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        "author": "Damian Mirecki",
        "author_email": "damian.mirecki@identt.pl",
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        "description": "R&D toolkit\n===========\n\nInstallation\n'''''''''''''''''''\n\n    ::\n\n        pip install rndtools\n\nMini-framework for keras model training\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nWhen you call ``train_model`` function then this framework will do few\nuseful things: 1. it creates dirs when it do not exist 1. it\nautomatically save architecture into ``atrchitecture.json``, 1. plot\nmodel graph 1. save python source code of ``get_model_function`` and\n``training_function``, 1. after each epoch draw loss and accuray chart,\n1. after each epoch save csv file with learning history. 1. saves some\nmeta information about model, for example: 1. processing time 1. best\ntrain and test loss 1. date of model creation\n\nWhat you should do:\n'''''''''''''''''''\n\n1. Implement ``load_data`` function.\n2. Implement function that returns compiled keras model. Function should\n   not have any parameters. Example:\n\n   ::\n\n       def get_model():\n         model = Sequential()\n         model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))\n         model.add(Dense(8, init='uniform', activation='relu'))\n         model.add(Dense(1, init='uniform', activation='sigmoid'))\n\n         model.compile(\n           optimizer=Adam(),\n           loss='binary_crossentropy',\n           metrics=['accuracy']\n         )\n\n        return model\n\n3. Implement function that trains model. Function should return model\n   history. Example: \\``\\` def train(data, model, model_folder,\n   callbacks=None): if callbacks is None: callbacks = []\n\n   ::\n\n        history = model.fit(data.X, data.Y, nb_epoch=150, batch_size=10, callbacks=callbacks)\n\n        return history\n\n   \\`\\`\\ ``Pay attention to callbacks parameter. There are some extra callbacks that you should add to model callbacks.  Also note that as in``\\ data\\ ``parameter function pass what``\\ load_data\\`\n   function returns.\n\nExample:\n\n::\n\n    >>> import rndtools as rnd\n    >>> rnd.train.train_model(\n        model_dir,\n        get_model_function=get_model,\n        training_function=train,\n        loading_data_function=load_data\n    )\n\n    Model path: /home/rd/notebooks/documents-detector/damian/models/in_the_wild/unet_mrz/7\n\n    ------------------------------\n    Creating dirs...\n    ------------------------------\n    ------------------------------\n    Creating and compiling model...\n    ------------------------------\n    ------------------------------\n    Saving architecture...\n    ------------------------------\n    ------------------------------\n    Plotting model...\n    ------------------------------\n    ------------------------------\n    Saving model source code...\n    ------------------------------\n    ------------------------------\n    Loading data...\n    ------------------------------\n    ------------------------------\n    Instantiating callbacks...\n    ------------------------------\n    ------------------------------\n    Training model...\n    ------------------------------\n    Epoch 1/1000\n\n    Finished!\n\nDataset In Parts Generator\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nSometimes there is so many data that it is problem to store it in\nmemory. Then you can use divide your dataset into parts\n``DatasetInPartsGenerator`` that will load this parts in turn, so you\nwill have only part of dataset in memory.\n\nPipeline\n~~~~~~~~\n\nPipeline consists of steps. Each step is a tuple (name of step, step class). Example pipeline:\n\n::\n\n    pipeline = Pipeline(\n        (\n            'grayscale',\n            Grayscale()\n        ),\n        (\n            'threshold',\n            Threshold()\n        ),\n        (\n            'blur',\n            Blur(\n                sigma=1.5\n            )\n        ),\n        (\n            'watershed',\n            Watershed(\n                min_distance=5,\n                threshold_rel=0.1\n            )\n        ),\n        show_progressbar=True\n    )\n\nTo create your own step just inherit from ``Step`` and implement\n``transform`` method:\n\n::\n\n    from rndtools.pipeline import Step\n\n    class CustomStep(Step):\n        def transform(self, params):\n            pass",
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