Model Wrappers¶

trulens.nn.models ¶

The TruLens library is designed to support models implemented via a variety of different popular python neural network frameworks: Keras (with TensorFlow or Theano backend), TensorFlow, and Pytorch. Models developed with different frameworks implement things (e.g., gradient computations) a number of different ways. We define framework specific ModelWrapper instances to create a unified model API, providing the same functionality to models that are implemented in disparate frameworks. In order to compute attributions for a model, we provide a trulens.nn.models.get_model_wrapper function that will return an appropriate ModelWrapper instance.

Some parameters are exclusively utilized for specific frameworks and are outlined in the parameter descriptions.

Functions¶

get_model_wrapper ¶

get_model_wrapper(model: ModelLike, *, logit_layer=None, replace_softmax: bool = False, softmax_layer=-1, custom_objects=None, device: str = None, input_tensors=None, output_tensors=None, internal_tensor_dict=None, default_feed_dict=None, session=None, backend=None, force_eval=True, **kwargs)

Returns a ModelWrapper implementation that exposes the components needed for computing attributions.

PARAMETER	DESCRIPTION
`model`	The model to wrap. If using the TensorFlow 1 backend, this is expected to be a graph object. TYPE: `ModelLike`
`logit_layer`	Supported for Keras and Pytorch models. Specifies the name or index of the layer that produces the logit predictions. DEFAULT: `None`
`replace_softmax`	Supported for Keras models only. If true, the activation function in the softmax layer (specified by `softmax_layer`) will be changed to a `'linear'` activation. TYPE: `bool` DEFAULT: `False`
`softmax_layer`	Supported for Keras models only. Specifies the layer that performs the softmax. This layer should have an `activation` attribute. Only used when `replace_softmax` is true. DEFAULT: `-1`
`custom_objects`	Optional, for use with Keras models only. A dictionary of custom objects used by the Keras model. DEFAULT: `None`
`device`	Optional, for use with Pytorch models only. A string specifying the device to run the model on. TYPE: `str` DEFAULT: `None`
`input_tensors`	Required for use with TensorFlow 1 graph models only. A list of tensors representing the input to the model graph. DEFAULT: `None`
`output_tensors`	Required for use with TensorFlow 1 graph models only. A list of tensors representing the output to the model graph. DEFAULT: `None`
`internal_tensor_dict`	Optional, for use with TensorFlow 1 graph models only. A dictionary mapping user-selected layer names to the internal tensors in the model graph that the user would like to expose. This is provided to give more human-readable names to the layers if desired. Internal tensors can also be accessed via the name given to them by tensorflow. DEFAULT: `None`
`default_feed_dict`	Optional, for use with TensorFlow 1 graph models only. A dictionary of default values to give to tensors in the model graph. DEFAULT: `None`
`session`	Optional, for use with TensorFlow 1 graph models only. A `tf.Session` object to run the model graph in. If `None`, a new temporary session will be generated every time the model is run. DEFAULT: `None`
`backend`	Optional, for forcing a specific backend. String values recognized are pytorch, tensorflow, keras, or tf.keras. DEFAULT: `None`
`force_eval`	_Optional, True will force a model.eval() call for PyTorch models. False will retain current model state DEFAULT: `True`

Returns: ModelWrapper