Keras之注意力模型实现

今天药忘吃喽~ 2023-06-02 09:12 49阅读 0赞

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][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，这些应该是优化的时候使用的

1 from keras.layers import Bidirectional, Concatenate, Permute, Dot, Input, LSTM, Multiply, Reshape
     2 from keras.layers import RepeatVector, Dense, Activation, Lambda
     3 from keras.optimizers import Adam
     4 #from keras.utils import to_categorical
     5 from keras.models import load_model, Model
     6 #from keras.callbacks import LearningRateScheduler
     7 import keras.backend as K
     8 
     9 import matplotlib.pyplot as plt
    10 %matplotlib inline
    11 
    12 import random
    13 #import math
    14 
    15 import json
    16 import numpy as np

2，加载数据集，以及翻译前和翻译后的词典

1 with open('data/Time Dataset.json','r') as f:
    2     dataset = json.loads(f.read())
    3 with open('data/Time Vocabs.json','r') as f:
    4     human_vocab, machine_vocab = json.loads(f.read())
    5     
    6 human_vocab_size = len(human_vocab)
    7 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也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：

3，定义数据处理方法

tokenize为将字符映射到索引，one-hot为对每个映射后的索引做了个one-hot编码处理

1 def preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty):
     2     """
     3     A method for tokenizing data.
     4     
     5     Inputs:
     6     dataset - A list of sentence data pairs.
     7     human_vocab - A dictionary of tokens (char) to id's.
     8     machine_vocab - A dictionary of tokens (char) to id's.
     9     Tx - X data size
    10     Ty - Y data size
    11     
    12     Outputs:
    13     X - Sparse tokens for X data
    14     Y - Sparse tokens for Y data
    15     Xoh - One hot tokens for X data
    16     Yoh - One hot tokens for Y data
    17     """
    18     
    19     # Metadata
    20     m = len(dataset)
    21     
    22     # Initialize
    23     X = np.zeros([m, Tx], dtype='int32')
    24     Y = np.zeros([m, Ty], dtype='int32')
    25     
    26     # Process data
    27     for i in range(m):
    28         data = dataset[i]
    29         X[i] = np.array(tokenize(data[0], human_vocab, Tx))
    30         Y[i] = np.array(tokenize(data[1], machine_vocab, Ty))
    31     
    32     # Expand one hots
    33     Xoh = oh_2d(X, len(human_vocab))
    34     Yoh = oh_2d(Y, len(machine_vocab))
    35     
    36     return (X, Y, Xoh, Yoh)
    37     
    38 def tokenize(sentence, vocab, length):
    39     """
    40     Returns a series of id's for a given input token sequence.
    41     
    42     It is advised that the vocab supports <pad> and <unk>.
    43     
    44     Inputs:
    45     sentence - Series of tokens
    46     vocab - A dictionary from token to id
    47     length - Max number of tokens to consider
    48     
    49     Outputs:
    50     tokens - 
    51     """
    52     tokens = [0]*length
    53     for i in range(length):
    54         char = sentence[i] if i < len(sentence) else "<pad>"
    55         char = char if (char in vocab) else "<unk>"
    56         tokens[i] = vocab[char]
    57         
    58     return tokens
    59 
    60 def ids_to_keys(sentence, vocab):
    61     """
    62     Converts a series of id's into the keys of a dictionary.
    63     """
    64     reverse_vocab = {v: k for k, v in vocab.items()}
    65     
    66     return [reverse_vocab[id] for id in sentence]
    67 
    68 def oh_2d(dense, max_value):
    69     """
    70     Create a one hot array for the 2D input dense array.
    71     """
    72     # Initialize
    73     oh = np.zeros(np.append(dense.shape, [max_value]))
    74 #     oh=np.zeros((dense.shape[0],dense.shape[1],max_value)) 这样写更为直观
    75     
    76     # Set correct indices
    77     ids1, ids2 = np.meshgrid(np.arange(dense.shape[0]), np.arange(dense.shape[1]))
    78     
    79 #     'F'表示一列列的展开，默认按行展开，这一行看不太懂
    80     oh[ids1.flatten(), ids2.flatten(), dense.flatten('F').astype(int)] = 1
    81     
    82     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%

1 Tx = 41 # Max x sequence length
     2 Ty = 5 # y sequence length
     3 X, Y, Xoh, Yoh = preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty)
     4 
     5 # Split data 80-20 between training and test
     6 train_size = int(0.8*len(dataset))
     7 Xoh_train = Xoh[:train_size]
     8 Yoh_train = Yoh[:train_size]
     9 Xoh_test = Xoh[train_size:]
    10 Yoh_test = Yoh[train_size:]

5，定义每次新预测时注意力的更新

个人解释：注意力机制即我们对输入产生一个偏好分布，让模型知道集中注意哪部分的输入，为此，在预测输出yi-1后，预测yi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：

![1254945-20190910102627585-1932185625.png][]

