title: Tflearns .fit() method with numpy.ndarrays causing TypeError
body: <p>So I get this error <code>TypeError: unhashable type: 'numpy.ndarray'</code> when executing the code below. I searched through Stackoverflow but haven't found a way to fix my problem. The goal is to classify digits via the mnist dataset. The error is in the modell.fit() method (from tflearn). I can attach the full error message of the error if needed. I tried it also with the method were you put the x and y lables in an dictionary and train it with this but it raised another error message. (Note I excluded my predict function in this code).</p>
  
  <p>Code:</p>
  
  <pre><code>import tflearn.datasets.mnist as mnist
  x,y,X,Y=mnist.load_data(one_hot=True)
  x=x.reshape([-1,28,28,1])
  X=X.reshape([-1,28,28,1])
  import tflearn
  
  
  class Neural_Network():
      def __init__(self,x,y):
          self.x=x
          self.y=y
          self.epochs=60000
  
      def main(self):
          cnn=tflearn.layers.core.input_data(shape=[None,28,28,1],name="input_layer")
          cnn=tflearn.layers.conv.conv_2d(cnn,32,2, activation="relu")
          cnn=tflearn.layers.conv.max_pool_2d(cnn,2)
          cnn=tflearn.layers.conv.conv_2d(cnn,32,2, activation="relu")
          cnn=tflearn.layers.conv.max_pool_2d(cnn,2)
          cnn=tflearn.layers.core.flatten(cnn)
          cnn=tflearn.layers.core.fully_connected(cnn,1000,activation="relu")        
          cnn=tflearn.layers.core.dropout(cnn,0.85)
          cnn=tflearn.layers.core.fully_connected(cnn,10,activation="softmax")
          cnn=tflearn.layers.estimator.regression(cnn,learning_rate=0.001)
          modell=tflearn.DNN(cnn)
          modell.fit(self.x,self.y)
          modell.save("mnist.modell")
  
  
  nn=Neural_Network(x,y)    
  nn.main()
  nn.predict(X[1])
  print("Label for prediction:",Y[1])
  </code></pre>