它与 MLP 一起进行二元分类效果很好。然而，在 LSTM 和卷积中，它给出了InvalidArgumentError.
我发现 y 需要重塑，我就这么做了。
我尝试了 x 的所有正值，并且模型运行良好。
那么负值有什么问题呢？
数据在代码中给出。有任何想法吗？
错误是这样的：
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/usr/local/src/conda3_runtime/home/envs/DSX-Python35-Spark/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1326 try:
-> 1327 return fn(*args)
1328 except errors.OpError as e:
....
File "/usr/local/src/conda3_runtime/home/envs/DSX-Python35-Spark/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1204, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): indices[0,0] = -1 is not in [0, 10)
[[Node: embedding_11/Gather = Gather[Tindices=DT_INT32, Tparams=DT_FLOAT, validate_indices=true, _device="/job:localhost/replica:0/task:0/cpu:0"](embedding_11/embeddings/read, _arg_embedding_11_input_0_3)]]
Code:
from keras.models import Sequential
from keras.layers import Dense, Embedding
from keras.layers import LSTM
max_features=10
maxlen=4
embedding_size=4
lstm_output_size = 1
batch_size = 2
epochs = 2
x=([[ 0, 6, 1, 0],
[-1, 0, 0, 1],
[ 4, -4, 3, 0],
[-1, 9, 1, -5],
[-2, 0, 0, 0],
[ 2, 0, 4, 1],
[ 4, 10, -4, -4],
[ 0, 10, -1, -4],
[ 3, -2, 2, 1],
[ 0, -1, 0, 0]])
y=([[0, 1, 0, 0, 1, 0, 0, 0, 0, 1]])
x, y = np.array(x), np.array(y)
y = y.reshape((10, 1))
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
print('Build model...')
model = Sequential()
model.add(Embedding(max_features, 64, input_length = maxlen ))
model.add(LSTM(64, activation="sigmoid", return_sequences=True, recurrent_activation="hard_sigmoid"))
model.add(Dropout(0.25))
model.add(LSTM(lstm_output_size))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print('Train...')
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test))
score, acc = model.evaluate(x_test, y_test,
batch_size=batch_size)
print('Test score:', score)
print('Test accuracy:', acc)
