Traceback (most recent call last):


 "/home/chris_r_brake/anaconda3/envs/keras_2/lib/python3.6/site-packages/keras/utils/data_utils.py", line 578, in get

inputs = self.queue.get(block=True).get()


 "/home/chris_r_brake/anaconda3/envs/keras_2/lib/python3.6/multiprocessing/pool.py", line 644, in get

raise self._value


 "/home/chris_r_brake/anaconda3/envs/keras_2/lib/python3.6/multiprocessing/pool.py", line 119, in worker

result = (True, func(*args, **kwds))


 "/home/chris_r_brake/anaconda3/envs/keras_2/lib/python3.6/site-packages/keras/utils/data_utils.py", line 401, in get_index

return _SHARED_SEQUENCES[uid][i]


 "/home/chris_r_brake/anaconda3/envs/keras_2/lib/python3.6/site-packages/keras/preprocessing/image.py", line 825, in __getitem__

return self._get_batches_of_transformed_samples(index_array)


 "/home/chris_r_brake/anaconda3/envs/keras_2/lib/python3.6/site-packages/keras/preprocessing/image.py", line 1233, in _get_batches_of_transformed_samples

img = self.image_data_generator.preprocessing_function(img)


 "/home/chris_r_brake/anaconda3/envs/keras_2/lib/python3.6/site-packages/keras/applications/inception_v3.py", line 407, in preprocess_input

return imagenet_utils.preprocess_input(x, mode='tf')


 "/home/chris_r_brake/anaconda3/envs/keras_2/lib/python3.6/site-packages/keras/applications/imagenet_utils.py", line 178, in preprocess_input

mode=mode)


 "/home/chris_r_brake/anaconda3/envs/keras_2/lib/python3.6/site-packages/keras/applications/imagenet_utils.py", line 115, in _preprocess_symbolic_input

x /= 127.5
TypeError: unsupported operand type(s) for /=: 'JpegImage
' and 'float'

Traceback (most recent call last):


 "/usr/local/bin/onmt-main", line 11, in <module>

sys.exit(main())


 "/usr/local/lib/python3.6/dist-packages/opennmt/bin/main.py", line 175, in main

checkpoint_path=args.checkpoint_path)


 "/usr/local/lib/python3.6/dist-packages/opennmt/runner.py", line 341, in score

logits=logits, labels=labels["ids_out"])


 "/home/ctext/.local/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 2045, in sparse_softmax_cross_entropy_with_logits

logits = ops.convert_to_tensor(logits)


 "/home/ctext/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1048, in convert_to_tensor

as_ref=False)


 "/home/ctext/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1144, in internal_convert_to_tensor

ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)


 "/home/ctext/.local/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 228, in _constant_tensor_conversion_function

return constant(v, dtype=dtype, name=name)


 "/home/ctext/.local/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py", line 207, in constant

value, dtype=dtype, shape=shape, verify_shape=verify_shape))


 "/home/ctext/.local/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py", line 531, in make_tensor_proto

"supported type." % (type(values), values))
TypeError: Failed to convert object of type <class 'dict'> to Tensor. Contents: {'logits': <tf.Tensor 'seq2seq/decoder_1/cond/Merge:0' shape=(?, ?, 50001) dtype=float32>, 'attention': <tf.Tensor 'seq2seq/decoder_1/cond_1/Merge:0' shape=(?, ?, ?) dtype=float32>}. Consider casting elements to a supported type.
