[GitHub] [incubator-mxnet] Justobe edited a comment on issue #17250: mxnet.base.MXNetError: Error in operator transpose176: [02:14:06] src/operator/tensor/./matrix_op-inl.h:354: Check failed: shp.ndim() == param.axes.ndim() (-1 vs. 4)

[GitBox <gi...@apache.org>]

Justobe edited a comment on issue #17250: mxnet.base.MXNetError: Error in operator transpose176: [02:14:06] src/operator/tensor/./matrix_op-inl.h:354: Check failed: shp.ndim() == param.axes.ndim() (-1 vs. 4)
URL: https://github.com/apache/incubator-mxnet/issues/17250#issuecomment-572346111
 
 
   @ptrendx I really appreciate your reply! I got another exception when I changed the version from 1.5 to 1.6 (both the version you provided and version mxnet-cu101_1.6.0b20191122). I reconfirmed that this exception only occur on the MXNET backend.
   
   > /root/anaconda3/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:94: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md
   >   train_symbol = func(*args, **kwargs)
   > /root/anaconda3/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:97: UserWarning: MXNet Backend performs best with `channels_first` format. Using `channels_last` will significantly reduce performance due to the Transpose operations. For performance improvement, please use this API`keras.utils.to_channels_first(x_input)`to transform `channels_last` data to `channels_first` format and also please change the `image_data_format` in `keras.json` to `channels_first`.Note: `x_input` is a Numpy tensor or a list of Numpy tensorRefer to: https://github.com/awslabs/keras-apache-mxnet/tree/master/docs/mxnet_backend/performance_guide.md
   >   test_symbol = func(*args, **kwargs)
   > /root/anaconda3/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:94: UserWarning: MXNet Backend uses `channels_first` format. Axis for BatchNorm should ideally be `1`.Provided - `-1`. Performance can be significantly lower!
   >   train_symbol = func(*args, **kwargs)
   > /root/anaconda3/lib/python3.6/site-packages/keras/backend/mxnet_backend.py:97: UserWarning: MXNet Backend uses `channels_first` format. Axis for BatchNorm should ideally be `1`.Provided - `-1`. Performance can be significantly lower!
   >   test_symbol = func(*args, **kwargs)
   > Traceback (most recent call last):
   >   File "crash_checker.py", line 44, in <module>
   >     model = keras.models.load_model(file_path,custom_objects=custom_objects())
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/engine/saving.py", line 496, in load_model
   >     model = _deserialize_model(f, custom_objects, compile)
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/engine/saving.py", line 302, in _deserialize_model
   >     model = model_from_config(model_config, custom_objects=custom_objects)
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/engine/saving.py", line 535, in model_from_config
   >     return deserialize(config, custom_objects=custom_objects)
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/layers/__init__.py", line 55, in deserialize
   >     printable_module_name='layer')
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/utils/generic_utils.py", line 145, in deserialize_keras_object
   >     list(custom_objects.items())))
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/engine/sequential.py", line 301, in from_config
   >     model.add(layer)
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/engine/sequential.py", line 181, in add
   >     output_tensor = layer(self.outputs[0])
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/engine/base_layer.py", line 470, in __call__
   >     output = self.call(inputs, **kwargs)
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/layers/convolutional.py", line 175, in call
   >     dilation_rate=self.dilation_rate)
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/backend/mxnet_backend.py", line 3705, in conv2d
   >     padding_mode=padding, data_format=data_format)
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/backend/mxnet_backend.py", line 94, in func_wrapper
   >     train_symbol = func(*args, **kwargs)
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/backend/mxnet_backend.py", line 5047, in _convnd
   >     filter_dilation)
   >   File "/root/anaconda3/lib/python3.6/site-packages/keras/backend/mxnet_backend.py", line 4870, in _preprocess_padding_mode
   >     for i in range(nd)])
   > ValueError: not enough values to unpack (expected 3, got 0)
   

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