.append(dense.shape, [max_value]))
 #     oh=np.zeros((dense.shape[0],dense.shape[1],max_value)) 这样写更为直观

     # Set correct indices
     ids1, ids2 = np.meshgrid(np.arange(dense.shape[0]), np.arange(dense.shape[1]))

 #     'F'表示一列列的展开，默认按行展开。将id序列中每个数字再one-hot化。
     oh[ids1.flatten(), ids2.flatten(), dense.flatten('F').astype(int)] = 1

     return oh

4，输入中最长的字符串为41，输出长度都是5，训练测试数据使用one-hot编码后的，训练集占比80%

 Tx = 41 # Max x sequence length
 Ty = 5 # y sequence length
 X, Y, Xoh, Yoh = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty)

 # Split data 80-20 between training and test
 train_size = int(0.8*len(dataset))
 Xoh_train = Xoh[:train_size]
 Yoh_train = Yoh[:train_size]
 Xoh_test = Xoh[train_size:]
 Yoh_test = Yoh[train_size:]

5，定义每次新预测时注意力的更新

在预测输出y_i-1后，预测y_i时，我们需要不同的注意力分布，即重新生成这个分布

 1 # Define part of the attention layer gloablly so as to
 2 # share the same layers for each attention step.
 3 def softmax(x):
 4     return K.softmax(x, axis=1)
 5 # 重复矢量，用于将一个矢量扩展成一个维度合适的tensor
 6 at_repeat = RepeatVector(Tx)
 7 # 在最后一位进行维度合并
 8 at_concatenate = Concatenate(axis=-1)
 9 at_dense1 = Dense(8, activation="tanh")
10 at_dense2 = Dense(1, activation="relu")
11 at_softmax = Activation(softmax, name='attention_weights')
12 # 这里参数名为axes。。虽然和axis是一个意思
13 at_dot = Dot(axes=1)
14
15 # 每次新的预测的时候都需要更新attention
16 def one_step_of_attention(h_prev, a):
17     """
18     Get the context.
19
20     Input:
21     h_prev - Previous hidden state of a RNN layer (m, n_h)
22     a - Input data, possibly processed (m, Tx, n_a)
23
24     Output:
25     context - Current context (m, Tx, n_a)
26     """
27     # Repeat vector to match a's dimensions
28     h_repeat = at_repeat(h_prev)
29     # Calculate attention weights
30     i = at_concatenate([a, h_repeat]) #对应公式中x和yt-1合并
31     i = at_dense1(i)#对应公式中第一个Dense
32     i = at_dense2(i)#第二个Dense
33     attention = at_softmax(i)#Softmax，此时得到一个注意力分布
34     # Calculate the context
35 #     这里使用新的attention与输入相乘，即注意力的核心原理：对于输入产生某种偏好分布
36     context = at_dot([attention, a])#Dot，使用注意力偏好分布作用于输入，返回更新后的输入
37
38     return context

以上，注意力的计算公式如下所示：

6，定义注意力层

 def attention_layer(X, n_h, Ty):
     """
     Creates an attention layer.

     Input:
     X - Layer input (m, Tx, x_vocab_size)
     n_h - Size of LSTM hidden layer
     Ty - Timesteps in output sequence

     Output:
     output - The output of the attention layer (m, Tx, n_h)
     """
     # Define the default state for the LSTM layer
 #     Lambda层不需要训练参数，这里初始化状态
     h = Lambda(lambda X: K.zeros(shape=(K.shape(X)[0], n_h)))(X)
     c = Lambda(lambda X: K.zeros(shape=(K.shape(X)[0], n_h)))(X)
     # Messy, but the alternative is using more Input()

     at_LSTM = LSTM(n_h, return_state=True)

     output = []

     # Run attention step and RNN for each output time step
　　　　# 这里就是每次预测时，先更新context，用这个新的context通过LSTM获得各个输出h
     for _ in range(Ty):
 #         第一次使用初始化的注意力参数作用输入X，之后使用上一次的h作用输入X，保证每次预测的时候注意力都对输入产生偏好
         context = one_step_of_attention(h, X)
 #         得到新的输出
         h, _, c = at_LSTM(context, initial_state=[h, c])

