This is the FAIRnets Ontology. Designed by the AIFB (http://www.aifb.kit.edu). It enables to formalize Artificial Neural Networks in RDF, making it possible to represent existing Neural Networks according the FAIR principles.
Optimization function used by Neural Network (e.g. SGD, Adam or RMSprop).
Describes the used dataset for training and evaluating
Number of downloads for this particular model.
Specifies the sequence of the layers. Starts at 1, the number of layers is n-1, at n: Number of layers.
Reflects potential real-world impact of the Model. Determines the Accuracy, Precision etc. of the model.
Specifies the sequence of models, starts at 1
Link to the respository (e.g. github) where the model and further information can be found.
Primary intended use and users (domain) for which the Neural Network was trained for.
Framework (e.g. Keras, Pandas, numpy, etc.) used to create this model.
Applies an activation function to an output.
Layer that applies an update to the cost function based input activity.
Average pooling operation for spatial data.
Average pooling operation for 3D data (spatial or spatio-temporal).
Batch normalization layer (Ioffe and Szegedy, 2014). Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.
Loss function typically used for classification problems.
1D convolution layer (e.g. temporal convolution). This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs.
2D convolution layer (e.g. spatial convolution over images). This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.
Transposed convolution layer (sometimes called Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.
3D convolution layer (e.g. spatial convolution over volumes). This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.
Convolutional LSTM. It is similar to an LSTM layer, but the input transformations and recurrent transformations are both convolutional.
Cropping layer for 1D input (e.g. temporal sequence). It crops along the time dimension (axis 1).
Cropping layer for 2D input (e.g. picture). It crops along spatial dimensions, i.e. height and width.
Cropping layer for 3D data (e.g. spatial or spatio-temporal).
Fast GRU implementation backed by CuDNN. Can only be run on GPU, with the TensorFlow backend.
Fast LSTM implementation with CuDNN. Can only be run on GPU, with the TensorFlow backend.
Just your regular densely-connected NN layer.
Depthwise separable 2D convolution. Depthwise Separable convolutions consists in performing just the first step in a depthwise spatial convolution (which acts on each input channel separately).
Turns positive integers (indexes) into dense vectors of fixed size.
Flattens the input. Does not affect the batch size.
Gated Recurrent Unit - Cho et al. 2014. There are two variants. The default one is based on 1406.1078v3 and has reset gate applied to hidden state before matrix multiplication. The other one is based on original 1406.1078v1 and has the order reversed.
Global average pooling operation for temporal data.
Global average pooling operation for spatial data.
Global Average pooling operation for 3D data.
Global max pooling operation for temporal data.
Global max pooling operation for spatial data.
Global Max pooling operation for 3D data.
Input() is used to instantiate a Keras tensor. A Keras tensor is a tensor object from the underlying backend (Theano, TensorFlow or CNTK), which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model.
Long Short-Term Memory layer - Hochreiter 1997.
Wraps arbitrary expression as a Layer object.
Masks a sequence by using a mask value to skip timesteps.
Max pooling operation for temporal data.
Max pooling operation for 3D data (spatial or spatio-temporal).
Permutes the dimensions of the input according to a given pattern. Useful for e.g. connecting RNNs and convnets together.
Loss function typically used for regression problems.
Depthwise separable 1D convolution. Separable convolutions consist in first performing a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes together the resulting output channels. Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.
Depthwise separable 2D convolution. Separable convolutions consist in first performing a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes together the resulting output channels. Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.
Fully-connected RNN where the output is to be fed back to input.
Spatial 1D version of Dropout. This version performs the same function as Dropout, however it drops entire 1D feature maps instead of individual elements. If adjacent frames within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout1D will help promote independence between feature maps and should be used instead.
Spatial 2D version of Dropout. This version performs the same function as Dropout, however it drops entire 2D feature maps instead of individual elements. If adjacent pixels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout2D will help promote independence between feature maps and should be used instead.
Spatial 3D version of Dropout. This version performs the same function as Dropout, however it drops entire 3D feature maps instead of individual elements. If adjacent pixels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout3D will help promote independence between feature maps and should be used instead.
Upsampling layer for 2D inputs. Repeats the rows and columns of the data by size[0] and size[1] respectively.
Upsampling layer for 3D inputs. Repeats the 1st, 2nd and 3rd dimensions of the data by size[0], size[1] and size[2] respectively.
Zero-padding layer for 1D input (e.g. temporal sequence).
Zero-padding layer for 2D input (e.g. picture). This layer can add rows and columns of zeros at the top, bottom, left and right side of an image tensor.
Zero-padding layer for 3D data (spatial or spatio-temporal).
Adadelta is a more robust extension of Adagrad that adapts learning rates based on a moving window of gradient updates, instead of accumulating all past gradients. This way, Adadelta continues learning even when many updates have been done. Compared to Adagrad, in the original version of Adadelta you don't have to set an initial learning rate. In this version, initial learning rate and decay factor can be set, as in most other Keras optimizers.
Adagrad is an optimizer with parameter-specific learning rates, which are adapted relative to how frequently a parameter gets updated during training. The more updates a parameter receives, the smaller the learning rate.
Much like Adam is essentially RMSprop with momentum, Nadam is Adam RMSprop with Nesterov momentum.
This optimizer is usually a good choice for recurrent neural networks.
Includes support for momentum, learning rate decay, and Nesterov momentum.