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scikeras.wrappers.KerasClassifier

scikeras.wrappers.KerasClassifier

Visibility: public Uploaded 07-05-2021 by Continuous Integration sklearn==0.22.2.post1 numpy>=1.11.0 scipy>=0.17.0 joblib>=0.11 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.22.2.post1
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Implementation of the scikit-learn classifier API for Keras. Below are a list of SciKeras specific parameters. For details on other parameters, please see the see the `tf.keras.Model documentation `_.

Parameters

batch_sizeNumber of samples per gradient update This will be applied to both `fit` and `predict`. To specify different numbers, pass `fit__batch_size=32` and `predict__batch_size=1000` (for example) To auto-adjust the batch size to use all samples, pass `batch_size=-1`default: 32
build_fndefault: null
callbacksdefault: null
class_weightWeights associated with classes in the form ``{class_label: weight}`` If not given, all classes are supposed to have weight one The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.default: null
epochsdefault: 5
lossThe loss function to use for training This can be a string for Keras' built in losses, an instance of tf.keras.losses.Loss or a class inheriting from tf.keras.losses.Loss Only strings and classes support parameter routingdefault: "categorical_crossentropy"
metricsdefault: null
modelUsed to build the Keras Model. When called, must return a compiled instance of a Keras Model to be used by `fit`, `predict`, etc If None, you must implement ``_keras_build_fn``default: {"oml-python:serialized_object": "function", "value": "__main__.cnn_model_1"}
optimizerThis can be a string for Keras' built in optimizers, an instance of tf.keras.optimizers.Optimizer or a class inheriting from tf.keras.optimizers.Optimizer Only strings and classes support parameter routingdefault: "Adam"
random_stateSet the Tensorflow random number generators to a reproducible deterministic state using this seed Pass an int for reproducible results across multiple function callsdefault: null
run_eagerlydefault: false
shuffledefault: true
validation_batch_sizedefault: null
validation_splitdefault: 0.0
verbosedefault: 1
warm_startIf True, subsequent calls to fit will _not_ reset the model parameters but *will* reset the epoch to zero If False, subsequent fit calls will reset the entire model This has no impact on partial_fit, which always trains for a single epoch starting from the current epochdefault: false

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