: Base class for all neural network modules.
Your models should also subclass this class. Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:`to`, etc. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool Initializes internal Module state, shared by both nn.Module and ScriptModule. ### Ancestors (in MRO) * torch.nn.modules.module.Module ### Class variables `dump_patches: bool` : `training: bool` : ### Methods `forward(self, inputs) ‑> Callable[..., Any]` : Defines the computation performed at every call. Should be overridden by all subclasses. .. note:: Although the recipe for forward pass needs to be defined within this function, one should call the :class:`Module` instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
FeedForwardTrainer(batch_size: int = 8, lr: float = 0.0001, epoch: int = 50, dropout_chance: int = 0.2, loss: str = 'mse', metrics: List[str] = None, hidden_layers: List[int] = None, hidden_activation: str = 'relu', last_activation: str = 'sigmoid', input_units: int = 8, output_units: int = 1, **kwargs)
: Base class for all PyTorch based trainer steps. All pytorch based
trainings should use this as the base class. An example is available
Constructor for the BaseTrainerStep. All subclasses used for custom training of user machine learning models should implement the `run_fn` `model_fn` and `input_fn` methods used for control flow, model training and input data preparation, respectively. Args: serving_model_dir: Directory indicating where to save the trained model. transform_output: Output of a preceding transform component. train_files: String, file pattern of the location of TFRecords for model training. Intended for use in the input_fn. eval_files: String, file pattern of the location of TFRecords for model evaluation. Intended for use in the input_fn. log_dir: Logs output directory. schema: Schema file from a preceding SchemaGen. ### Ancestors (in MRO) * zenml.core.steps.trainer.pytorch_trainers.torch_base_trainer.TorchBaseTrainerStep * zenml.core.steps.trainer.base_trainer.BaseTrainerStep * zenml.core.steps.base_step.BaseStep ### Methods `input_fn(self, file_pattern: List[str], tf_transform_output: tensorflow_transform.output_wrapper.TFTransformOutput)` : Class method for loading data from TFRecords saved to a location on disk. Override this method in subclasses to define your own custom data preparation flow. Args: file_pattern: File pattern matching saved TFRecords on disk. tf_transform_output: Output of the preceding Transform / Preprocessing component. Returns: dataset: A tf.data.Dataset constructed from the input file pattern and transform. `model_fn(self, train_dataset, eval_dataset)` : Class method defining the training flow of the model. Override this in subclasses to define your own custom training flow. Args: train_dataset: tf.data.Dataset containing the training data. eval_dataset: tf.data.Dataset containing the evaluation data. Returns: model: A trained machine learning model. `run_fn(self)` : Class method defining the control flow of the training process inside the TFX Trainer Component Executor. Override this method in subclasses to define your own custom training flow.