Keras LSTM在线学习

Question

我有一个非常可预测的序列。在下面，您可以看到其中的一部分：

deque([4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4])

基本上，这是一个变化的，但仍然可以预测的数目4s，其次是8，和28每三个8s。

我想要为在线预测建立一个非常简单的LSTM模型:每次一个新的数字到达，它就被附加在deque的右边。因此，LSTM是在由deque的0:seq_length元素组成的旧序列上训练的，训练目标是seq_length元素。然后，对1:seq_length+1元素执行窗口移位和预测。最后，deque的最左边元素被丢弃。我的直觉告诉我，这应该使网络记忆序列。

然而，我的网络只回答了4个，但令人惊讶的是，它只回答了8个，几乎所有的时间都没有。然后，一个(长)，稍后，它回到回答只有4。

我的模型结构如图所示。当然，我已经为seq_length和lstm_cells尝试过不同的价值观，但没有一个给了我成功。这些数据来自于最近一轮的调查：

seq_length = 64  #Length of the sequence to be inserted into the LSTM
vocab_size = 4  #Size of the final dense layer of the model
lstm_cells = 16  #Size of the LSTM layer

model = Sequential()
model.add(LSTM(lstm_cells, input_shape=(seq_length, 1)))
model.add(Dense(vocab_size))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['categorical_accuracy'])

下面是如何在模型上准备、训练和预测数据的方法。变量序列是在这篇文章的开头显示的deque。我维护一个列表词汇表= 4,8,28，它是在看到新的数字时建立在执行时间上的，所以vocabi将类I转换成相应的序列数。然后，我创建一个字典图例来做相反的事情。这或多或少就是正在进行的在线循环：

while True:

    # Receives new number and puts it into the deque:
    sequence.append(generateNextNumber())

    # At this point, please note that the length of the deque is seq_length + 1.

    # Dictionary to convert numbers to classes:
    legend = dict([(v, k) for k, v in enumerate(vocab)])
    # Converts the deque into a list:
    seq_list = list(sequence)
    # Each iteration is comprised of 1 training and 1 prediction. These are the training sequence and target:
    train_seq = [ [legend[i]] for i in seq_list[:seq_length] ]
    train_target = legend[ seq_list[seq_length] ]
    # And the prediction sequence just shifts the window by 1:
    pred_seq = [ [legend[i]] for i in seq_list[1:] ]

    # Batches data into a batch of size 1:
    x = np.zeros((1, seq_length, 1))
    y = np.zeros((1, vocab_size))
    x[0,:] = train_seq
    y[0,:] = to_categorical( train_target, num_classes=vocab_size )
    # Online training:
    model.fit(x=x, y=y, batch_size=1, epochs=1, verbose=0)

    # Now that one training step is done, make a prediction:
    x_pred = np.zeros((1, seq_length, 1))
    x[0,:] = pred_seq
    predicted_onehot = model.predict(x_pred)
    # Avoids "index out of range" erros when the LSTM vocab is still being built:
    predicted_index = min(np.argmax(predicted_onehot), len(vocab)-1)
    predicted_number = vocab[ predicted_index ]
    # Reverts deque length to seq_length:
    sequence.popleft()

最后，这是一个示例输出：

HIT! Current hit rate: 34.753665869071725 (predicted: 4, sequence was: deque([4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4]))

HIT! Current hit rate: 34.75566735175926 (predicted: 4, sequence was: deque([4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4]))

Predicted 4 but it was 8

HIT! Current hit rate: 34.75660255820374 (predicted: 4, sequence was: deque([4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4]))

HIT! Current hit rate: 34.758603766640086 (predicted: 4, sequence was: deque([4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4]))

HIT! Current hit rate: 34.7606048523142 (predicted: 4, sequence was: deque([4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4]))

HIT! Current hit rate: 34.76260581523739 (predicted: 4, sequence was: deque([4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4]))

HIT! Current hit rate: 34.76460665542095 (predicted: 4, sequence was: deque([4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4]))

HIT! Current hit rate: 34.76660737287616 (predicted: 4, sequence was: deque([4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4]))

Predicted 4 but it was 28

HIT! Current hit rate: 34.767541707556425 (predicted: 4, sequence was: deque([4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 8, 4, 4, 4, 4, 4, 4, 28, 4]))

出什么事了？

先谢谢你。

Answer 1

我有个问题要说：

x[0,:] = pred_seq

应该是：

x_pred[0,:] = pred_seq

现在，一切都或多或少地正常工作了。我仍然将这个问题留在这里，因为它提供了一些关于LSTM在线学习的很好的见解。