Question

我正在神经网络中加载句子向量嵌入（Facebook的InferSent）以及其他元数据。以下是我的数据集的一瞥：

端点矢量：句子嵌入ndarray。形状：（4096,1）描述Vector：句子嵌入ndarray。形状：（4096,1） HTTP路径：分类变量（0或1）是描述：二进制因变量（待预测） EndpointDesc Count：数值变量（int）余弦相似度：端点与极端之间的相似性描述载体。

我的数据集包含5621行和这6个功能。我是神经网络的新手，想要使用端点向量（以后使用所有其他功能）来预测“Is Description”变量。 Keras中LSTM的输入形状非常棘手。我不得不做同样的研究，我仍然不确定我输入LSTM的数据是否正常。

X = trainDf.iloc[:, [0]].values
y = trainDf.iloc[:, [3]].values
X = X.reshape(len(X), 1, 1)
y = y.reshape(len(y), 1, 1)
model = Sequential()
model.add(LSTM(1, input_shape=(1,1), return_sequences=True, activation="sigmoid"))
model.add(LSTM(1, input_shape=(1,1), return_sequences=True, activation="sigmoid"))
model.add(LSTM(1, input_shape=(1,1), return_sequences=True, activation="sigmoid"))
model.add(LSTM(1, input_shape=(1,1), return_sequences=True, activation="sigmoid"))
model.add(LSTM(1, input_shape=(1,1), return_sequences=True, activation="sigmoid"))
model.add(LSTM(1, input_shape=(1,1), return_sequences=True, activation="sigmoid"))
model.add(LSTM(1, input_shape=(1,1), return_sequences=True, activation="sigmoid"))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, y, epochs=1, batch_size=1, validation_data=(X, y))

predict = model.predict(X)

代码抛出以下错误：

    Train on 5621 samples, validate on 5621 samples
Epoch 1/1

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-217-c46383616bf3> in <module>()
      8 model.add(LSTM(1, input_shape=(1,1), return_sequences=True, activation="sigmoid"))
      9 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
---> 10 model.fit(X, y, epochs=1, batch_size=1, validation_data=(X, y))
     11 
     12 predict = model.predict(X)

/var/home/pranavmakhijani/anaconda/lib/python3.5/site-packages/keras/models.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
    963                               initial_epoch=initial_epoch,
    964                               steps_per_epoch=steps_per_epoch,
--> 965                               validation_steps=validation_steps)
    966 
    967     def evaluate(self, x=None, y=None,

/var/home/pranavmakhijani/anaconda/lib/python3.5/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
   1667                               initial_epoch=initial_epoch,
   1668                               steps_per_epoch=steps_per_epoch,
-> 1669                               validation_steps=validation_steps)
   1670 
   1671     def evaluate(self, x=None, y=None,

/var/home/pranavmakhijani/anaconda/lib/python3.5/site-packages/keras/engine/training.py in _fit_loop(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
   1204                         ins_batch[i] = ins_batch[i].toarray()
   1205 
-> 1206                     outs = f(ins_batch)
   1207                     if not isinstance(outs, list):
   1208                         outs = [outs]

/var/home/pranavmakhijani/anaconda/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
   2473         session = get_session()
   2474         updated = session.run(fetches=fetches, feed_dict=feed_dict,
-> 2475                               **self.session_kwargs)
   2476         return updated[:len(self.outputs)]
   2477 

/var/home/pranavmakhijani/anaconda/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    776     try:
    777       result = self._run(None, fetches, feed_dict, options_ptr,
--> 778                          run_metadata_ptr)
    779       if run_metadata:
    780         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/var/home/pranavmakhijani/anaconda/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    952             np_val = subfeed_val.to_numpy_array()
    953           else:
--> 954             np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
    955 
    956           if (not is_tensor_handle_feed and

/var/home/pranavmakhijani/anaconda/lib/python3.5/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
    480 
    481     """
--> 482     return array(a, dtype, copy=False, order=order)
    483 
    484 def asanyarray(a, dtype=None, order=None):

ValueError: setting an array element with a sequence.

后续步骤：我想在我的数据集中包含所有其他变量作为训练特征。

Keras LSTM错误：ValueError：使用序列设置数组元素

0 个答案: