neuron¶
NeuronGradient¶

class
captum.attr._core.neuron_gradient.
NeuronGradient
(forward_func, layer, device_ids=None)[source]¶  Parameters
forward_func (callable) – The forward function of the model or any modification of it
layer (torch.nn.Module) – Layer for which attributions are computed. Output size of attribute matches this layer’s input or output dimensions, depending on whether we attribute to the inputs or outputs of the layer, corresponding to attribution of each neuron in the input or output of this layer. Currently, it is assumed that the inputs or the outputs of the layer, depending on which one is used for attribution, can only be a single tensor.
device_ids (list(int)) – Device ID list, necessary only if forward_func applies a DataParallel model. This allows reconstruction of intermediate outputs from batched results across devices. If forward_func is given as the DataParallel model itself, then it is not necessary to provide this argument.

attribute
(inputs, neuron_index, additional_forward_args=None, attribute_to_neuron_input=False)[source]¶ Computes the gradient of the output of a particular neuron with respect to the inputs of the network.
 Parameters
inputs (tensor or tuple of tensors) – Input for which neuron gradients are computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple of the input tensors should be provided. It is assumed that for all given input tensors, dimension 0 corresponds to the number of examples, and if multiple input tensors are provided, the examples must be aligned appropriately.
neuron_index (int or tuple) – Index of neuron in output of given layer for which attribution is desired. Length of this tuple must be one less than the number of dimensions in the output of the given layer (since dimension 0 corresponds to number of examples). An integer may be provided instead of a tuple of length 1.
additional_forward_args (tuple, optional) – If the forward function requires additional arguments other than the inputs for which attributions should not be computed, this argument can be provided. It must be either a single additional argument of a Tensor or arbitrary (nontuple) type or a tuple containing multiple additional arguments including tensors or any arbitrary python types. These arguments are provided to forward_func in order following the arguments in inputs. Note that attributions are not computed with respect to these arguments. Default: None
attribute_to_neuron_input (bool, optional) – Indicates whether to compute the attributions with respect to the neuron input or output. If attribute_to_neuron_input is set to True then the attributions will be computed with respect to neuron’s inputs, otherwise it will be computed with respect to neuron’s outputs. Note that currently it is assumed that either the input or the output of internal neurons, depending on whether we attribute to the input or output, is a single tensor. Support for multiple tensors will be added later. Default: False
 Returns
 attributions (tensor or tuple of tensors):
Gradients of particular neuron with respect to each input feature. Attributions will always be the same size as the provided inputs, with each value providing the attribution of the corresponding input index. If a single tensor is provided as inputs, a single tensor is returned. If a tuple is provided for inputs, a tuple of corresponding sized tensors is returned.
 Return type
tensor or tuple of tensors of attributions
Examples:
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x32x32. >>> net = ImageClassifier() >>> neuron_ig = NeuronGradient(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # To compute neuron attribution, we need to provide the neuron >>> # index for which attribution is desired. Since the layer output >>> # is Nx12x32x32, we need a tuple in the form (0..11,0..31,0..31) >>> # which indexes a particular neuron in the layer output. >>> # For this example, we choose the index (4,1,2). >>> # Computes neuron gradient for neuron with >>> # index (4,1,2). >>> attribution = neuron_ig.attribute(input, (4,1,2))
NeuronIntegratedGradients¶

class
captum.attr._core.neuron_integrated_gradients.
NeuronIntegratedGradients
(forward_func, layer, device_ids=None)[source]¶  Parameters
forward_func (callable) – The forward function of the model or any modification of it
layer (torch.nn.Module) – Layer for which attributions are computed. Output size of attribute matches this layer’s input or output dimensions, depending on whether we attribute to the inputs or outputs of the layer, corresponding to attribution of each neuron in the input or output of this layer. Currently, it is assumed that the inputs or the outputs of the layer, depending on which one is used for attribution, can only be a single tensor.
device_ids (list(int)) – Device ID list, necessary only if forward_func applies a DataParallel model. This allows reconstruction of intermediate outputs from batched results across devices. If forward_func is given as the DataParallel model itself, then it is not necessary to provide this argument.

