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        "author": "Pedro A. Favuzzi",
        "author_email": "pa.favuzzi@gmail.com",
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        "description": "# TfExperiment\n\nA simple library to manage Tensorflow experiments though git and reduce boilerplate.\nCompatible with tf 1.x\n\n# Usage\n\nThis library relies of git to manage experiments. Each experiment should be a unique git branch, and the name of the experiment, if not give, will be the current git branch.\n\n```python\nexperiment = tfExperiment.Experiment(finalizeGraph = False)\nexperiment.saveGraph()\n\n# output\n# > graph location ======================================>\n# > tensorboard --logdir  output\\experimentName\\graph\n\n# with with\nwith experiment.trainingSession(epochs = 125, saveAfter = 2, testAfter = 2) as ts:\n    ts.saveGraph() # function to save the graph\n    ts.trainCallback = runTrainingCallback\n    ts.testCallback = runTestCallback\n\n# as function\nexperiment.train(runTrainingCallback)\nexperiment.test(runTestCallback)\n```\n\n## API\n\n### ```__init__(name = None, finalizeGraph = False, location = os.path.join(os.getcwd(), 'output'))```\n\n* ```name: string```: Name of the experiment, if no name is provided the name of the current git branch will be used.\n\n* ```finalizeGraph: bool```: Finalizes the graph. *Attention* I have not tried this feature much.\n\n* ```location: string```: absolute path where the experiment results where saved in a folder with same name as name\n\n\n### ```train(trainCallback, epochs = 1, saveModelAfter = 2, saveGraph = False, testCallback = None, testAfter = 0)```\n\nRuns the training and validates/test the model\n\n* ```trainCallback: function```: Function to be run at each epoch. This should contain your loop with the training actions to execute for each batch. The training callback can take 2 parameters: session (current tf.session), and env (if env is used you should use the exact name) experiment environment with access to functionalities like timer and dataSaver. \n\n* ```epochs: integer```: Number of epochs to run, that is to say the number of times the traininCallbacks will be called. *Attention:* the experiment object keeps track of the number of epochs run so far, so if you call ```experiment.train``` again, the epoch number will continue to grow from the last epoch number.\n\n* ```saveModelAfter: integer```: Save the model after n epochs have run. This only considers the current run.\n\n* ```saveGraph: bool```: If we should save the graph at the current run.\n\n* ```testCallback: function```: Function to call to test/validate the current network. Similar to trainCallback.\n\n* ```testAfter: integer```: test the model after n epochs have run. This only considers the current run.\n\n### ```test(testCallback)```\n\nRuns the testing/validation of the model once\n\n* ```testCallback: function```: Function to call to test/validate the current network. Similar to trainCallback.\n\n### ```env: Box object```\n\nThe env object contains\n\n* ```env.training.currentEpoch: integer```: number of epochs since the instance was initialized.\n* ```env.training.currentEpoch: integer```: number of epochs since the instance was initialized.\n* ```env.training.dataSavePath: path string```: path in which the data will be used if dataSaver is used during training.\n* ```env.training.dataSaver: dataSaver Instance```: dataSaver instance (initialized with env.training.dataSavePath) for training to the training file.\n* ```env.testing.dataSavePath: path string``` path in which the data will be used if dataSaver is used during testing.\n* ```env.testing.dataSaver: dataSaver Instance```: dataSaver instance (initialized with env.testing.dataSavePath) for testing to the training file.\n\n### Proposed New API\n\n```python\ndef TrainExperiment(Experiment):\n    def __init__(self, constructor, ...):\n        #someconfig\n        #self.nrTotEpochs\n        #self.epochsToValidateAfter\n        #...\n\n    def beforeEpoch\n    def afterEpoch\n\n    def beforeSave\n    def afterSave\n\n    def beforeTest\n    def afterTest\n\n    def beforeIteration\n    def afterIteration\n\n    def train(session, data, dataProvider = None):\n        return 0 #trainingLoopPerSession\n\n    def validate(session, data, dataProvider = None):\n        return 0 #trainingLoopPerSession\n\n\nexperiment(TrainExperiment)\n```\n\n",
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