I want to define my own Lstm model as follows:
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')
However, I meet an error:
AttributeError: 'NoneType' object has no attribute '_inbound_nodes'
The detail information is as follows:
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'
You should use keras.layers.Lambda to wrap K.* operations as a layer instead of K.* function directly.
# 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)