我正在尝试在时间序列数据上使用LSTM模型。我正在使用的数据的具体背景是针对未来价格预测的Twitter情绪分析。我的数据看起来像这样:
date mentions likes retweets polarity count Volume Close
2017-04-10 0.24 0.123 -0.58 0.211 0.58 0.98 0.87
2017-04-11 -0.56 0.532 0.77 0.231 -0.23 0.42 0.92
.
.
.
2019-01-10 0.23 0.356 -0.21 -0.682 0.23 -0.12 -0.23
数据是大小(608,8),我计划使用的功能是第2列到第7列,我预测的目标是Close
(即第8列)。我知道LSTM模型需要输入为3D张量的形状,所以我做了一些操作来转换和重塑数据:
x = np.asarray(data.iloc[:, 1:8])
y = np.asarray(data.iloc[:. 8])
x = x.reshape(x.shape[0], 1, x.shape[1])
之后我试图训练LSTM模型:
batch_size = 200
model = Sequential()
model.add(LSTM(batch_size, input_dim=3, activation='relu', return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(128, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(loss='mean_squared_error',
optimizer='rmsprop',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=15)
运行这个模型给了我一个:
ValueError: Error when checking input: expected lstm_10_input to have
shape (None, 3) but got array with shape (1, 10)
有谁知道我哪里出错了?它是在我准备数据的方式,还是我训练模型错了?
我一直在阅读关于这个社区以及文章/博客的许多相关问题,但我仍然无法找到解决方案...感谢任何帮助,谢谢!
x的形状应该是形状(batch_size, timesteps, input_dim)
LSTM的第一个参数不是批量大小,而是输出大小
例:
df = pd.DataFrame(np.random.randn(100,9))
x_train = df.iloc[:,1:8].values
y_train = df.iloc[:,8].values
# No:of sample, times_steps, input_size (1 in your case)
x_train = x_train.reshape(x_train.shape[0],x_train.shape[1], 1)
model = Sequential()
# 16 outputs of first LSTM per time step
model.add(LSTM(16, input_dim=1, activation='relu', return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(8, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(4, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(loss='mean_squared_error',
optimizer='rmsprop',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=15, batch_size=32)