Keras:具有类权重的 LSTM

Question

我的问题与 this question 密切相关但也超越了它。

我正在尝试实现以下 LSTM在 Keras 那里

时间步数为 nb_tsteps=10

输入特征的数量是 nb_feat=40

每个时间步长 LSTM 单元的数量为 120

LSTM 层之后是 TimeDistributedDense 层

从上面引用的问题中，我明白我必须将输入数据呈现为
nb_samples, 10, 40
我在哪里得到 nb_samples通过滚动一个长度的窗口 nb_tsteps=10跨越形状的原始时间序列 (5932720, 40) .代码因此

model = Sequential()
model.add(LSTM(120, input_shape=(X_train.shape[1], X_train.shape[2]), 
  return_sequences=True, consume_less='gpu'))
model.add(TimeDistributed(Dense(50, activation='relu')))
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(20, activation='relu')))
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(10, activation='relu')))
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(3, activation='relu')))
model.add(TimeDistributed(Dense(1, activation='sigmoid')))

现在我的问题(假设以上是正确的):
二元响应 (0/1) 严重不平衡，我需要传递 class_weight字典如 cw = {0: 1, 1: 25}至 model.fit() .但是我得到一个异常(exception) class_weight not supported for 3+ dimensional targets .这是因为我将响应数据显示为 (nb_samples, 1, 1) .如果我将其 reshape 为二维数组 (nb_samples, 1)我收到异常 Error when checking model target: expected timedistributed_5 to have 3 dimensions, but got array with shape (5932720, 1) .

非常感谢您的帮助!

Answer 1

我认为你应该使用 sample_weight与 sample_weight_mode='temporal' .

来自 Keras 文档:

sample_weight: Numpy array of weights for the training samples, used for scaling the loss function (during training only). You can either pass a flat (1D) Numpy array with the same length as the input samples (1:1 mapping between weights and samples), or in the case of temporal data, you can pass a 2D array with shape (samples, sequence_length), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sample_weight_mode="temporal" in compile().

在您的情况下，您需要提供与标签形状相同的二维数组。