DeepExplain provides a unified framework for state-of-the-art gradient and perturbation-based attribution methods. It can be used by researchers and practitioners for better undertanding the recommended existing models, as well for benchmarking other attribution methods.
It supports Tensorflow as well as Keras with Tensorflow backend. Only Tensorflow V1 is supported. For V2, there is an open pull-request, that works if eager execution is disabled.
Implements the following methods:
Gradient-based attribution methods
- Saliency maps
- Gradient * Input
- Integrated Gradients
- DeepLIFT, in its first variant with Rescale rule (*)
- ε-LRP (*)
Methods marked with (*) are implemented as modified chain-rule, as better explained in Towards better understanding of gradient-based attribution methods for Deep Neural Networks, Ancona et al, ICLR 2018. As such, the result might be slightly different from the original implementation.
Pertubration-based attribution methods
- Occlusion, as an extension of the grey-box method by Zeiler et al.
- Shapley Value sampling
Consider a network and a specific input to this network (eg. an image, if the network is trained for image classification). The input is multi-dimensional, made of several features. In the case of images, each pixel can be considered a feature. The goal of an attribution method is to determine a real value R(x_i) for each input feature, with respect to a target neuron of interest (for example, the activation of the neuron corresponsing to the correct class).
When the attributions of all input features are arranged together to have the same shape of the input sample we talk about attribution maps (as in the picture below), where red and blue colors indicate respectively features that contribute positively to the activation of the target output and features having a suppressing effect on it.
This can help to better understand the network behavior, which features mostly contribute to the output and possible reasons for missclassification.
pip install -e git+https://github.com/marcoancona/DeepExplain.git#egg=deepexplain
Notice that DeepExplain assumes you already have installed Tensorflow > 1.0 and (optionally) Keras > 2.0.
Working examples for Tensorflow and Keras can be found in the example folder of the repository. DeepExplain
consists of a single method: explain(method_name, target_tensor, input_tensor, samples, ...args).
| Parameter name | Short name | Type | Description |
|---|---|---|---|
method_name |
string, required | Name of the method to run (see Which method to use?). | |
target_tensor |
T |
Tensor, required | Tensorflow Tensor object representing the output of the model for which attributions are seeked (see Which tensor to target?). |
input_tensor |
X |
Tensor, required | Symbolic input to the network. |
input_data |
xs |
numpy array, required | Batch of input samples to be fed to X and for which attributions are seeked. Notice that the first dimension must always be the batch size. |
target_weights |
ys |
numpy array, optional | Batch of weights to be applied to T if this has more than one output. Usually necessary on classification problems where there are multiple output units and we need to target a specific one to generate explanations for. In this case, ys can be provided with the one-hot encoding of the desired unit. |
batch_size |
int, optional | By default, DeepExplain will try to evaluate the model using all data in xs at the same time. If xs contains many samples, it might be necessary to split the processing in batches. In this case, providing a batch_size greater than zero will automatically split the evaluation into chunks of the given size. |
|
...args |
various, optional | Method-specific parameters (see below). |
The method explain must be called within a DeepExplain context:
# Pseudo-code
from deepexplain.tensorflow import DeepExplain
# Option 1. Create and train your model within a DeepExplain context
with DeepExplain(session=...) as de: # < enter DeepExplain context
model = init_model() # < construct the model
model.fit() # < train the model
attributions = de.explain(...) # < compute attributions
# Option 2. First create and train your model, then apply DeepExplain.
# IMPORTANT: in order to work correctly, the graph to analyze
# must always be (re)constructed within the context!
model = init_model() # < construct the model
model.fit() # < train the model
with DeepExplain(session=...) as de: # < enter DeepExplain context
new_model = init_model() # < assumes init_model() returns a *new* model with the weights of `model`
attributions = de.explain(...) # < compute attributionsWhen initializing the context, make sure to pass the session parameter:
# With Tensorflow
import tensorflow as tf
# ...build model
sess = tf.Session()
# ... use session to train your model if necessary
with DeepExplain(session=sess) as de:
...
# With Keras
import keras
from keras import backend as K
model = Sequential() # functional API is also supported
# ... build model and train
with DeepExplain(session=K.get_session()) as de:
...See concrete examples here.
DeepExplain supports several methods. The main partition is between gradient-based methods and perturbation-based methods. The former are faster, given that they estimate attributions with a few forward and backward iterations through the network. The latter perturb the input and measure the change in output with respect to the original input. This requires to sequentially test each feature (or group of features) and therefore takes more time, but tends to produce smoother results.
Cooperative game theory suggests Shapley Values as a unique way to distribute attribution to features such that some important theoretical properties are satisfied. Unfortunately, computing Shapley Values exactly is prohibitively expensive, therefore DeepExplain provides a sampling-based approximation. By changing the samples parameters, one can adjust the trade-off between performance and error. Notice that this method will still be significantly slower than other methods in this library.