6，定义注意力层

1 def attention_layer(X, n_h, Ty):
     2     """
     3     Creates an attention layer.
     4     
     5     Input:
     6     X - Layer input (m, Tx, x_vocab_size)
     7     n_h - Size of LSTM hidden layer
     8     Ty - Timesteps in output sequence
     9     
    10     Output:
    11     output - The output of the attention layer (m, Tx, n_h)
    12     """    
    13     # Define the default state for the LSTM layer
    14 #     Lambda层不需要训练参数，这里初始化状态
    15     h = Lambda(lambda X: K.zeros(shape=(K.shape(X)[0], n_h)))(X)
    16     c = Lambda(lambda X: K.zeros(shape=(K.shape(X)[0], n_h)))(X)
    17     # Messy, but the alternative is using more Input()
    18     
    19     at_LSTM = LSTM(n_h, return_state=True)
    20     
    21     output = []
    22               
    23     # 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
    24     for _ in range(Ty):
    25 #         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
    26         context = one_step_of_attention(h, X)
    27 #         得到新的输出
    28         h, _, c = at_LSTM(context, initial_state=[h, c])
    29         
    30         output.append(h)
    31 #     返回全部输出
    32     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

1 model = get_model(Tx, Ty, layer1_size, layer2_size, human_vocab_size, machine_vocab_size)
     2 #这里我们可以看下模型的构成，需要提前安装graphviz模块
     3 from keras.utils import plot_model
     4 #在当前路径下生成模型各层的结构图，自己去看看理解
     5 plot_model(model,show_shapes=True,show_layer_names=True)
     6 opt = Adam(lr=0.05, decay=0.04, clipnorm=1.0)
     7 model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
     8 # (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
     9 outputs_train = list(Yoh_train.swapaxes(0,1))
    10 model.fit([Xoh_train], outputs_train, epochs=30, batch_size=100,verbose=2

如下为模型的结构图

![1254945-20190910110420265-1731919968.png][]

9，评估

1 outputs_test = list(Yoh_test.swapaxes(0,1))
    2 score = model.evaluate(Xoh_test, outputs_test) 
    3 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

3 i = random.randint(0, len(dataset))
     4 
     5 def get_prediction(model, x):
     6     prediction = model.predict(x)
     7     max_prediction = [y.argmax() for y in prediction]
     8     str_prediction = "".join(ids_to_keys(max_prediction, machine_vocab))
     9     return (max_prediction, str_prediction)
    10 
    11 max_prediction, str_prediction = get_prediction(model, Xoh[i:i+1])
    12 
    13 print("Input: " + str(dataset[i][0]))
    14 print("Tokenized: " + str(X[i]))
    15 print("Prediction: " + str(max_prediction))
    16 print("Prediction text: " + str(str_prediction))

11，还可以查看一下注意力的图像

1 i = random.randint(0, len(dataset))
     2 
     3 def plot_attention_graph(model, x, Tx, Ty, human_vocab, layer=7):
     4     # Process input
     5     tokens = np.array([tokenize(x, human_vocab, Tx)])
     6     tokens_oh = oh_2d(tokens, len(human_vocab))
     7     
     8     # Monitor model layer
     9     layer = model.layers[layer]
    10     
    11     layer_over_time = K.function(model.inputs, [layer.get_output_at(t) for t in range(Ty)])
    12     layer_output = layer_over_time([tokens_oh])
    13     layer_output = [row.flatten().tolist() for row in layer_output]
    14     
    15     # Get model output
    16     prediction = get_prediction(model, tokens_oh)[1]
    17     
    18     # Graph the data
    19     fig = plt.figure()
    20     fig.set_figwidth(20)
    21     fig.set_figheight(1.8)
    22     ax = fig.add_subplot(111)
    23     
    24     plt.title("Attention Values per Timestep")
    25     
    26     plt.rc('figure')
    27     cax = plt.imshow(layer_output, vmin=0, vmax=1)
    28     fig.colorbar(cax)
    29     
    30     plt.xlabel("Input")
    31     ax.set_xticks(range(Tx))
    32     ax.set_xticklabels(x)
    33     
    34     plt.ylabel("Output")
    35     ax.set_yticks(range(Ty))
    36     ax.set_yticklabels(prediction)
    37     
    38     plt.show()
    39 # 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
    40 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。

![1254945-20190910105812127-1544655057.png][]

转载于:https://www.cnblogs.com/lunge-blog/p/11496287.html

[https_github.com_Choco31415_Attention_Network_With_Keras]: https://github.com/Choco31415/Attention_Network_With_Keras
[1254945-20190910102627585-1932185625.png]: /images/20230601/99f4a024077649228a10335cb3b077a8.png
[1254945-20190910110420265-1731919968.png]: /images/20230601/f85194647b064f07803e82ac892c304e.png
[1254945-20190910105812127-1544655057.png]: /images/20230601/9c4a3fe17bde440c864e7c83063b49e9.png