attribute
(inputs, neuron_index, baselines=None, additional_forward_args=None, n_steps=50, method='gausslegendre', internal_batch_size=None, attribute_to_neuron_input=False)[source]¶ Approximates the integral of gradients for a particular neuron along the path from a baseline input to the given input. If no baseline is provided, the default baseline is the zero tensor. More details regarding the integrated gradient method can be found in the original paper here: https://arxiv.org/abs/1703.01365
Note that this method is equivalent to applying integrated gradients where the output is the output of the identified neuron.
 Parameters
inputs (tensor or tuple of tensors) – Input for which neuron integrated gradients are computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple of the input tensors should be provided. It is assumed that for all given input tensors, dimension 0 corresponds to the number of examples, and if multiple input tensors are provided, the examples must be aligned appropriately.
neuron_index (int or tuple) – Index of neuron in output of given layer for which attribution is desired. Length of this tuple must be one less than the number of dimensions in the output of the given layer (since dimension 0 corresponds to number of examples). An integer may be provided instead of a tuple of length 1.
baselines (scalar, tensor, tuple of scalars or tensors, optional) –
Baselines define the starting point from which integral is computed. Baselines can be provided as:
 a single tensor, if inputs is a single tensor, with
exactly the same dimensions as inputs or the first dimension is one and the remaining dimensions match with inputs.
 a single scalar, if inputs is a single tensor, which will
be broadcasted for each input value in input tensor.
 a tuple of tensors or scalars, the baseline corresponding
to each tensor in the inputs’ tuple can be:  either a tensor with matching dimensions to
corresponding tensor in the inputs’ tuple or the first dimension is one and the remaining dimensions match with the corresponding input tensor.
 or a scalar, corresponding to a tensor in the
inputs’ tuple. This scalar value is broadcasted for corresponding input tensor.
In the cases when baselines is not provided, we internally use zero scalar corresponding to each input tensor.
Default: None
additional_forward_args (tuple, optional) – If the forward function requires additional arguments other than the inputs for which attributions should not be computed, this argument can be provided. It must be either a single additional argument of a Tensor or arbitrary (nontuple) type or a tuple containing multiple additional arguments including tensors or any arbitrary python types. These arguments are provided to forward_func in order following the arguments in inputs. For a tensor, the first dimension of the tensor must correspond to the number of examples. It will be repeated for each of n_steps along the integrated path. For all other types, the given argument is used for all forward evaluations. Note that attributions are not computed with respect to these arguments. Default: None
n_steps (int, optional) – The number of steps used by the approximation method. Default: 50.
method (string, optional) – Method for approximating the integral, one of riemann_right, riemann_left, riemann_middle, riemann_trapezoid or gausslegendre. Default: gausslegendre if no method is provided.
internal_batch_size (int, optional) – Divides total #steps * #examples data points into chunks of size internal_batch_size, which are computed (forward / backward passes) sequentially. For DataParallel models, each batch is split among the available devices, so evaluations on each available device contain internal_batch_size / num_devices examples. If internal_batch_size is None, then all evaluations are processed in one batch. Default: None
attribute_to_neuron_input (bool, optional) – Indicates whether to compute the attributions with respect to the neuron input or output. If attribute_to_neuron_input is set to True then the attributions will be computed with respect to neuron’s inputs, otherwise it will be computed with respect to neuron’s outputs. Note that currently it is assumed that either the input or the output of internal neuron, depending on whether we attribute to the input or output, is a single tensor. Support for multiple tensors will be added later. Default: False
 Returns
 attributions (tensor or tuple of tensors):
Integrated gradients for particular neuron with respect to each input feature. Attributions will always be the same size as the provided inputs, with each value providing the attribution of the corresponding input index. If a single tensor is provided as inputs, a single tensor is returned. If a tuple is provided for inputs, a tuple of corresponding sized tensors is returned.
 Return type
tensor or tuple of tensors of attributions
Examples:
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x32x32. >>> net = ImageClassifier() >>> neuron_ig = NeuronIntegratedGradients(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # To compute neuron attribution, we need to provide the neuron >>> # index for which attribution is desired. Since the layer output >>> # is Nx12x32x32, we need a tuple in the form (0..11,0..31,0..31) >>> # which indexes a particular neuron in the layer output. >>> # For this example, we choose the index (4,1,2). >>> # Computes neuron integrated gradients for neuron with >>> # index (4,1,2). >>> attribution = neuron_ig.attribute(input, (4,1,2))
NeuronConductance¶

class
captum.attr._core.neuron_conductance.
NeuronConductance
(forward_func, layer, device_ids=None)[source]¶  Parameters
forward_func (callable) – The forward function of the model or any modification of it
layer (torch.nn.Module) – Layer for which neuron attributions are computed. Attributions for a particular neuron in the input or output of this layer are computed using the argument neuron_index in the attribute method. Currently, only layers with a single tensor input or output are supported.
layer – Layer for which attributions are computed. Output size of attribute matches this layer’s input or output dimensions, depending on whether we attribute to the inputs or outputs of the layer, corresponding to attribution of each neuron in the input or output of this layer. Currently, it is assumed that the inputs or the outputs of the layer, depending on which one is used for attribution, can only be a single tensor.
device_ids (list(int)) – Device ID list, necessary only if forward_func applies a DataParallel model. This allows reconstruction of intermediate outputs from batched results across devices. If forward_func is given as the DataParallel model itself, then it is not necessary to provide this argument.

