MyCaffe
1.12.2.41
Deep learning software for Windows C# programmers.

The MyCaffe.layers namespace contains all layers that have a solidified code base, including the Layer class. More...
Namespaces  
namespace  alpha 
The MyCaffe.layers.alpha namespace contains all experimental layers that have a fluid and changing code base.  
namespace  beta 
The MyCaffe.layers.beta namespace contains all beta stage layers.  
namespace  gpt 
The MyCaffe.layers.gpt namespace contains all GPT related layers.  
namespace  hdf5 
The MyCaffe.layers.hdf5 namespace contains all HDF5 related layers.  
namespace  lnn 
The MyCaffe.layers.lnn namespace contains all Liquid Neural Network (LNN) related layers.  
namespace  nt 
The MyCaffe.layers.nt namespace contains all Neural Transfer related layers.  
namespace  ssd 
The MyCaffe.layers.ssd namespace contains all SingleShot MultiBox (SSD) related layers.  
namespace  tft 
The MyCaffe.layers.tft namespace contains all TFT related layers.  
Classes  
class  AbsValLayer 
The AbsValLayer computes the absolute value of the input. More...  
class  AccuracyLayer 
The AccuracyLayer computes the classification accuracy for a oneofmany classification task. This layer is initialized with the MyCaffe.param.AccuracyParameter. More...  
class  ArgMaxLayer 
The ArgMaxLayer computes the index of the K max values for each datum across all dimensions . This layer is initialized with the MyCaffe.param.ArgMaxParameter. More...  
class  AttentionLayer 
[DEPRECIATED] The AttentionLayer provides focus for LSTM based encoder/decoder models. More...  
class  BaseConvolutionLayer 
The BaseConvolutionLayer is an abstract base class that factors out BLAS code common to ConvolutionLayer and DeconvolutionLayer More...  
class  BaseDataLayer 
The BaseDataLayer is the base class for data Layers that feed Blobs of data into the Net. More...  
class  BasePrefetchingDataLayer 
The BasePrefetchingDataLayer is the base class for data Layers that prefetch data before feeding the Blobs of data into the Net. More...  
class  Batch 
The Batch contains both the data and label Blobs of the batch. More...  
class  BatchNormLayer 
The BatchNormLayer normalizes the input to have 0mean and/or unit (1) variance across the batch. This layer is initialized with the BatchNormParameter. More...  
class  BatchReindexLayer 
The BatchReindexLayer provides an index into the input blob along its first axis. More...  
class  BiasLayer 
The BiasLayer computes a sum of two input Blobs, with the shape of the latter Blob 'broadcast' to match the shape of the former. Equivalent to tiling the latter Blob, then computing the elementwise sum. This layer is initialized with the MyCaffe.param.BiasParameter. More...  
class  BNLLLayer 
The Binomial Normal Log Liklihod Layer. More...  
class  ClipLayer 
The ClipLayer provides a neuron layer that clips the data to fit within the [min,max] range. This layer is initialized with the MyCaffe.param.ClipParameter. More...  
class  ConcatLayer 
The ConcatLayer takes at least two Blobs and concatentates them along either the num or channel dimension, outputing the result. This layer is initialized with the MyCaffe.param.ConcatParameter. More...  
class  ConstantLayer 
The ConstantLayer provides a layer that just outputs a constant value. This layer is initialized with the MyCaffe.param.ConstantParameter. More...  
class  ContrastiveLossLayer 
The ContrastiveLossLayer computes the contrastive loss where . This layer is initialized with the MyCaffe.param.ContrastiveLossParameter. More...  
class  ConvolutionLayer 
The ConvolutionLayer convolves the input image with a bank of learned filters, and (optionally) adds biases. This layer is initialized with the MyCaffe.param.ConvolutionParameter. More...  
class  CopyLayer 
The CopyLayer copies the src bottom to the dst bottom. The layer has no output. More...  
class  CropLayer 
The CropLayer takes a Blob and crops it to the shape specified by the second input Blob, across all dimensions after the specified axis. More...  
class  DataLayer 
The DataLayer loads data from the IXImageDatabase database. This layer is initialized with the MyCaffe.param.DataParameter. More...  
