Update doc to remove Classifier error

Author: Irina Nicolae

docs/guide/usage.rst

@@ -1,6 +1,7 @@
 Running scripts
 ===============
 The library contains three main scripts for:
+
 * training a classifier using (`train.py`)
 * crafting adversarial examples on a trained model through (`generate_adversarial.py`)
 * testing model accuracy on different test sets using (`test_accuracies.py`)
@@ -18,6 +19,7 @@ Some examples of how to use the toolbox when writing your own code can be found
 See `examples/README.md` for more information about what each example does. To run an example, use the following command:
 
 .. code-block:: bash
+
    python3 examples/<example_name>.py
 
 .. _GitHub: https:github.com/IBM/adversarial-robustness-toolbox

docs/index.rst

@@ -23,7 +23,7 @@ The Adversarial Robustness Toolbox contains implementations of the following att
 * Jacobian Saliency Map (`Papernot et al., 2016`_)
 * Universal Perturbation (`Moosavi-Dezfooli et al., 2016`_)
 * Virtual Adversarial Method (`Moosavi-Dezfooli et al., 2015b`_)
-* C&W Attack (`Carlini and Wagner, 2016`_)
+* C&W Attack (`Carlini and Wagner, 2016`_)
 * NewtonFool (`Jang et al., 2017`_)
 
 The following defense methods are also supported:

docs/modules/classifiers.rst

@@ -1,14 +1,3 @@
 :mod:`src.classifiers`
 ======================
 .. automodule:: src.classifiers
-
-Supported models
-----------------
-.. autoclass:: Classifier
-   :members:
-.. autoclass:: CNN
-   :members:
-.. autoclass:: MLP
-   :members:
-.. autoclass:: ResNet
-   :members:

docs/modules/metrics.rst

@@ -2,10 +2,19 @@
 ==================
 .. automodule:: src.metrics
 
-Model robustness metrics
-------------------------
+Loss Sensitivity
+----------------
 .. autofunction:: loss_sensitivity
+
+Empirical Robustness
+--------------------
 .. autofunction:: empirical_robustness
+
+Distance to nearest neighbors
+-----------------------------
 .. autofunction:: nearest_neighbour_dist
+
+CLEVER
+------
 .. autofunction:: clever_u
 .. autofunction:: clever_t

docs/modules/utils.rst

@@ -1,8 +1,15 @@
 :mod:`src.utils`
 ================
 .. automodule:: src.utils
-Functions
----------
+
+Load Dataset
+------------
+.. autofunction:: load_dataset
+
+Random Targets
+--------------
 .. autofunction:: random_targets
+
+Preprocess
+----------
 .. autofunction:: preprocess
-.. autofunction:: load_dataset

src/classifiers/__init__.py

@@ -2,7 +2,3 @@
 Classifier API for applying all attacks. Use the :class:`Classifier` wrapper to be able to apply an attack to a
 preexisting model.
 """
-from src.classifiers.classifier import Classifier
-from src.classifiers.cnn import CNN
-from src.classifiers.mlp import MLP
-from src.classifiers.resnet import ResNet