添加的图层必须是类图层的实例

debugcn 发表于 Dev

琥珀色Bhanarkar

我正在建立一个Bi-LSTM网络，并且其中包括一个注意层。但是这给了一个错误，那就是添加的层必须是类层的实例。

我导入的一些库是

from keras.models import Model, Sequential
from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding, Bidirectional, Conv1D, Flatten, GlobalMaxPooling1D, SpatialDropout1D
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import backend as K
from tensorflow.keras.layers import *

注意层类是

class attention(Layer):
    
    def __init__(self, return_sequences=True):
        self.return_sequences = return_sequences
        super(attention,self).__init__()
        
    def build(self, input_shape):
        
        self.W=self.add_weight(name="att_weight", shape=(input_shape[-1],1),
                               initializer="normal")
        self.b=self.add_weight(name="att_bias", shape=(input_shape[1],1),
                               initializer="zeros")
        
        super(attention,self).build(input_shape)
        
    def call(self, x):
        
        e = K.tanh(K.dot(x,self.W)+self.b)
        a = K.softmax(e, axis=1)
        output = x*a
        
        if self.return_sequences:
            return output
        
        return K.sum(output, axis=1)

模型看起来像这样

model = Sequential()
model.add(Embedding(max_words, 1152, input_length=max_len, weights=[embeddings]))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Bidirectional(LSTM(32, return_sequences=True)))
model.add(attention(return_sequences=True))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.summary()

但这是一个错误

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-41-ba5b52fe2c87> in <module>()
      1 model = Sequential()
----> 2 model.add(Embedding(max_words, 1152, input_length=max_len, weights=[embeddings]))
      3 model.add(BatchNormalization())
      4 model.add(Activation('tanh'))
      5 #model.add(SpatialDropout1D(0.5))

/usr/local/lib/python3.6/dist-packages/keras/engine/sequential.py in add(self, layer)
    131             raise TypeError('The added layer must be '
    132                             'an instance of class Layer. '
--> 133                             'Found: ' + str(layer))
    134         self.built = False
    135         if not self._layers:

TypeError: The added layer must be an instance of class Layer. Found: <tensorflow.python.keras.layers.embeddings.Embedding object at 0x7f0da41aec50>

马贝格

该文档页面指出，在定义一个custom时Layer，应使用以下语法：

class Linear(tf.keras.layers.Layer):
    def __init__(self, units=32, input_dim=32):
        super(Linear, self).__init__()
        w_init = tf.random_normal_initializer()
        self.w = tf.Variable(
            initial_value=w_init(shape=(input_dim, units), dtype="float32"),
            trainable=True,
        )
        b_init = tf.zeros_initializer()
        self.b = tf.Variable(
            initial_value=b_init(shape=(units,), dtype="float32"), trainable=True
        )

    def call(self, inputs):
        return tf.matmul(inputs, self.w) + self.b

因此，您的Layer导入在技术上是正确的。但是，您使用纯keras图层初始化模型，这会导致错误。tf.keras随处使用该功能，错误将消失，例如：

https://www.tensorflow.org/guide/keras/sequential_model

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# Your own layer here
class AttentionCustom(layers.Layer):
     pass

model = keras.Sequential(
    [
        layers.Dense(2, activation="relu", name="layer1"),
        layers.Dense(3, activation="relu", name="layer2"),
        Attention(),
        layers.Dense(4, name="layer3"),
    ]
)

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