AttributeError: 无法设置属性。 分层注意力网络

Question

当我定义分层注意力网络时，会弹出一个错误，上面写着“AttributeError: can't set attribute”。 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])

这是我构建模型的主文件。

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,vector_size,), 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, return_sequences = True, kernel_initializer = keras.initializers.lecun_normal(seed = None), 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)

这段代码给了我一个错误，它说：

---------------------------------------------------------------------------
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, return_sequences = True, kernel_initializer = keras.initializers.lecun_normal(seed = None), 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, return_sequences = True, kernel_initializer = keras.initializers.lecun_normal(seed = None), 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, inputs, args, kwargs, input_list):
    925       return self._functional_construction_call(inputs, args, kwargs,
--> 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, inputs, args, kwargs, 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.b, 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, name, 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.

我是神经网络的菜鸟。 请帮忙。

Answer 1

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。 链接在这里

对我来说仍然没有解决，但我相信你可以尝试设置self._trainable_weights而不是self.trainable_weights