         output.append(h)
 #     返回全部输出
     return output

7，定义模型

 1 layer3 = Dense(machine_vocab_size, activation=softmax)
 2 layer1_size=32
 3 layer2_size=64
 4 def get_model(Tx, Ty, layer1_size, layer2_size, x_vocab_size, y_vocab_size):
 5     """
 6     Creates a model.
 7
 8     input:
 9     Tx - Number of x timesteps
10     Ty - Number of y timesteps
11     size_layer1 - Number of neurons in BiLSTM
12     size_layer2 - Number of neurons in attention LSTM hidden layer
13     x_vocab_size - Number of possible token types for x
14     y_vocab_size - Number of possible token types for y
15
16     Output:
17     model - A Keras Model.
18     """
19
20     # Create layers one by one
21     X = Input(shape=(Tx, x_vocab_size))
22     # 使用双向LSTM
23     a1 = Bidirectional(LSTM(layer1_size, return_sequences=True), merge_mode='concat')(X)
24
25 #     注意力层
26     a2 = attention_layer(a1, layer2_size, Ty)
27     # 对输出h应用一个Dense得到最后输出y
28     a3 = [layer3(timestep) for timestep in a2]
29
30     # Create Keras model
31     model = Model(inputs=[X], outputs=a3)
32
33     return model

8，训练模型

 model = get_model(Tx, Ty, layer1_size, layer2_size, human_vocab_size, machine_vocab_size)
 #这里我们可以看下模型的构成，需要提前安装graphviz模块
 from keras.utils import plot_model
 #在当前路径下生成模型各层的结构图，自己去看看理解
 plot_model(model,show_shapes=True,show_layer_names=True)
 opt = Adam(lr=0.05, decay=0.04, clipnorm=1.0)
 model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
 # (8000,5,11)->(5,8000,11)，以时间序列而非样本序列去训练，因为多个样本间是没有“序”的关系的，这样RNN也学不到啥东西
 outputs_train = list(Yoh_train.swapaxes(0,1))
 model.fit([Xoh_train], outputs_train, epochs=30, batch_size=100,verbose=2

如下为模型的结构图

9，评估

 outputs_test = list(Yoh_test.swapaxes(0,1))
 score = model.evaluate(Xoh_test, outputs_test)
 print('Test loss: ', score[0])

10，预测

这里就随机对数据集中的一个样本进行预测

 i = random.randint(0, len(dataset))

 def get_prediction(model, x):
     prediction = model.predict(x)
     max_prediction = [y.argmax() for y in prediction]
     str_prediction = "".join(ids_to_keys(max_prediction, machine_vocab))
     return (max_prediction, str_prediction)

 max_prediction, str_prediction = get_prediction(model, Xoh[i:i+1])

 print("Input: " + str(dataset[i][0]))
 print("Tokenized: " + str(X[i]))
 print("Prediction: " + str(max_prediction))
 print("Prediction text: " + str(str_prediction))

11，还可以查看一下注意力的图像

 i = random.randint(0, len(dataset))

 def plot_attention_graph(model, x, Tx, Ty, human_vocab, layer=7):
     # Process input
     tokens = np.array([tokenize(x, human_vocab, Tx)])
     tokens_oh = oh_2d(tokens, len(human_vocab))

     # Monitor model layer
     layer = model.layers[layer]

     layer_over_time = K.function(model.inputs, [layer.get_output_at(t) for t in range(Ty)])
     layer_output = layer_over_time([tokens_oh])
     layer_output = [row.flatten().tolist() for row in layer_output]

     # Get model output
     prediction = get_prediction(model, tokens_oh)[1]

     # Graph the data
     fig = plt.figure()
     fig.set_figwidth(20)
     fig.set_figheight(1.8)
     ax = fig.add_subplot(111)

     plt.title("Attention Values per Timestep")

     plt.rc('figure')
     cax = plt.imshow(layer_output, vmin=0, vmax=1)
     fig.colorbar(cax)

     plt.xlabel("Input")
     ax.set_xticks(range(Tx))
     ax.set_xticklabels(x)

     plt.ylabel("Output")
     ax.set_yticks(range(Ty))
     ax.set_yticklabels(prediction)

     plt.show()
 # 这个图像如何看：先看纵坐标，从上到下，为15:48，生成1和5时注意力在four这个单词上，生成48分钟的时候注意力集中在before单词上，这个例子非常好
 plot_attention_graph(model, dataset[i][0], Tx, Ty, human_vocab)

如图所示，在预测1和5时注意力在four单词上，预测4，8时注意力在before单词上，这比较符合逻辑。