Traceback (most recent call last):


 "/home/akirkedal/software/g2p-seq2seq/tests/g2p_unittest.py", line 30, in test_evaluate

g2p_model.load_decode_model()


 "/home/akirkedal/software/g2p-seq2seq/g2p_seq2seq/g2p.py", line 96, in load_decode_model

forward_only=True)


 "/home/akirkedal/software/g2p-seq2seq/g2p_seq2seq/seq2seq_model.py", line 164, in __init__

softmax_loss_function=softmax_loss_function)


 "/home/akirkedal/software/anaconda/envs/g2p/lib/python2.7/site-packages/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py", line 1201, in model_with_buckets

decoder_inputs[:bucket[1]])


 "/home/akirkedal/software/g2p-seq2seq/g2p_seq2seq/seq2seq_model.py", line 163, in <lambda>

self.target_weights, buckets, lambda x, y: seq2seq_f(x, y, True),


 "/home/akirkedal/software/g2p-seq2seq/g2p_seq2seq/seq2seq_model.py", line 140, in seq2seq_f

dtype=dtype)


 "/home/akirkedal/software/anaconda/envs/g2p/lib/python2.7/site-packages/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py", line 855, in embedding_attention_seq2seq

encoder_cell, encoder_inputs, dtype=dtype)


 "/home/akirkedal/software/anaconda/envs/g2p/lib/python2.7/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn.py", line 197, in static_rnn

(output, state) = call_cell()


 "/home/akirkedal/software/anaconda/envs/g2p/lib/python2.7/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn.py", line 184, in <lambda>

call_cell = lambda: cell(input_, state)


 "/home/akirkedal/software/anaconda/envs/g2p/lib/python2.7/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py", line 881, in __call__

return self._cell(embedded, state)


 "/home/akirkedal/software/anaconda/envs/g2p/lib/python2.7/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py", line 146, in __call__

with _checked_scope(self, scope or "gru_cell", reuse=self._reuse):


 "/home/akirkedal/software/anaconda/envs/g2p/lib/python2.7/contextlib.py", line 17, in __enter__

return self.gen.next()


 "/home/akirkedal/software/anaconda/envs/g2p/lib/python2.7/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py", line 77, in _checked_scope

type(cell).__name__))
ValueError: Attempt to reuse RNNCell <tensorflow.contrib.rnn.python.ops.core_rnn_cell_impl.GRUCell object at 0x7f18f8090d50> with a different variable scope than its first use.
First use of cell was with scope 'embedding_attention_seq2seq/embedding_attention_decoder/attention_decoder/gru_cell', this attempt is with scope 'embedding_attention_seq2seq/rnn/gru_cell'.
Please create a new instance of the cell if you would like it to use a different set of weights.
If before you were using: MultiRNNCell([GRUCell(...)] * num_layers), change to: MultiRNNCell([GRUCell(...) for _ in range(num_layers)]).
If before you were using the same cell instance as both the forward and reverse cell of a bidirectional RNN, simply create two instances (one for forward, one for reverse).
In May 2017, we will start transitioning this cell's behavior to use existing stored weights, if any, when it is called with scope=None (which can lead to silent model degradation, so this error will remain until then.)

Traceback (most recent call last):


 "h5_coreml.py", line 4, in <module>

coreml_model = coremltools.converters.keras.convert(sys.argv[1], input_names='input1', image_input_names='input1', output_names='output1', image_scale=1/255.)


 "/home/xuzh/convert_yolo_to_coreml/coremltools/lib/python3.6/site-packages/coremltools/converters/keras/_keras_converter.py", line 745, in convert

custom_conversion_functions=custom_conversion_functions)


 "/home/xuzh/convert_yolo_to_coreml/coremltools/lib/python3.6/site-packages/coremltools/converters/keras/_keras_converter.py", line 543, in convertToSpec

custom_objects=custom_objects)


 "/home/xuzh/convert_yolo_to_coreml/coremltools/lib/python3.6/site-packages/coremltools/converters/keras/_keras2_converter.py", line 350, in _convert

image_scale = image_scale)


 "/home/xuzh/convert_yolo_to_coreml/coremltools/lib/python3.6/site-packages/coremltools/models/neural_network.py", line 2542, in set_pre_processing_parameters

channels, height, width = array_shape
ValueError: not enough values to unpack (expected 3, got 1)