attribute
(inputs, neuron_index, baselines=None, target=None, additional_forward_args=None, n_steps=50, method='riemann_trapezoid', internal_batch_size=None, attribute_to_neuron_input=False)[source]¶ Computes conductance with respect to particular hidden neuron. The returned output is in the shape of the input, showing the attribution / conductance of each input feature to the selected hidden layer neuron. The details of the approach can be found here: https://arxiv.org/abs/1805.12233
 Parameters
inputs (tensor or tuple of tensors) – Input for which neuron conductance is computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple of the input tensors should be provided. It is assumed that for all given input tensors, dimension 0 corresponds to the number of examples, and if multiple input tensors are provided, the examples must be aligned appropriately.
neuron_index (int or tuple) – Index of neuron in output of given layer for which attribution is desired. Length of this tuple must be one less than the number of dimensions in the output of the given layer (since dimension 0 corresponds to number of examples). An integer may be provided instead of a tuple of length 1.
baselines (scalar, tensor, tuple of scalars or tensors, optional) –
Baselines define the starting point from which integral is computed and can be provided as:
 a single tensor, if inputs is a single tensor, with
exactly the same dimensions as inputs or the first dimension is one and the remaining dimensions match with inputs.
 a single scalar, if inputs is a single tensor, which will
be broadcasted for each input value in input tensor.
 a tuple of tensors or scalars, the baseline corresponding
to each tensor in the inputs’ tuple can be:  either a tensor with matching dimensions to
corresponding tensor in the inputs’ tuple or the first dimension is one and the remaining dimensions match with the corresponding input tensor.
 or a scalar, corresponding to a tensor in the
inputs’ tuple. This scalar value is broadcasted for corresponding input tensor.
In the cases when baselines is not provided, we internally use zero scalar corresponding to each input tensor.
Default: None
target (int, tuple, tensor or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, no target index is necessary. For general 2D outputs, targets can be either:
 a single integer or a tensor containing a single
integer, which is applied to all input examples
 a list of integers or a 1D tensor, with length matching
the number of examples in inputs (dim 0). Each integer is applied as the target for the corresponding example.
For outputs with > 2 dimensions, targets can be either:
 A single tuple, which contains #output_dims  1
elements. This target index is applied to all examples.
 A list of tuples with length equal to the number of
examples in inputs (dim 0), and each tuple containing #output_dims  1 elements. Each tuple is applied as the target for the corresponding example.
Default: None
additional_forward_args (tuple, optional) – If the forward function requires additional arguments other than the inputs for which attributions should not be computed, this argument can be provided. It must be either a single additional argument of a Tensor or arbitrary (nontuple) type or a tuple containing multiple additional arguments including tensors or any arbitrary python types. These arguments are provided to forward_func in order following the arguments in inputs. For a tensor, the first dimension of the tensor must correspond to the number of examples. It will be repeated for each of n_steps along the integrated path. For all other types, the given argument is used for all forward evaluations. Note that attributions are not computed with respect to these arguments. Default: None
n_steps (int, optional) – The number of steps used by the approximation method. Default: 50.
method (string, optional) – Method for approximating the integral, one of riemann_right, riemann_left, riemann_middle, riemann_trapezoid or gausslegendre. Default: gausslegendre if no method is provided.
internal_batch_size (int, optional) – Divides total #steps * #examples data points into chunks of size internal_batch_size, which are computed (forward / backward passes) sequentially. For DataParallel models, each batch is split among the available devices, so evaluations on each available device contain internal_batch_size / num_devices examples. If internal_batch_size is None, then all evaluations are processed in one batch. Default: None
attribute_to_neuron_input (bool, optional) – Indicates whether to compute the attributions with respect to the neuron input or output. If attribute_to_neuron_input is set to True then the attributions will be computed with respect to neuron’s inputs, otherwise it will be computed with respect to neuron’s outputs. Note that currently it is assumed that either the input or the output of internal neuron, depending on whether we attribute to the input or output, is a single tensor. Support for multiple tensors will be added later. Default: False
 Returns
 attributions (tensor or tuple of tensors):
Conductance for particular neuron with respect to each input feature. Attributions will always be the same size as the provided inputs, with each value providing the attribution of the corresponding input index. If a single tensor is provided as inputs, a single tensor is returned. If a tuple is provided for inputs, a tuple of corresponding sized tensors is returned.
 Return type
tensor or tuple of tensors of attributions
Examples:
>>> # ImageClassifier takes a single input tensor of images Nx3x32x32, >>> # and returns an Nx10 tensor of class probabilities. >>> # It contains an attribute conv1, which is an instance of nn.conv2d, >>> # and the output of this layer has dimensions Nx12x32x32. >>> net = ImageClassifier() >>> neuron_cond = NeuronConductance(net, net.conv1) >>> input = torch.randn(2, 3, 32, 32, requires_grad=True) >>> # To compute neuron attribution, we need to provide the neuron >>> # index for which attribution is desired. Since the layer output >>> # is Nx12x32x32, we need a tuple in the form (0..11,0..31,0..31) >>> # which indexes a particular neuron in the layer output. >>> # Computes neuron conductance for neuron with >>> # index (4,1,2). >>> attribution = neuron_cond.attribute(input, (4,1,2))