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shenweichen
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update docs
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docs/source/History.md

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# History
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- 09/07/2019 : [v0.6.1](https://github.com/shenweichen/DeepCTR/releases/tag/v0.6.1) released.Fix bugs in `CCPM` and `DynamicGRU`.
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- 09/08/2019 : [v0.6.1](https://github.com/shenweichen/DeepCTR/releases/tag/v0.6.1) released.Fix bugs in `CCPM` and `DynamicGRU`.
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- 08/02/2019 : [v0.6.0](https://github.com/shenweichen/DeepCTR/releases/tag/v0.6.0) released.Now DeepCTR is compatible with tensorflow `1.14` and `2.0.0`.
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- 07/21/2019 : [v0.5.2](https://github.com/shenweichen/DeepCTR/releases/tag/v0.5.2) released.Refactor `Linear` Layer.
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- 07/10/2019 : [v0.5.1](https://github.com/shenweichen/DeepCTR/releases/tag/v0.5.1) released.Add [FiBiNET](./Features.html#fibinet-feature-importance-and-bilinear-feature-interaction-network).

docs/source/index.rst

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News
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-----
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09/08/2019 : Start the development of [DeepCTR-PyTorch](https://github.com/shenweichen/deepctr-pytorch),welcome to join us!! . `Changelog <https://github.com/shenweichen/DeepCTR/releases/tag/v0.6.1>`_
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08/02/2019 : Now DeepCTR is compatible with tensorflow `1.14` and `2.0.0`. `Changelog <https://github.com/shenweichen/DeepCTR/releases/tag/v0.6.0>`_
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07/21/2019 : Refactor Linear Layer. `Changelog <https://github.com/shenweichen/DeepCTR/releases/tag/v0.5.2>`_
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07/10/2019 : Add `FiBiNEt <./Features.html#fibinet-feature-importance-and-bilinear-feature-interaction-network>`_ . `Changelog <https://github.com/shenweichen/DeepCTR/releases/tag/v0.5.1>`_
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.. toctree::
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:maxdepth: 2
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:caption: Home:

examples/run_classification_criteo_multi_gpu.py

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# 4.Define Model,train,predict and evaluate
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model = DeepFM(linear_feature_columns, dnn_feature_columns, task='binary')
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#model = multi_gpu_model(model, gpus=2)
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model = multi_gpu_model(model, gpus=2)
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model.compile("adam", "binary_crossentropy",
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metrics=['binary_crossentropy'], )

tests/utils.py

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print(model_name+" test train valid pass!")
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model.save_weights(model_name + '_weights.h5')
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model.load_weights(model_name + '_weights.h5')
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os.remove(model_name + '_weights.h5')
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os.remove(model_name + '_weights.h5')
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print(model_name+" test save load weight pass!")
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if check_model_io:
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save_model(model, model_name + '.h5')
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model = load_model(model_name + '.h5', custom_objects)
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os.remove(model_name + '.h5')
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os.remove(model_name + '.h5')
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print(model_name + " test save load model pass!")
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print(model_name + " test pass!")

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