[深度应用]·Keras极简实现Attention结构
在上篇博客中笔者讲解来Attention结构的基本概念，在这篇博客使用Keras搭建一个基于Attention结构网络加深理解。。
1.生成数据
这里让x[:, attention_column] = y[:, 0]，X数据的第一列等于Y数据第零列（其实就是label），这样第一列数据和label的相关度就会很大，最后通过输出相关度来证明思路正确性。
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.定义网络
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 model3.训练与作图
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.结果展示
实验结果表明，第一列相关性最大，符合最初的思想。