class  DataNormalizerLayer 
The DataNormalizerLayer normalizes the input data (and optionally label) based on the normalization operations specified in the layer parameter. More...  
class  DebugLayer 
The DebugLayer merely stores, up to max_stored_batches, batches of input which are then optionally used by various debug visualizers. This layer is initialized with the MyCaffe.param.DebugParameter. More...  
class  DeconvolutionLayer 
The DeconvolutionLayer convolves the input with a bank of learned filtered, and (optionally) add biases, treating filters and convolution parameters in the opposite sense as ConvolutionLayer. This layer is initialized with the MyCaffe.param.ConvolutionParameter. More...  
class  DropoutLayer 
During training only, sets a random portion of to 0, adjusting the rest of the vector magnitude accordingly This layer is initialized with the MyCaffe.param.DropoutParameter. More...  
class  DummyDataLayer 
The DummyDataLayer provides data to the Net generated by a Filler. This layer is initialized with the MyCaffe.param.DummyDataParameter. More...  
class  EltwiseLayer 
The EltwiseLayer computes elementwise oeprations, such as product and sum, along multiple input blobs. This layer is initialized with the MyCaffe.param.EltwiseParameter. More...  
class  ELULayer 
The ELULayer computes exponential linear unit nonlinearity . This layer is initialized with the MyCaffe.param.EluParameter. More...  
class  EmbedLayer 
The EmbedLayer is a layer for learning 'embeddings' of onehot vector input. This layer is initialized with the MyCaffe.param.EmbedParameter. More...  
class  EuclideanLossLayer 
The EuclideanLossLayer computes the Euclidean (L2) loss for realvalued regression tasks. More...  
class  ExpLayer 
The ExpLayer which computes the exponential of the input. This layer is initialized with the MyCaffe.param.ExpParameter. More...  
class  FilterLayer 
The FilterLayer takes two+ Blobs, interprets last Blob as a selector and filters remaining Blobs accordingly with selector data (0 means that the corresponding item has to be filtered, nonzero means that corresponding item needs to stay). More...  
class  FlattenLayer 
The FlattenLayer reshapes the input Blob into flat vectors This layer is initialized with the MyCaffe.param.FlattenParameter. More...  
class  GradientScaleLayer 
The GradientScaleLayer which scales the deltas during the backpropagation. This layer is initialized with the MyCaffe.param.GradientScaleParameter. More...  
class  HingeLossLayer 
The HingeLossLayer computes the hinge loss for a oneofmany classification task. This layer is initialized with the MyCaffe.param.HingeLossParameter. More...  
class  Im2colLayer 
The Im2ColLayer is a helper layer for image operations that rearranges image regions into column vectors. More...  
class  ImageDataLayer 
The ImageDataLayer loads data from the image files located in the root directory specified. This layer is initialized with the MyCaffe.param.ImageDataParameter. More...  
class  InfogainLossLayer 
The InforgainLossLayer is a generalization of SoftmaxWithLossLayer that takes an 'information gain' (infogain) matrix specifying the 'value of all label pairs. This layer is initialized with the MyCaffe.param.InfogainLossParameter. More...  
class  InnerProductLayer 
The InnerProductLayer, also know as a 'fullyconnected' layer, computes the inner product with a set of learned weights, and (optionally) adds biases. This layer is initialized with the MyCaffe.param.InnerProductParameter. More...  
class  InputLayer 
The InputLayer provides data to the Net by assigning top Blobs directly. This layer is initialized with the MyCaffe.param.InputParameter. More...  
class  LabelMappingLayer 
/b DEPRECIATED (use DataLayer DataLabelMappingParameter instead) The LabelMappingLayer converts original labels to new labels specified by the label mapping. This layer is initialized with the MyCaffe.param.LabelMappingParameter. More...  
class  LastBatchLoadedArgs 
Specifies the arguments sent to the OnBatchLoad event used when synchronizing between Data Layers. More...  
class  Layer 
An interface for the units of computation which can be composed into a Net. More...  
class  LayerParameterEx 
The LayerParameterEx class is used when sharing another Net to conserve GPU memory and extends the LayerParameter with shared Blobs for this purpose. More...  
