title: ValueError: Tensor conversion requested dtype
body: <p>I am trying to build a CNN in Tflearn to process 360x390px images. I am testing it on a small dataset of 170 images before deploying to the actual dataset.
  I am working on Jupyter notebook.</p>
  
  <p>The model fitting phasecan start, but after a couple seconds it throws an error that I do not understand: <code>ValueError: Tensor conversion requested dtype int64 for Tensor with dtype int32: 'Tensor("strided_slice/stack_4:0", shape=(1,), dtype=int32)</code></p>
  
  <p>Full error log:</p>
  
  <pre><code>Exception in thread Thread-6:
  Traceback (most recent call last):
    File "/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 491, in apply_op
      preferred_dtype=default_dtype)
    File "/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 704, in internal_convert_to_tensor
      ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
    File "/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 577, in _TensorTensorConversionFunction
      % (dtype.name, t.dtype.name, str(t)))
  ValueError: Tensor conversion requested dtype int64 for Tensor with dtype int32: 'Tensor("strided_slice/stack_4:0", shape=(1,), dtype=int32)'
  
  During handling of the above exception, another exception occurred:
  
  Traceback (most recent call last):
    File "/anaconda/lib/python3.6/threading.py", line 916, in _bootstrap_inner
      self.run()
    File "/anaconda/lib/python3.6/threading.py", line 864, in run
      self._target(*self._args, **self._kwargs)
    File "/anaconda/lib/python3.6/site-packages/tflearn/data_flow.py", line 187, in fill_feed_dict_queue
      data = self.retrieve_data(batch_ids)
    File "/anaconda/lib/python3.6/site-packages/tflearn/data_flow.py", line 222, in retrieve_data
      utils.slice_array(self.feed_dict[key], batch_ids)
    File "/anaconda/lib/python3.6/site-packages/tflearn/utils.py", line 187, in slice_array
      return X[start]
    File "/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 497, in _SliceHelper
      name=name)
    File "/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 655, in strided_slice
      shrink_axis_mask=shrink_axis_mask)
    File "/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py", line 3568, in strided_slice
      shrink_axis_mask=shrink_axis_mask, name=name)
    File "/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 527, in apply_op
      inferred_from[input_arg.type_attr]))
  TypeError: Input 'strides' of 'StridedSlice' Op has type int32 that does not match type int64 of argument 'begin'.
  </code></pre>
  
  <p>My network architecture seems to be ok though, I understand it is a type error but cannot pinpoint where it comes from.</p>
  
  <p>X is a np array of size (batch,H,W,C); Y is a one_hot np array of size  (batch,C) </p>
  
  <p>For info, my network looks like below:</p>
  
  <pre><code>mynet = input_data(shape=[None,H,W,C])
  
  mynet = conv_2d(mynet,nb_filter=32,filter_size=4,strides=2,activation='relu',regularizer="L2")
  mynet = max_pool_2d(mynet,2)
  mynet = conv_2d(mynet,nb_filter=64,filter_size=4,strides=2,activation='relu',regularizer="L2")
  mynet = max_pool_2d(mynet,2)
  mynet = conv_2d(mynet,nb_filter=128,filter_size=4,strides=2,activation='relu',regularizer="L2")
  mynet = max_pool_2d(mynet,2)
  
  mynet = fully_connected(mynet,n_units=1024,activation='relu')
  mynet = dropout(mynet,0.8)
  
  mynet = fully_connected(mynet,n_units=3,activation='softmax')
  mynet = regression(mynet,optimizer='adam',learning_rate=0.01,loss='categorical_crossentropy')
  
  model = tflearn.DNN(mynet)
  
  model.fit(X,Y, n_epoch=20, validation_set=0.15,shuffle=True,show_metric=True) # &lt;== This is where I get the error
  </code></pre>
  
  <p>Thanks for your help :)</p>