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How to install USE successfully? #28
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hi, what is the version of your installed tensorflow and tensorflow-hub? |
I installed tensorflow==2.2.0 and tensorflow-hub==0.7.0, but it shows a warning when I Collecting tensorflow_hub==0.7.0 Do you think I am supposed to use another version of tensor flow? |
yes, please check the requirements.txt file, which logs the tensorflow and tensorflow-hub version you should use to make it work. |
Thanks! I checked the Besides, I found an example code posted on website of Again, thx so much for your sharing your code! |
How about creating a new environment (like a conda environment) and install all packages by running this command: pip install requirements.txt |
Update: I think this problem about loading USE is caused by the version of Tensorflow. It should be run under tensorflow v1.0. |
Thank you so much for the updates! |
When I run
module_url = "https://tfhub.dev/google/universal-sentence-encoder-large/3"
self.embed = hub.Module(module_url)
there is error informed as below:
RuntimeError Traceback (most recent call last)
in ()
1 module_url = "https://tfhub.dev/google/universal-sentence-encoder-large/3"
----> 2 hub.Module(module_url)
/usr/local/lib/python3.6/dist-packages/tensorflow_hub/module.py in init(self, spec, trainable, name, tags)
174 name=self._name,
175 trainable=self._trainable,
--> 176 tags=self._tags)
177 # pylint: enable=protected-access
178
/usr/local/lib/python3.6/dist-packages/tensorflow_hub/native_module.py in _create_impl(self, name, trainable, tags)
384 trainable=trainable,
385 checkpoint_path=self._checkpoint_variables_path,
--> 386 name=name)
387
388 def _export(self, path, variables_saver):
/usr/local/lib/python3.6/dist-packages/tensorflow_hub/native_module.py in init(self, spec, meta_graph, trainable, checkpoint_path, name)
443 # TPU training code.
444 with scope_func():
--> 445 self._init_state(name)
446
447 def _init_state(self, name):
/usr/local/lib/python3.6/dist-packages/tensorflow_hub/native_module.py in _init_state(self, name)
446
447 def _init_state(self, name):
--> 448 variable_tensor_map, self._state_map = self._create_state_graph(name)
449 self._variable_map = recover_partitioned_variable_map(
450 get_node_map_from_tensor_map(variable_tensor_map))
/usr/local/lib/python3.6/dist-packages/tensorflow_hub/native_module.py in _create_state_graph(self, name)
503 meta_graph,
504 input_map={},
--> 505 import_scope=relative_scope_name)
506
507 # Build a list from the variable name in the module definition to the actual
/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py in import_meta_graph(meta_graph_or_file, clear_devices, import_scope, **kwargs)
1460 return _import_meta_graph_with_return_elements(meta_graph_or_file,
1461 clear_devices, import_scope,
-> 1462 **kwargs)[0]
1463
1464
/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py in _import_meta_graph_with_return_elements(meta_graph_or_file, clear_devices, import_scope, return_elements, **kwargs)
1470 """Import MetaGraph, and return both a saver and returned elements."""
1471 if context.executing_eagerly():
-> 1472 raise RuntimeError("Exporting/importing meta graphs is not supported when "
1473 "eager execution is enabled. No graph exists when eager "
1474 "execution is enabled.")
RuntimeError: Exporting/importing meta graphs is not supported when eager execution is enabled. No graph exists when eager execution is enabled.
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