我有一个问题,在这一点上,我完全迷失了如何解决它.我正在使用带有LSTM层的Keras投影时间序列.我正在尝试使用之前的10个数据点来预测第11个数据点.
这是代码:
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
def _load_data(data):
"""
data should be pd.DataFrame()
"""
n_prev = 10
docX, docY = [], []
for i in range(len(data)-n_prev):
docX.append(data.iloc[i:i+n_prev].as_matrix())
docY.append(data.iloc[i+n_prev].as_matrix())
if not docX:
pass
else:
alsX = np.array(docX)
alsY = np.array(docY)
return alsX, alsY
X, y = _load_data(df_test)
X_train = X[:25]
X_test = X[25:]
y_train = y[:25]
y_test = y[25:]
in_out_neurons = 2
hidden_neurons = 300
model = Sequential()
model.add(LSTM(in_out_neurons, hidden_neurons, return_sequences=False)) …