如何将fit_generator与分割成批的顺序数据一起使用? [英] How to use fit_generator with sequential data that is split into batches?

问题描述

我正在尝试为我的Keras lstm模型编写一个生成器.配合fit_generator方法使用. 我的第一个问题是发电机应该返回什么?一批吗顺序? Keras文档中的示例为每个数据条目返回x，y，但是如果我的数据是连续的怎么办?我想将其分成几批吗?

I am trying to write a generator for my Keras lstm model. To use it with fit_generator method. My first question is what should my generator return? A batch? A sequence? Example in Keras documentation returns x,y for each data entry, but what if my data is sequential? And I want to split it into batches?

这是为给定输入创建批处理的python方法

Here is the python method that creates a batch for a given input

def get_batch(data, batch_num, batch_size, seq_length):
    i_start = batch_num*batch_size;
    batch_sequences = []
    batch_labels = []
    batch_chunk = data.iloc[i_start:(i_start+batch_size)+seq_length].values
    for i in range(0, batch_size):
        sequence = batch_chunk[(i_start+i):(i_start+i)+seq_length];
        label = data.iloc[(i_start+i)+seq_length].values;
        batch_labels.append(label)
        batch_sequences.append(sequence)
    return np.array(batch_sequences), np.array(batch_labels);

此方法的输出用于这样的输入:

The output of this method for an input like this:

get_batch(data, batch_num=0, batch_size=2, seq_length=3):

将是:

x = [
      [[1],[2],[3]],
      [[2],[3],[4]]
    ]

这就是我对模型的想象:

Here is how I imagine my model:

model = Sequential()
model.add(LSTM(256, return_sequences=True, input_shape=(seq_length, num_features)))
model.add(Dropout(0.2))
model.add(LSTM(256))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')

我的问题是如何将我的方法转换为生成器?

My question is how can I translate my method into a generator?

推荐答案

以下是使用 Sequence 就像Keras中的生成器一样:

Here is a solution that uses Sequence which acts like a generator in Keras:

class MySequence(Sequence):
  def __init__(self, num_batches):
    self.num_batches = num_batches

  def __len__(self):
    return self.num_batches # the length is the number of batches

  def __getitem__(self, batch_id):
    return get_batch(data, batch_id, self.batch_size, seq_length)

我认为这更干净，并且不会修改您的原始功能.现在，您将MySequence的实例传递给model.fit_generator.

I think this is cleaner and doesn't modify your original function. Now you pass an instance of MySequence to model.fit_generator.

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