title: Shape must be rank 1 but is rank 2 tflearn error
body: <p>I am using a DNN provided by <code>tflearn</code> to learn from some data. My <code>data</code> variable has a shape of <code>(6605, 32)</code> and my <code>labels</code> data has a shape of <code>(6605,)</code> which I reshape in the code below to <code>(6605, 1)</code>...</p>
  <pre><code># Target label used for training
  labels = np.array(data[label], dtype=np.float32)
  
  # Reshape target label from (6605,) to (6605, 1)
  labels = tf.reshape(labels, shape=[-1, 1])
  
  # Data for training minus the target label.
  data = np.array(data.drop(label, axis=1), dtype=np.float32)
  
  # DNN
  net = tflearn.input_data(shape=[None, 32])
  net = tflearn.fully_connected(net, 32)
  net = tflearn.fully_connected(net, 32)
  net = tflearn.fully_connected(net, 1, activation='softmax')
  net = tflearn.regression(net)
  
  # Define model.
  model = tflearn.DNN(net)
  model.fit(data, labels, n_epoch=10, batch_size=16, show_metric=True)
  </code></pre>
  <p>This gives me a couple of errors, the first is...</p>
  <blockquote>
  <p>tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape must be rank 1 but is rank 2 for 'strided_slice' (op: 'StridedSlice') with input shapes: [6605,1], [1,16], [1,16], [1].</p>
  </blockquote>
  <p>...and the second is...</p>
  <blockquote>
  <p>During handling of the above exception, another exception occurred:</p>
  <p>ValueError: Shape must be rank 1 but is rank 2 for 'strided_slice' (op: 'StridedSlice') with input shapes: [6605,1], [1,16], [1,16], [1].</p>
  </blockquote>
  <p>I  have no idea what <code>rank 1</code> and <code>rank 2</code> are, so I do not have an idea as to how to fix this issue.</p>