基于Keras实现的音频分类系统

# Keras Visualization Toolkit [](https://travis-ci.org/raghakot/keras-vis) [](https://github.com/raghakot/keras-vis/blob/master/LICENSE) [](https://keras-vis.herokuapp.com/) keras-vis is a high-level toolkit for visualizing and debugging your trained keras neural net models. Currently supported visualizations include: - Activation maximization - Saliency maps - Class activation maps All visualizations by default support N-dimensional image inputs. i.e., it generalizes to N-dim image inputs to your model. The toolkit generalizes all of the above as energy minimization problems with a clean, easy to use, and extendable interface. Compatible with both theano and tensorflow backends with 'channels_first', 'channels_last' data format. ## Quick links * Read the documentation at [https://raghakot.github.io/keras-vis](https://raghakot.github.io/keras-vis). * The Japanese edition is [https://keisen.github.io/keras-vis-docs-ja](https://keisen.github.io/keras-vis-docs-ja). * Join the slack [channel](https://keras-vis.herokuapp.com/) for questions/discussions. * We are tracking new features/tasks in [waffle.io](https://waffle.io/raghakot/keras-vis). Would love it if you lend us a hand and submit PRs. ## Getting Started In image backprop problems, the goal is to generate an input image that minimizes some loss function. Setting up an image backprop problem is easy. **Define weighted loss function** Various useful loss functions are defined in [losses](https://raghakot.github.io/keras-vis/vis.losses). A custom loss function can be defined by implementing [Loss.build_loss](https://raghakot.github.io/keras-vis/vis.losses/#lossbuild_loss). ```python from vis.losses import ActivationMaximization from vis.regularizers import TotalVariation, LPNorm filter_indices = [1, 2, 3] # Tuple consists of (loss_function, weight) # Add regularizers as needed. losses = [ (ActivationMaximization(keras_layer, filter_indices), 1), (LPNorm(model.input), 10), (TotalVariation(model.input), 10) ] ``` **Configure optimizer to minimize weighted loss** In order to generate natural looking images, image search space is constrained using regularization penalties. Some common regularizers are defined in [regularizers](https://raghakot.github.io/keras-vis/vis.regularizers). Like loss functions, custom regularizer can be defined by implementing [Loss.build_loss](https://raghakot.github.io/keras-vis/vis.losses/#lossbuild_loss). ```python from vis.optimizer import Optimizer optimizer = Optimizer(model.input, losses) opt_img, grads, _ = optimizer.minimize() ``` Concrete examples of various supported visualizations can be found in [examples folder](https://github.com/raghakot/keras-vis/tree/master/examples). ## Installation 1) Install [keras](https://github.com/fchollet/keras/blob/master/README.md#installation) with theano or tensorflow backend. Note that this library requires Keras > 2.0 2) Install keras-vis > From sources ```bash sudo python setup.py install ``` > PyPI package ```bash sudo pip install keras-vis ``` ## Visualizations **NOTE: The links are currently broken and the entire documentation is being reworked. Please see examples/ for samples.** Neural nets are black boxes. In the recent years, several approaches for understanding and visualizing Convolutional Networks have been developed in the literature. They give us a way to peer into the black boxes, diagnose mis-classifications, and assess whether the network is over/under fitting. Guided backprop can also be used to create [trippy art](https://deepdreamgenerator.com/gallery), neural/texture [style transfer](https://github.com/jcjohnson/neural-style) among the list of other growing applications. Various visualizations, documented in their own pages, are summarized here. <hr/> ### [Conv filter visualization](https://raghakot.github.io/keras-vis/visualizations/conv_filters) <img src="https://raw.githubusercontent.com/raghakot/keras-vis/master/images/conv_vis/cover.jpg?raw=true"/> *Convolutional filters learn 'template matching' filters that maximize the output when a similar template pattern is found in the input image. Visualize those templates via Activation Maximization.* <hr/> ### [Dense layer visualization](https://raghakot.github.io/keras-vis/visualizations/dense) <img src="https://raw.githubusercontent.com/raghakot/keras-vis/master/images/dense_vis/cover.png?raw=true"/> *How can we assess whether a network is over/under fitting or generalizing well?* <hr/> ### [Attention Maps](https://raghakot.github.io/keras-vis/visualizations/attention) <img src="https://raw.githubusercontent.com/raghakot/keras-vis/master/images/attention_vis/cover.png?raw=true"/> *How can we assess whether a network is attending to correct parts of the image in order to generate a decision?* <hr/> ### Generating animated gif of optimization progress It is possible to generate an animated gif of optimization progress by leveraging [callbacks](https://raghakot.github.io/keras-vis/vis.callbacks). Following example shows how to visualize the activation maximization for 'ouzel' class (output_index: 20). ```python from vis.losses import ActivationMaximization from vis.regularizers import TotalVariation, LPNorm from vis.modifiers import Jitter from vis.optimizer import Optimizer from vis.callbacks import GifGenerator from vis.utils.vggnet import VGG16 # Build the VGG16 network with ImageNet weights model = VGG16(weights='imagenet', include_top=True) print('Model loaded.') # The name of the layer we want to visualize # (see model definition in vggnet.py) layer_name = 'predictions' layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]]) output_class = [20] losses = [ (ActivationMaximization(layer_dict[layer_name], output_class), 2), (LPNorm(model.input), 10), (TotalVariation(model.input), 10) ] opt = Optimizer(model.input, losses) opt.minimize(max_iter=500, verbose=True, image_modifiers=[Jitter()], callbacks=[GifGenerator('opt_progress')]) ``` Notice how the output jitters around? This is because we used [Jitter](https://raghakot.github.io/keras-vis/vis.modifiers/#jitter), a kind of [ImageModifier](https://raghakot.github.io/keras-vis/vis.modifiers/#imagemodifier) that is known to produce crisper activation maximization images. As an exercise, try: - Without Jitter - Varying various loss weights  <hr/> ## Citation Please cite keras-vis in your publications if it helped your research. Here is an example BibTeX entry: ``` @misc{raghakotkerasvis, title={keras-vis}, author={Kotikalapudi, Raghavendra and contributors}, year={2017}, publisher={GitHub}, howpublished={\url{https://github.com/raghakot/keras-vis}}, } ```