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        "author": "Shkarupa Alex",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Software Development",
            "Topic :: Software Development :: Libraries",
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        "description": "# tfseqestimator\n\nSequence estimators for TensorFlow.\n\n\n## Available estimators\n\n- FullSequenceClassifier: one class for a whole sequence\n- FullSequenceRegressor: one value for a whole sequence\n- SequenceItemsClassifier: one class for each sequence item\n- SequenceItemsRegressor: one value for each sequence item\n\n\nUsage\n-----\n\n```python\nfrom tfseqestimator import FullSequenceClassifier, RnnType\nimport tensorflow.contrib.feature_column as contrib_features\n\ntoken_sequence = contrib_features.sequence_categorical_column_with_hash_bucket(...)\ntoken_emb = contrib_features.embedding_column(categorical_column=token_sequence, ...)\n\nestimator = FullSequenceClassifier(\n    sequence_feature_columns=[token_emb],\n    rnn_type=RnnType.REGULAR_STACKED_LSTM,\n    rnn_layers=[32, 16]\n)\n\n# Input builders\ndef input_fn_train: # returns x, y\n  pass\nestimator.train(input_fn=input_fn_train, steps=100)\n\ndef input_fn_eval: # returns x, y\n  pass\nmetrics = estimator.evaluate(input_fn=input_fn_eval, steps=10)\n\ndef input_fn_predict: # returns x, None\n  pass\npredictions = estimator.predict(input_fn=input_fn_predict)\n```",
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