class  LogLayer 
The LogLayer computes the log of the input. This layer is initialized with the MyCaffe.param.LogParameter. More...  
class  LossLayer 
The LossLayer provides an interface for Layer's that take two blobs as input – usually (1) predictions and (2) groundtruth labels – and output a singleton blob representing the loss. This layer is initialized with the MyCaffe.param.LossParameter. More...  
class  LRNLayer 
The "Local Response Normalization" LRNLayer is used to normalize the input in a local region across or within feature maps. This layer is initialized with the MyCaffe.param.LRNParameter. More...  
class  LSTMAttentionLayer 
The LSTMAttentionLayer adds attention to the longshort term memory layer and is used in encoder/decoder models. To use attention, just set 'enable_attention'=true. When disabled, this layer operates like a standard LSTM layer where inputs are in the shape T,B,I with T=timesteps, B=batch and I=input. More...  
class  LSTMLayer 
The LSTMLayer processes sequential inputs using a 'Long ShortTerm Memory' (LSTM) [1] style recurrent neural network (RNN). Implemented by unrolling the LSTM computation through time. This layer is initialized with the MyCaffe.param.RecurrentParameter. More...  
class  LSTMSimpleLayer 
[DEPRECIATED  use LSTMAttentionLayer instead with enable_attention = false] The LSTMSimpleLayer is a simpe version of the longshort term memory layer. This layer is initialized with the MyCaffe.param.LSTMSimpleParameter. More...  
class  LSTMUnitLayer 
The LSTMUnitLayer is a helper for LSTMLayer that computes a single timestep of the nonlinearity of the LSTM, producing the updated cell and hidden states. More...  
class  MathLayer 
The MathLayer which computes various mathematical functions of the input. This layer is initialized with the MyCaffe.param.MathParameter. More...  
class  MemoryDataLayer 
The MemoryDataLayer provides data to the Net from memory. This layer is initialized with the MyCaffe.param.MemoryDataParameter. More...  
class  MemoryDataLayerGetDataArgs 
The MemoryDataLayerGetDataArgs class is passed to the OnGetData event. More...  
class  MemoryDataLayerPackDataArgs 
The MemoryDataLayerPackDataArgs is passed to the OnDataPack event which fires each time the data received in AddDatumVector needs to be packed into a specific ordering as is the case when using an LSTM network. More...  
class  MemoryLossLayer 
The MemoryLossLayer provides a method of performing a custom loss functionality. Similar to the MemoryDataLayer, the MemoryLossLayer supports an event used to get the loss value. This event is called OnGetLoss, which once retrieved is used for learning on the backward pass. More...  
class  MemoryLossLayerGetLossArgs 
The MemoryLossLayerGetLossArgs class is passed to the OnGetLoss event. More...  
class  MultinomialLogisticLossLayer 
The MultinomialLogicistLossLayer computes the multinomial logistc loss for a oneofmany classification task, directly taking a predicted probability distribution as input. More...  
class  MVNLayer 
The "MeanVariance Normalization" MVNLayer normalizes the input to have 0mean and/or unit (1) variance. This layer is initialized with the MyCaffe.param.MVNParameter. More...  
class  NeuronLayer 
The NeuronLayer is an interface for layers that take one blob as input (x) and produce only equallysized blob as output (y), where each element of the output depends only on the corresponding input element. More...  
class  ParameterLayer 
The ParameterLayer passes its blob[0] data and diff to the top[0]. More...  
class  PoolingLayer 
The PoolingLayer pools the input image by taking the max, average, etc. within regions. This layer is initialized with the MyCaffe.param.PoolingParameter. More...  
class  PowerLayer 
The PowerLayer computes the power of the input. This layer is initialized with the MyCaffe.param.PowerParameter. More...  
class  PReLULayer 
The PReLULayer computes the "Parameterized Rectified Linear Unit" nonlinearity. This layer is initialized with the MyCaffe.param.PReLUParameter. More...  
class  QuantileLossLayer 
The QuantileLossLayer computes the quantile loss for realvalued regression tasks. More...  
class  RecurrentLayer 
The RecurrentLayer is an abstract class for implementing recurrent behavior inside of an unrolled newtork. This layer type cannot be instantiated – instead, you should use one of teh implementations which defines the recurrent architecture, such as RNNLayer or LSTMLayer. This layer is initialized with the MyCaffe.param.RecurrentParameter. More...  
