title: Sequence labeling with Keras - ignore first K predictions
body: <p>I'm trying to create a sequence labeler for some long sequences.</p>
  
  <p>Due to the nature of the problem, I don't expect the network to perform very well at the beginning of the sequence, due to a lack of historical data.</p>
  
  <p>How can I train the network to ignore the first <code>k</code> predictions?</p>
  
  <p>My network strucutre is as follows:</p>
  
  <pre><code>model = Sequential()
  model.add(LSTM(10,return_sequences = True, input_shape = (None, 5)))
  model.add(LSTM(10,return_sequences = True))
  model.add(LSTM(10,return_sequences = True))
  model.add(TimeDistributed(Dense(1,activation='sigmoid')))
  
  model.compile(optimizer = adam,loss = 'binary_crossentropy',metrics = ['acc'])
  </code></pre>
  
  <p>And I train it by doing: <code>model.fit(X,y)</code> where <code>X.shape</code> is <code>(m,n,5)</code> and <code>y.shape</code> is <code>(m,n,1)</code> where <code>m</code> is number of sequences and <code>n</code> is the sequence length</p>