尝试将TensorFlow保存模型转换为TensorFlow.js模型时出错

Question

I have successfully trained a DNNClassifier to classify texts (posts from an online discussion board). I've created and saved my model using this code:

embedded_text_feature_column = hub.text_embedding_column(
    key="sentence",
    module_spec="https://tfhub.dev/google/nnlm-de-dim128/1")
feature_columns = [embedded_text_feature_column]
estimator = tf.estimator.DNNClassifier(
    hidden_units=[500, 100],
    feature_columns=feature_columns,
    n_classes=2,
    optimizer=tf.train.AdagradOptimizer(learning_rate=0.003))
feature_spec = tf.feature_column.make_parse_example_spec(feature_columns)
serving_input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
estimator.export_savedmodel(export_dir_base="/my/dir/base", serving_input_receiver_fn=serving_input_receiver_fn)

Now I want to convert my saved model to use it with the JavaScript version of TensorFlow, tf.js , using the tfjs-converter .

When I issue the following command:

tensorflowjs_converter --input_format=tf_saved_model --output_node_names='dnn/head/predictions/str_classes,dnn/head/predictions/probabilities' --saved_model_tags=serve /my/dir/base /my/export/dir

…I get this error message:

ValueError: Node 'dnn/input_from_feature_columns/input_layer/sentence_hub_module_embedding/module_apply_default/embedding_lookup_sparse/embedding_lookup' expects to be colocated with unknown node 'dnn/input_from_feature_columns/input_layer/sentence_hub_module_embedding

I assume I'm doing something wrong when saving the model.

What is the correct way to save an estimator model so that it can be converted with tfjs-converter ?

The source code of my project can be found on GitHub.

Answer 1

You can try this and I think this will work. Just input your input format in code.

tensorflowjs_converter --input_format keras \
                       path/to/my_model.h5 \
                       path/to/tfjs_target_dir