Python 属性错误：'；非类型'；对象没有属性'_入站节点'；在克拉斯

我想定义自己的Lstm模型，如下所示：

from keras import backend as K
from keras.callbacks import ModelCheckpoint
from keras.layers.core import Dense, Activation, Flatten, Dropout
from keras.layers import Input,Concatenate, Average, Maximum
from keras.layers.normalization import BatchNormalization
from keras.layers import LSTM, Bidirectional
from keras.models import Model
from keras.optimizers import Adam

class LSTMModel(object):

    def __init__(self, config):
        self.num_batch = config['num_batch']
        self.maxlen = config['maxlen']
        self.embedding_dims = config['embedding_dims']
        self.lstm_dims = config['lstm_dims']
        self.hidden_dims = config['hidden_dims']
        self.epochs = config['epochs']
        self.classes = config['classes']
        self.optimizer = config['optimizer']

    def load_data(self):
        (X_train, y_train), (X_test, y_test) = \
            imdb.load_data(num_words=self.max_features, seed=11)

        X_train = sequence.pad_sequences(X_train, maxlen=self.maxlen)
        X_test = sequence.pad_sequences(X_test, maxlen=self.maxlen)

        return (X_train, y_train), (X_test, y_test)

    def build_model(self, loss, P=None):

        input = Input(shape=(self.maxlen , self.embedding_dims))

        rnn_outputs, forward_h, forward_c, backward_h, backward_c  =\
        Bidirectional(LSTM(self.lstm_dims, return_sequences = True, return_state = True,
                           kernel_initializer='uniform'))(input)
        avg_pool = K.mean(rnn_outputs, axis = 1)
        max_pool = K.max(rnn_outputs, axis = 1)
        print(avg_pool)
        print(max_pool)
        x = Concatenate()([avg_pool, max_pool])
        print(x)
        #Add a dense layer
        x = Dense(self.hidden_dims, kernel_initializer = 'he_normal')(x)
        x = Activation('relu')(x)
        x = BatchNormalization(momentum = 0.5)(x)
        x = Dropout(0.5)(x)

        output = Dense(self.classes, kernel_initializer = 'he_normal')(x)

        if loss in yes_bound:
            output = BatchNormalization(axis=1)(output)

        if loss in yes_softmax:
            output = Activation('softmax')(output)

        model = Model(inputs=input, outputs=output)
        self.compile(model, loss, P)


if __name__ == "__main__":

    config = {
        "maxlen": 100,
        "embedding_dims": 31,
        "lstm_dims":20,
        "hidden_dims": 80,
        "classes": 21,
        "epochs": 50,
        "num_batch": 24,
        "optimizer": None
    }

    model = LSTMModel(config)
    model.build_model('crossentropy')

  File "F:\models.py", line 169, in build_model
    model = Model(inputs=input, outputs=output)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 93, in __init__
    self._init_graph_network(*args, **kwargs)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 237, in _init_graph_network
    self.inputs, self.outputs)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1353, in _map_graph_network
    tensor_index=tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1312, in build_map
    node = layer._inbound_nodes[node_index]

AttributeError: 'NoneType' object has no attribute '_inbound_nodes'

但是，我遇到了一个错误：

AttributeError: 'NoneType' object has no attribute '_inbound_nodes'

详情如下:

from keras import backend as K
from keras.callbacks import ModelCheckpoint
from keras.layers.core import Dense, Activation, Flatten, Dropout
from keras.layers import Input,Concatenate, Average, Maximum
from keras.layers.normalization import BatchNormalization
from keras.layers import LSTM, Bidirectional
from keras.models import Model
from keras.optimizers import Adam

class LSTMModel(object):

    def __init__(self, config):
        self.num_batch = config['num_batch']
        self.maxlen = config['maxlen']
        self.embedding_dims = config['embedding_dims']
        self.lstm_dims = config['lstm_dims']
        self.hidden_dims = config['hidden_dims']
        self.epochs = config['epochs']
        self.classes = config['classes']
        self.optimizer = config['optimizer']

    def load_data(self):
        (X_train, y_train), (X_test, y_test) = \
            imdb.load_data(num_words=self.max_features, seed=11)

        X_train = sequence.pad_sequences(X_train, maxlen=self.maxlen)
        X_test = sequence.pad_sequences(X_test, maxlen=self.maxlen)

        return (X_train, y_train), (X_test, y_test)

    def build_model(self, loss, P=None):

        input = Input(shape=(self.maxlen , self.embedding_dims))

        rnn_outputs, forward_h, forward_c, backward_h, backward_c  =\
        Bidirectional(LSTM(self.lstm_dims, return_sequences = True, return_state = True,
                           kernel_initializer='uniform'))(input)
        avg_pool = K.mean(rnn_outputs, axis = 1)
        max_pool = K.max(rnn_outputs, axis = 1)
        print(avg_pool)
        print(max_pool)
        x = Concatenate()([avg_pool, max_pool])
        print(x)
        #Add a dense layer
        x = Dense(self.hidden_dims, kernel_initializer = 'he_normal')(x)
        x = Activation('relu')(x)
        x = BatchNormalization(momentum = 0.5)(x)
        x = Dropout(0.5)(x)

        output = Dense(self.classes, kernel_initializer = 'he_normal')(x)

        if loss in yes_bound:
            output = BatchNormalization(axis=1)(output)

        if loss in yes_softmax:
            output = Activation('softmax')(output)

        model = Model(inputs=input, outputs=output)
        self.compile(model, loss, P)


if __name__ == "__main__":

    config = {
        "maxlen": 100,
        "embedding_dims": 31,
        "lstm_dims":20,
        "hidden_dims": 80,
        "classes": 21,
        "epochs": 50,
        "num_batch": 24,
        "optimizer": None
    }

    model = LSTMModel(config)
    model.build_model('crossentropy')

  File "F:\models.py", line 169, in build_model
    model = Model(inputs=input, outputs=output)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 93, in __init__
    self._init_graph_network(*args, **kwargs)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 237, in _init_graph_network
    self.inputs, self.outputs)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1353, in _map_graph_network
    tensor_index=tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1340, in build_map
    node_index, tensor_index)

  File "E:\SoftwareInstall\anaconda3.5.2.0\lib\site-packages\keras\engine\network.py", line 1312, in build_map
    node = layer._inbound_nodes[node_index]

AttributeError: 'NoneType' object has no attribute '_inbound_nodes'

---

您应该使用

keras.layers.Lambda

将

K.*

操作包装为一个层，而不是直接使用

K.*

函数

# change
avg_pool = K.mean(rnn_outputs, axis = 1)
max_pool = K.max(rnn_outputs, axis = 1)
# to
avg_pool = Lambda(lambda x:K.mean(x,axis=1))(rnn_outputs)
max_pool = Lambda(lambda x:K.max(x,axis=1))(rnn_outputs)