reduce mlp number of parameters for testing
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@ -35,7 +35,6 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
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super().__init__(**kwargs)
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super().__init__(**kwargs)
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model_training_params = self.freqai_info.get("model_training_parameters", {})
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model_training_params = self.freqai_info.get("model_training_parameters", {})
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self.n_hidden: int = model_training_params.get("n_hidden", 1024)
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self.max_iters: int = model_training_params.get("max_iters", 100)
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self.max_iters: int = model_training_params.get("max_iters", 100)
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self.batch_size: int = model_training_params.get("batch_size", 64)
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self.batch_size: int = model_training_params.get("batch_size", 64)
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self.learning_rate: float = model_training_params.get("learning_rate", 3e-4)
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self.learning_rate: float = model_training_params.get("learning_rate", 3e-4)
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@ -89,13 +89,12 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
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if 'PyTorchClassifierMultiTarget' in model:
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if 'PyTorchClassifierMultiTarget' in model:
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model_save_ext = 'zip'
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model_save_ext = 'zip'
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freqai_conf['freqai']['model_training_parameters'].update({
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freqai_conf['freqai']['model_training_parameters'].update({
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"n_hidden": 1024,
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"max_iters": 1,
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"max_iters": 100,
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"batch_size": 64,
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"batch_size": 64,
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"learning_rate": 3e-4,
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"learning_rate": 3e-4,
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"max_n_eval_batches": None,
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"max_n_eval_batches": None,
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"model_kwargs": {
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"model_kwargs": {
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"hidden_dim": 1024,
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"hidden_dim": 32,
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"dropout_percent": 0.2,
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"dropout_percent": 0.2,
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"n_layer": 1,
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"n_layer": 1,
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}
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}
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