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 Single-Shot 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 one-of-many 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 $(C \times H \times W)$ . 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 pre-fetch 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 0-mean 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 $E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 + \left(1-y\right) \max \left(margin-d, 0\right)^2$ where $d = \left\| \left\| a_n - b_n \right\| \right\|_2$ . 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 non-linearity $y = \left\{ \begin{array}{lr} x \: \mbox{if} \; x > 0 \\ \alpha (\exp(x)-1) \: \mbox{if} \; x \le 0 \end{array} \right.$ . This layer is initialized with the MyCaffe.param.EluParameter. More...

class	EmbedLayer
	The EmbedLayer is a layer for learning 'embeddings' of one-hot vector input. This layer is initialized with the MyCaffe.param.EmbedParameter. More...

class	EuclideanLossLayer
	The EuclideanLossLayer computes the Euclidean (L2) loss $E = \frac{1}{2N} \sum\limits_{n=1}^N \left\| \left\| \hat{y}_n - y_n \right\| \right\|_2^2$ for real-valued 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, non-zero 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 one-of-many 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 'fully-connected' 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) ground-truth 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 long-short 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 Short-Term 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 long-short 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 non-linearity 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 one-of-many classification task, directly taking a predicted probability distribution as input. More...

class	MVNLayer
	The "Mean-Variance Normalization" MVNLayer normalizes the input to have 0-mean 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 equally-sized 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" non-linearity. This layer is initialized with the MyCaffe.param.PReLUParameter. More...

class	QuantileLossLayer
	The QuantileLossLayer computes the quantile loss $E = \frac{1}{2N} \sum\limits_{n=1}^N \left\| \left\| \hat{y}_n - y_n \right\| \right\|_2^2$ for real-valued 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 non-linearity, 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 arbitrary-sized output Blob. This layer is initialized with the MyCaffe.param.ReshapeParameter. More...

class	RNNLayer
	The RNNLayer processes time-varying 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 cross-entropy (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 cross-entropy (logisitic) loss and is often used for predicting targets interpreted as probabilities. More...

class	SoftmaxCrossEntropyLossLayer
	The SoftmaxCrossEntropyLossLayer computes the cross-entropy (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 one-of-many classification task, passing real-valued 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 auto-encoders. 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...

Detailed Description

The MyCaffe.layers namespace contains all layers that have a solidified code base, including the Layer class.

Namespaces

Classes

Detailed Description