Some methods allow tunable parameters. See the table below.
| Method | method_name |
Optional parameters | Notes |
|---|---|---|---|
| Saliency | saliency |
[Gradient] Only positive attributions. | |
| Gradient * Input | grad*input |
[Gradient] Fast. May be affected by noisy gradients and saturation of the nonlinerities. | |
| Integrated Gradients | intgrad |
steps, baseline |
[Gradient] Similar to Gradient * Input, but performs steps iterations (default: 100) though the network, varying the input from baseline (default: zero) to the actual provided sample. When provided, baseline must be a numpy array with the size of the input (but no batch dimension since the same baseline will be used for all inputs in the batch). |
| epsilon-LRP | elrp |
epsilon |
[Gradient]Computes Layer-wise Relevance Propagation. Only recommanded with ReLU or Tanh nonlinearities. Value for epsilon must be greater than zero (default: .0001). |
| DeepLIFT (Rescale) | deeplift |
baseline |
[Gradient] In most cases a faster approximation of Integrated Gradients. Do not apply to networks with multiplicative units (ie. LSTM or GRU). When provided, baseline must be a numpy array with the size of the input, without the batch dimension (default: zero). |
| Occlusion | occlusion |
window_shape, step |
[Perturbation] Computes rolling window view of the input array and replace each window with zero values, measuring the effect of the perturbation on the target output. The optional parameters window_shape and step behave like in skimage. By default, each feature is tested independently (window_shape=1 and step=1), however this might be extremely slow for large inputs (such as ImageNet images). When the input presents some local coherence (eg. images), you might prefer larger values for window_shape. In this case the attributions of the features in each window will be summed up. Notice that the result might vary significantly for different window sizes. |
| Shapley Value sampling | shapley_sampling |
samples, sampling_dims |
[Perturbation] Computes approximate Shapley Values by sampling samples times each input feature. Notice that this method can be significantly slower than all the others as it runs the network samples*n times, where n is the number of input features in your input. The parameter sampling_dims (a list of integers) can be used to select which dimensions should be sampled. For example, if the inputs are RGB images, sampling_dims=[1,2] would sample pixels considering the three color channels atomic. Instead sampling_dims=[1,2,3] (default) will samples over the channels as well. |
In general, any tensor that represents the activation of any hidden or output neuron can be user as target_tensor. If your network performs a classification task (ie. one output neuron for each possible class) you might want to target the neuron corresponding to the correct class for a given sample, such that the attribution map might help you undertand the reasons for this neuron to (not) activate. However you can also target the activation of another class, for example a class that is often missclassified, to have insight about features that activate this class.
Important: Tensors in Tensorflow and Keras usually include the activations of all neurons of a layer. If you pass such a tensor to explain you will get the sum attribution map for all neurons the Tensor refers to. If you want to target a specific neuron you need either to slice the component you are interested in or multiply it for a binary mask that only select the target neuron.
# Example on MNIST (classification, with 10 output classes)
X = Placeholder(...) # input tensor
T = model(X) # output layer, 2-dimensional Tensor of shape (1, 10), where first dimension is the batch size
ys = [[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]] # numpy array of shape (1, 10) with one-hot encoding of labels
# We need to target only one of the 10 output units in `T`
# Option 1 (recommanded): use the `ys` parameter
de.explain('method_name', T, X, xs, ys=ys)
# Option 2: manually mask the target. This will not work with batch processing.
T *= ys # < masked target tensor: only the second component of `logits` will be used to compute attributions
de.explain('method_name', T, X, xs)Softmax: if the network last activation is a Softmax, it is recommanded to target the activations before this normalization.
If you need to run explain() multiple times (for example, new data to process with the same model comes in over time) it is recommanded that you use the Explainer API. This provides a way to compile the graph operations needed to generate the explanations and evaluate this graph in two different steps.
Within a DeepExplain context (de), call de.get_explainer(). This method takes the same arguments of explain() except xs, ys and batch_size. It returns an explainer object (explainer) which provides a run() method. Call explainer.run(xs, [ys], [batch_size]) to generate the explanations. Calling run() multiple times will not add new operations to the computational graph.
# Normal API:
for i in range(100):
# The following line will become slower and slower as new operations are added to the computational graph at each iteration
attributions = de.explain('saliency', T, X, xs[i], ys=ys[i], batch_size=3)
# Use the Explainer API instead:
# First create an explainer
explainer = de.get_explainer('saliency', T, X)
for i in range(100):
# Then generate explanations for some data without slowing things down
attributions = explainer.run(xs[i], ys=ys[i], batch_size=3) The most common cause of ValueError("None values not supported.") is run() being called with a tensor_input and target_tensor that are disconnected in the backpropagation. This is common when an embedding lookup layer is used, since the lookup operation does not propagate the gradient. To generate attributions for NLP models, the input of DeepExplain should be the result of the embedding lookup instead of the original model input. Then, attributions for each word are found by summing up along the appropriate dimension of the resulting attribution matrix.
Tensorflow pseudocode:
input_x = graph.get_operation_by_name("input_x").outputs[0]
# Get a reference to the embedding tensor
embedding = graph.get_operation_by_name("embedding").outputs[0]
pre_softmax = graph.get_operation_by_name("output/scores").outputs[0]
# Evaluate the embedding tensor on the model input (in other words, perform the lookup)
embedding_out = sess.run(embedding, {input_x: x_test})
# Run DeepExplain with the embedding as input
attributions = de.explain('elrp', pre_softmax * y_test_logits, embedding, embedding_out)Models with multiple inputs are supported for gradient-based methods. Instead, the Occlusion method will raise an exception if
called on a model with multiple inputs (how perturbation should be generated for multiple inputs is actually not well defined).
For a minimal (toy) example see the example folder.
DeepExplain is still in active development. If you experience problems, feel free to open an issue. Contributions to extend the functinalities of this framework and/or to add support for other methods are welcome.
MIT