问题描述
当我定义分层注意力网络时，弹出一个错误，提示“ AttributeError：无法设置属性”。请帮忙。
这是Attention.py文件
import keras
import Attention
from keras.engine.topology import Layer,Input
from keras import backend as K
from keras import initializers
#Hierarchical Attention Layer Implementation
'''
Implemented by Arkadipta De (MIT Licensed)
'''
class Hierarchical_Attention(Layer):
def __init__(self,attention_dim):
self.init = initializers.get('normal')
self.supports_masking = True
self.attention_dim = attention_dim
super(Hierarchical_Attention,self).__init__()
def build(self,input_shape):
assert len(input_shape) == 3
self.W = K.variable(self.init((input_shape[-1],self.attention_dim)))
self.b = K.variable(self.init((self.attention_dim,)))
self.u = K.variable(self.init((self.attention_dim,1)))
self.trainable_weights = [self.W,self.b,self.u]
super(Hierarchical_Attention,self).build(input_shape)
def compute_mask(self,inputs,mask=None):
return mask
def call(self,x,mask=None):
# size of x :[batch_size,sel_len,attention_dim]
# size of u :[batch_size,attention_dim]
# uit = tanh(xW+b)
uit = K.tanh(K.bias_add(K.dot(x,self.W),self.b))
ait = K.dot(uit,self.u)
ait = K.squeeze(ait,-1)
ait = K.exp(ait)
if mask is not None:
# Cast the mask to floatX to avoid float64 upcasting in theano
ait *= K.cast(mask,K.floatx())
ait /= K.cast(K.sum(ait,axis=1,keepdims=True) + K.epsilon(),K.floatx())
ait = K.expand_dims(ait)
weighted_input = x * ait
output = K.sum(weighted_input,axis=1)
return output
def compute_output_shape(self,input_shape):
return (input_shape[0],input_shape[-1])
这是我用来建立模型的主要文件。
import re
import os
import numpy as np
import pandas as pd
import keras
from keras.engine.topology import Layer,Input
import Attention
from sklearn.model_selection import train_test_split
from keras.models import Model,Input
from keras.layers import Dropout,Dense,LSTM,GRU,Bidirectional,concatenate,Multiply,Subtract
from keras.utils import to_categorical
from keras import backend as K
from keras import initializers
Max_Title_Length = 0
Max_Content_Length = 0
for i in range(0,len(X)):
Max_Title_Length = max(Max_Title_Length,len(X['title'][i]))
Max_Content_Length = max(Max_Content_Length,len(X['text'][i]))
vector_size = 100
input_title = Input(shape = (Max_Title_Length,vector_size,),name = 'input_title')
input_content = Input(shape = (Max_Content_Length,name = 'input_content')
def Classifier(input_title,input_content):
#x = Bidirectional(GRU(units = 100,return_sequences = True,kernel_initializer = keras.initializers.lecun_normal(seed = None),unit_forget_bias = True))(input_title)
x = Bidirectional(GRU(100,return_sequences=True))(input_title)
x_attention = Attention.Hierarchical_Attention(100)(x)
#y = Bidirectional(LSTM(units = 100,unit_forget_bias = True))(input_content)
y = Bidirectional(GRU(100,return_sequences=True))(input_content)
y_attention = Attention.Hierarchical_Attention(100)(y)
z = concatenate([x_attention,y_attention])
z = Dense(units = 512,activation = 'relu')(z)
z = Dropout(0.2)(z)
z = Dense(units = 256,activation = 'relu')(z)
z = Dropout(0.2)(z)
z = Dense(units = 128,activation = 'relu')(z)
z = Dropout(0.2)(z)
z = Dense(units = 50,activation = 'relu')(z)
z = Dropout(0.2)(z)
z = Dense(units = 10,activation = 'relu')(z)
z = Dropout(0.2)(z)
output = Dense(units = 2,activation = 'softmax')(z)
model = Model(inputs = [input_title,input_content],outputs = output)
model.summary()
return model
def compile_and_train(model,num_epochs):
model.compile(optimizer= 'adam',loss= 'categorical_crossentropy',metrics=['acc'])
history = model.fit([train_x_title,train_x_content],train_label,batch_size=32,epochs=num_epochs)
return history
Classifier_Model = Classifier(input_title,input_content)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __setattr__(self,name,value)
2761 try:
-> 2762 super(tracking.AutoTrackable,self).__setattr__(name,value)
2763 except AttributeError:
AttributeError: can't set attribute
During handling of the above exception,another exception occurred:
AttributeError Traceback (most recent call last)
6 frames
<ipython-input-43-32804502e0b0> in <module>()
32 return history
33
---> 34 Classifier_Model = Classifier(input_title,input_content)
<ipython-input-43-32804502e0b0> in Classifier(input_title,input_content)
7 #x = Bidirectional(GRU(units = 100,unit_forget_bias = True))(input_title)
8 x = Bidirectional(GRU(200,return_sequences=True))(input_title)
----> 9 x_attention = Attention.Hierarchical_Attention(100)(x)
10 #y = Bidirectional(LSTM(units = 100,unit_forget_bias = True))(input_content)
11 y = Bidirectional(GRU(100,return_sequences=True))(input_content)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __call__(self,*args,**kwargs)
924 if _in_functional_construction_mode(self,args,kwargs,input_list):
925 return self._functional_construction_call(inputs,--> 926 input_list)
927
928 # Maintains info about the `Layer.call` stack.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _functional_construction_call(self,input_list)
1096 # Build layer if applicable (if the `build` method has been
1097 # overridden).
-> 1098 self._maybe_build(inputs)
1099 cast_inputs = self._maybe_cast_inputs(inputs,input_list)
1100
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in _maybe_build(self,inputs)
2641 # operations.
2642 with tf_utils.maybe_init_scope(self):
-> 2643 self.build(input_shapes) # pylint:disable=not-callable
2644 # We must set also ensure that the layer is marked as built,and the build
2645 # shape is stored since user defined build functions may not be calling
/content/Attention.py in build(self,input_shape)
23 self.b = K.variable(self.init((self.attention_dim,)))
24 self.u = K.variable(self.init((self.attention_dim,1)))
---> 25 self.trainable_weights = [self.W,self.u]
26 super(Hierarchical_Attention,self).build(input_shape)
27
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in __setattr__(self,value)
2765 ('Can\'t set the attribute "{}",likely because it conflicts with '
2766 'an existing read-only @property of the object. Please choose a '
-> 2767 'different name.').format(name))
2768 return
2769
AttributeError: Can't set the attribute "trainable_weights",likely because it conflicts with an existing read-only @property of the object. Please choose a different name.
我是神经网络领域的菜鸟。请帮忙。
解决方法
当我尝试在Google Colab上执行代码时遇到了同样的问题。
我在StackOverflow上找到了一些答案，说这是Colab上tf的一个持续问题。 link here
对我来说，它仍然没有解决，但是我相信您可以尝试设置self._trainable_weights而不是self.trainable_weights