[深度應用]·Keras極簡實現Attention結構

在上篇部落格中筆者講解來Attention結構的基本概念，在這篇部落格使用Keras搭建一個基於Attention結構網路加深理解。。

1.生成資料 

這裡讓x[:, attention_column] = y[:, 0]，X資料的第一列等於Y資料第零列（其實就是label），這樣第一列資料和label的相關度就會很大，最後通過輸出相關度來證明思路正確性。

import keras.backend as K
import numpy as np


def get_activations(model, inputs, print_shape_only=False, layer_name=None):
    # Documentation is available online on Github at the address below.
    # From: https://github.com/philipperemy/keras-visualize-activations
    print('----- activations -----')
    activations = []
    inp = model.input
    if layer_name is None:
        outputs = [layer.output for layer in model.layers]
    else:
        outputs = [layer.output for layer in model.layers if layer.name == layer_name]  # all layer outputs
    funcs = [K.function([inp] + [K.learning_phase()], [out]) for out in outputs]  # evaluation functions
    layer_outputs = [func([inputs, 1.])[0] for func in funcs]
    for layer_activations in layer_outputs:
        activations.append(layer_activations)
        if print_shape_only:
            print(layer_activations.shape)
        else:
            print(layer_activations)
    return activations


def get_data(n, input_dim, attention_column=1):
    """
    Data generation. x is purely random except that it's first value equals the target y.
    In practice, the network should learn that the target = x[attention_column].
    Therefore, most of its attention should be focused on the value addressed by attention_column.
    :param n: the number of samples to retrieve.
    :param input_dim: the number of dimensions of each element in the series.
    :param attention_column: the column linked to the target. Everything else is purely random.
    :return: x: model inputs, y: model targets
    """
    x = np.random.standard_normal(size=(n, input_dim))
    y = np.random.randint(low=0, high=2, size=(n, 1))
    x[:, attention_column] = y[:, 0]
    return x, y
 

2.定義網路

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

from attention_utils import get_activations, get_data

np.random.seed(1337)  # for reproducibility

from keras.models import *
from keras.layers import Input, Dense,Multiply,Activation

input_dim = 4

def Att(att_dim,inputs,name):
    V = inputs
    QK = Dense(att_dim,bias=None)(inputs)
    QK = Activation("softmax",name=name)(QK)
    MV = Multiply()([V, QK])
    return(MV)


def build_model():
    inputs = Input(shape=(input_dim,))

    atts1 = Att(input_dim,inputs,"attention_vec")

    x = Dense(16)(atts1)
    atts2 = Att(16,x,"attention_vec1")


    output = Dense(1, activation='sigmoid')(atts2)
    model = Model(input=inputs, output=output)
    return model
 

3.訓練與作圖

if __name__ == '__main__':
    N = 10000
    inputs_1, outputs = get_data(N, input_dim)

    print(inputs_1[:2],outputs[:2])

    m = build_model()
    m.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    print(m.summary())

    m.fit(inputs_1, outputs, epochs=20, batch_size=128, validation_split=0.2)

    testing_inputs_1, testing_outputs = get_data(1, input_dim)

    # Attention vector corresponds to the second matrix.
    # The first one is the Inputs output.
    attention_vector = get_activations(m, testing_inputs_1,
                                       print_shape_only=True,
                                       layer_name='attention_vec')[0].flatten()
    print('attention =', attention_vector)

    # plot part.
    

    pd.DataFrame(attention_vector, columns=['attention (%)']).plot(kind='bar',
                                                                   title='Attention Mechanism as '
                                                                         'a function of input'
                                                                         ' dimensions.')
    plt.show()
 

