title: cassification of sequence data with tflearn
body: <p>I would like to train a model using <code>tflearn</code> for sequence classification.</p>
  
  <p>E.g if I had a sequence of 16000 points which through convolution were reduced to 4000, and each point could be classed as one of 64 categories, the model would then look something like this:</p>
  
  <pre><code>network = input_data(shape=[None, 1600, 1], name='input')
  network = conv_1d(network, 32, 11, activation='relu', padding="same")
  network = max_pool_1d(network, 4, strides=4)
  network = fully_connected(network, 64, activation='softmax')
  network = regression(network, 
                       optimizer='adam', 
                       learning_rate=0.01,
                       loss='categorical_crossentropy', 
                       name='target',
                       to_one_hot=True,
                       n_classes=(4000, 64),
                       batch_size=1)
  </code></pre>
  
  <p>This is relatively straightfowrard to do in keras but I am finding it hard to express in tflearn.</p>