AttributeError: can't set attribute. Hierarchical Attentional Network

Question

When I am defining the Hierarchical Attentional Network, an error is popping up which says "AttributeError: can't set attribute". Please help.

This is the Attention.py file

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])

This is the main file where I'm building the model.

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)

This code is giving me an error which says:

---------------------------------------------------------------------------
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.

I'm a noob in Neural Networks. Please help.

Answer 1

I ran into the same problem when I was trying to execute the code on Google Colab.

I found some answers on StackOverflow says it's an ongoing issue with tf on Colab. link here

It remains unsolved for me, but I believe you can try to set self._trainable_weights instead of self.trainable_weights