我对深度学习和神经网络还比较陌生。
我有一个包含文本和数值特征的数据集，我试图用这里给出的方法来解决这个问题
我将数据集分为两部分，一部分是文本特征(X_text)，另一部分是数值特征(X_num)。我将文本(X_text)中的所有列合并到一个单一的列中，然后删除了其他列。接着我对这一列运行了TfidfVectorizer，并将其转换为一个形状为(1905, 20859)的数组。X_num的形状为(1905,34)
之后我使用的代码如下
from keras.models import Sequentialfrom keras.layers import Dense, Embedding, Flatten, LSTM, Input, Bidirectional, Concatenatefrom keras.optimizers import adamfrom keras import regularizersfrom keras.backend import concatenatefrom keras import Modelnlp_input = Input(shape=(20860,))meta_input = Input(shape=(35,))emb = Embedding(output_dim=32, input_dim=20859)(nlp_input)nlp_output = Bidirectional(LSTM(128, dropout=0.3, recurrent_dropout=0.3, kernel_regularizer=regularizers.l2(0.01)))(emb)x = concatenate([nlp_out, meta_input])layer1 = Dense(32, activation='relu')(x)layer2 = Dense(1, activation='sigmoid')(layer1)model = Model(inputs=[nlp_input , meta_input], outputs=layer2)optimizer=adam(lr=0.00001)model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics = ['binary_accuracy'])我得到的错误是：
Traceback (most recent call last) <ipython-input-51-d98028f8916d> in <module> 13 layer1 = Dense(32, activation='relu')(x) 14 layer2 = Dense(1, activation='sigmoid')(layer1) ---> 15 model = Model(inputs=[nlp_input , meta_input], outputs=layer2) /anaconda3/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs) 89 warnings.warn('Update your `' + object_name + '` call to the ' + 90 'Keras 2 API: ' + signature, stacklevel=2) ---> 91 return func(*args, **kwargs) 92 wrapper._original_function = func 93 return wrapper /anaconda3/lib/python3.6/site-packages/keras/engine/network.py in __init__(self, *args, **kwargs) 91 'inputs' in kwargs and 'outputs' in kwargs): 92 # Graph network ---> 93 self._init_graph_network(*args, **kwargs) 94 else: 95 # Subclassed network /anaconda3/lib/python3.6/site-packages/keras/engine/network.py in _init_graph_network(self, inputs, outputs, name) 229 # Keep track of the network's nodes and layers. 230 nodes, nodes_by_depth, layers, layers_by_depth = _map_graph_network( --> 231 self.inputs, self.outputs) 232 self._network_nodes = nodes 233 self._nodes_by_depth = nodes_by_depth /anaconda3/lib/python3.6/site-packages/keras/engine/network.py in _map_graph_network(inputs, outputs) 1364 layer=layer, 1365 node_index=node_index, -> 1366 tensor_index=tensor_index) 1367 1368 for node in reversed(nodes_in_decreasing_depth): /anaconda3/lib/python3.6/site-packages/keras/engine/network.py in build_map(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index) 1351 tensor_index = node.tensor_indices[i] 1352 build_map(x, finished_nodes, nodes_in_progress, layer, -> 1353 node_index, tensor_index) 1354 1355 finished_nodes.add(node) /anaconda3/lib/python3.6/site-packages/keras/engine/network.py in build_map(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index) 1351 tensor_index = node.tensor_indices[i] 1352 build_map(x, finished_nodes, nodes_in_progress, layer, -> 1353 node_index, tensor_index) 1354 1355 finished_nodes.add(node) /anaconda3/lib/python3.6/site-packages/keras/engine/network.py in build_map(tensor, finished_nodes, nodes_in_progress, layer, node_index, tensor_index) 1323 ValueError: if a cycle is detected. 1324 """ -> 1325 node = layer._inbound_nodes[node_index] 1326 1327 # Prevent cycles. AttributeError: 'NoneType' object has no attribute '_inbound_nodes'我在其他地方读到可以使用Lambda层，将函数作为Keras中的层，也许问题就出在这里。但据我所知，我并没有一个函数可以调用。您有什么修复这个问题的想法吗？
回答：