class  ReductionLayer 
The ReductionLayer computes the 'reductions' – operations that return a scalar output Blob for an input Blob of arbitrary size, such as the sum, absolute sum, and sum of squares. This layer is initialized with the MyCaffe.param.ReductionParameter. More...  
class  ReLULayer 
The ReLULayer computes the "Rectifier Linear Unit" ReLULayer nonlinearity, a classic for neural networks. This layer is initialized with the MyCaffe.param.ReLUParameter. More...  
class  ReshapeLayer 
The ReshapeLayer reshapes the input Blob into an arbitrarysized output Blob. This layer is initialized with the MyCaffe.param.ReshapeParameter. More...  
class  RNNLayer 
The RNNLayer processes timevarying inputs using a simple recurrent neural network (RNN). Implemented as a network unrolling the RNN computation in time. This layer is initialized with the MyCaffe.param.RecurrentParameter. More...  
class  ScaleLayer 
The ScaleLayer computes the elementwise product of two input Blobs, with the shape of the latter Blob 'broadcast' to match the shape of the former. Equivalent to tiling the later Blob, then computing the elementwise product. Note: for efficiency and convienience this layer can additionally perform a 'broadcast' sum too when 'bias_term: true' This layer is initialized with the MyCaffe.param.ScaleParameter. is set. More...  
class  SigmoidCrossEntropyLossLayer 
The SigmoidCrossEntropyLayer computes the crossentropy (logisitic) loss and is often used for predicting targets interpreted as probabilities. More...  
class  SigmoidLayer 
The SigmoidLayer is a neuron layer that calculates the sigmoid function, a classc choice for neural networks. This layer is initialized with the MyCaffe.param.SigmoidParameter. More...  
class  SilenceLayer 
The SilenceLayer ignores bottom blobs while producing no top blobs. (This is useuful to suppress output during testing.) More...  
class  SliceLayer 
The SliceLayer takes a blob and slices it along either the num or channel dimensions outputting multiple sliced blob results. This layer is initialized with the MyCaffe.param.SliceParameter. More...  
class  SoftmaxCrossEntropy2LossLayer 
The SoftmaxCrossEntropy2Layer computes the crossentropy (logisitic) loss and is often used for predicting targets interpreted as probabilities. More...  
class  SoftmaxCrossEntropyLossLayer 
The SoftmaxCrossEntropyLossLayer computes the crossentropy (logisitic) loss and is often used for predicting targets interpreted as probabilities in reinforcement learning. More...  
class  SoftmaxLayer 
The SoftmaxLayer computes the softmax function. This layer is initialized with the MyCaffe.param.SoftmaxParameter. More...  
class  SoftmaxLossLayer 
Computes the multinomial logistic loss for a oneofmany classification task, passing realvalued predictions through a softmax to get a probability distribution over classes. More...  
class  SplitLayer 
The SplitLayer creates a 'split' path in the network by copying the bottom blob into multiple top blob's to be used by multiple consuming layers. More...  
class  SPPLayer 
The SPPLayer does spatial pyramid pooling on the input image by taking the max, average, etc. within regions so that the result vector of different sized images are of the same size. This layer is initialized with the MyCaffe.param.SPPParameter. More...  
class  SwishLayer 
The SwishLayer provides a novel activation function that tends to work better than ReLU. This layer is initialized with the MyCaffe.param.SwishParameter. More...  
class  TanhLayer 
The TanhLayer is a neuron layer that calculates the tanh function, popular with autoencoders. This layer is initialized with the MyCaffe.param.TanhParameter. More...  
class  ThresholdLayer 
The ThresholdLayer is a neuron layer that tests whether the input exceeds a threshold: outputs 1 for inputs above threshold; 0 otherwise. This layer is initialized with the MyCaffe.param.ThresholdParameter. More...  
class  TileLayer 
The TileLayer copies a Blob along specified dimensions. This layer is initialized with the MyCaffe.param.TileParameter. More...  
The MyCaffe.layers namespace contains all layers that have a solidified code base, including the Layer class.