4.結果展示

實驗結果表明，第一列相關性最大，符合最初的思想。

​

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, 4)            0                                            
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 4)            16          input_1[0][0]                    
__________________________________________________________________________________________________
attention_vec (Activation)      (None, 4)            0           dense_1[0][0]                    
__________________________________________________________________________________________________
multiply_1 (Multiply)           (None, 4)            0           input_1[0][0]                    
                                                                 attention_vec[0][0]              
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 16)           80          multiply_1[0][0]                 
__________________________________________________________________________________________________
dense_3 (Dense)                 (None, 16)           256         dense_2[0][0]                    
__________________________________________________________________________________________________
attention_vec1 (Activation)     (None, 16)           0           dense_3[0][0]                    
__________________________________________________________________________________________________
multiply_2 (Multiply)           (None, 16)           0           dense_2[0][0]                    
                                                                 attention_vec1[0][0]             
__________________________________________________________________________________________________
dense_4 (Dense)                 (None, 1)            17          multiply_2[0][0]                 
==================================================================================================
Total params: 369
Trainable params: 369
Non-trainable params: 0
__________________________________________________________________________________________________
None
Train on 8000 samples, validate on 2000 samples
Epoch 1/20
2019-05-26 20:02:22.289119: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-05-26 20:02:22.290211: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 4. Tune using inter_op_parallelism_threads for best performance.
8000/8000 [==============================] - 2s 188us/step - loss: 0.6918 - acc: 0.5938 - val_loss: 0.6893 - val_acc: 0.7715
Epoch 2/20
8000/8000 [==============================] - 0s 23us/step - loss: 0.6848 - acc: 0.7889 - val_loss: 0.6774 - val_acc: 0.8065
Epoch 3/20
8000/8000 [==============================] - 0s 28us/step - loss: 0.6619 - acc: 0.8091 - val_loss: 0.6417 - val_acc: 0.7780
Epoch 4/20
8000/8000 [==============================] - 0s 29us/step - loss: 0.6132 - acc: 0.8166 - val_loss: 0.5771 - val_acc: 0.8610
Epoch 5/20
8000/8000 [==============================] - 0s 28us/step - loss: 0.5304 - acc: 0.8925 - val_loss: 0.4758 - val_acc: 0.9185
Epoch 6/20
8000/8000 [==============================] - 0s 28us/step - loss: 0.4177 - acc: 0.9433 - val_loss: 0.3554 - val_acc: 0.9680
Epoch 7/20
8000/8000 [==============================] - 0s 24us/step - loss: 0.3028 - acc: 0.9824 - val_loss: 0.2533 - val_acc: 0.9930
Epoch 8/20
8000/8000 [==============================] - 0s 40us/step - loss: 0.2180 - acc: 0.9961 - val_loss: 0.1872 - val_acc: 0.9985
Epoch 9/20
8000/8000 [==============================] - 0s 37us/step - loss: 0.1634 - acc: 0.9986 - val_loss: 0.1442 - val_acc: 0.9985
Epoch 10/20
8000/8000 [==============================] - 0s 33us/step - loss: 0.1269 - acc: 0.9998 - val_loss: 0.1140 - val_acc: 0.9985
Epoch 11/20
8000/8000 [==============================] - 0s 22us/step - loss: 0.1013 - acc: 0.9998 - val_loss: 0.0921 - val_acc: 0.9990
Epoch 12/20
8000/8000 [==============================] - 0s 28us/step - loss: 0.0825 - acc: 0.9999 - val_loss: 0.0758 - val_acc: 0.9995
Epoch 13/20
8000/8000 [==============================] - 0s 22us/step - loss: 0.0682 - acc: 1.0000 - val_loss: 0.0636 - val_acc: 0.9995
Epoch 14/20
8000/8000 [==============================] - 0s 20us/step - loss: 0.0572 - acc: 0.9999 - val_loss: 0.0538 - val_acc: 0.9995
Epoch 15/20
8000/8000 [==============================] - 0s 23us/step - loss: 0.0485 - acc: 1.0000 - val_loss: 0.0460 - val_acc: 0.9995
Epoch 16/20
8000/8000 [==============================] - 0s 22us/step - loss: 0.0416 - acc: 1.0000 - val_loss: 0.0397 - val_acc: 0.9995
Epoch 17/20
8000/8000 [==============================] - 0s 23us/step - loss: 0.0360 - acc: 1.0000 - val_loss: 0.0345 - val_acc: 0.9995
Epoch 18/20
8000/8000 [==============================] - 0s 22us/step - loss: 0.0314 - acc: 1.0000 - val_loss: 0.0302 - val_acc: 0.9995
Epoch 19/20
8000/8000 [==============================] - 0s 22us/step - loss: 0.0276 - acc: 1.0000 - val_loss: 0.0266 - val_acc: 0.9995
Epoch 20/20
8000/8000 [==============================] - 0s 21us/step - loss: 0.0244 - acc: 1.0000 - val_loss: 0.0235 - val_acc: 1.0000
----- activations -----
(1, 4)
attention = [0.05938202 0.7233456  0.1254946  0.09177781]

&n