Caffe layers
Caffe layer 按首字母排序
title: Absolute Value Layer
Absolute Value Layer
-
Layer type:
AbsVal -
Doxygen Documentation
-
Header:
./include/caffe/layers/absval_layer.hpp -
CPU implementation:
./src/caffe/layers/absval_layer.cpp -
CUDA GPU implementation:
./src/caffe/layers/absval_layer.cu -
Sample
layer { name: "layer" bottom: "in" top: "out" type: "AbsVal" }
The AbsVal layer computes the output as abs(x) for each input element x.
title: Accuracy and Top-k
Accuracy and Top-k
Accuracy scores the output as the accuracy of output with respect to target – it is not actually a loss and has no backward step.
- Layer type:
Accuracy - Doxygen Documentation
- Header:
./include/caffe/layers/accuracy_layer.hpp - CPU implementation:
./src/caffe/layers/accuracy_layer.cpp
Parameters
- Parameters (
AccuracyParameter accuracy_param) - From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/AccuracyParameter.txt %}
{% endhighlight %}
title: ArgMax Layer
ArgMax Layer
- Layer type:
ArgMax - Doxygen Documentation
- Header:
./include/caffe/layers/argmax_layer.hpp - CPU implementation:
./src/caffe/layers/argmax_layer.cpp
Parameters
- Parameters (
ArgMaxParameter argmax_param) - From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/ArgMaxParameter.txt %}
{% endhighlight %}
title: Batch Norm Layer
Batch Norm Layer
- Layer type:
BatchNorm - Doxygen Documentation
- Header:
./include/caffe/layers/batch_norm_layer.hpp - CPU implementation:
./src/caffe/layers/batch_norm_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/batch_norm_layer.cu
Parameters
- Parameters (
BatchNormParameter batch_norm_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/BatchNormParameter.txt %}
{% endhighlight %}
title: Batch Reindex Layer
Batch Reindex Layer
- Layer type:
BatchReindex - Doxygen Documentation
- Header:
./include/caffe/layers/batch_reindex_layer.hpp - CPU implementation:
./src/caffe/layers/batch_reindex_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/batch_reindex_layer.cu
Parameters
No parameters.
title: Bias Layer
Bias Layer
- Layer type:
Bias - Doxygen Documentation
- Header:
./include/caffe/layers/bias_layer.hpp - CPU implementation:
./src/caffe/layers/bias_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/bias_layer.cu
Parameters
- Parameters (
BiasParameter bias_param) - From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/BiasParameter.txt %}
{% endhighlight %}
title: BNLL Layer
BNLL Layer
- Layer type:
BNLL - Doxygen Documentation
- Header:
./include/caffe/layers/bnll_layer.hpp - CPU implementation:
./src/caffe/layers/bnll_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/bnll_layer.cu
The BNLL (binomial normal log likelihood) layer computes the output as log(1 + exp(x)) for each input element x.
Parameters
No parameters.
Sample
layer {
name: "layer"
bottom: "in"
top: "out"
type: BNLL
}
title: Clip Layer
Clip Layer
- Layer type:
Clip - Doxygen Documentation
- Header:
./include/caffe/layers/clip_layer.hpp - CPU implementation:
./src/caffe/layers/clip_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/clip_layer.cu
Parameters
- Parameters (
ClipParameter clip_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/ClipParameter.txt %}
{% endhighlight %}
title: Concat Layer
Concat Layer
-
Layer type:
Concat -
Doxygen Documentation
-
Header:
./include/caffe/layers/concat_layer.hpp -
CPU implementation:
./src/caffe/layers/concat_layer.cpp -
CUDA GPU implementation:
./src/caffe/layers/concat_layer.cu -
Input
n_i * c_i * h * wfor each input blob i from 1 to K.
-
Output
- if
axis = 0:(n_1 + n_2 + ... + n_K) * c_1 * h * w, and all inputc_ishould be the same. - if
axis = 1:n_1 * (c_1 + c_2 + ... + c_K) * h * w, and all inputn_ishould be the same.
- if
-
Sample
layer { name: "concat" bottom: "in1" bottom: "in2" top: "out" type: "Concat" concat_param { axis: 1 } }
The Concat layer is a utility layer that concatenates its multiple input blobs to one single output blob.
Parameters
- Parameters (
ConcatParameter concat_param)- Optional
axis[default 1]: 0 for concatenation along num and 1 for channels.
- Optional
- From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/ConcatParameter.txt %}
{% endhighlight %}
title: Contrastive Loss Layer
Contrastive Loss Layer
- Layer type:
ContrastiveLoss - Doxygen Documentation
- Header:
./include/caffe/layers/contrastive_loss_layer.hpp - CPU implementation:
./src/caffe/layers/contrastive_loss_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/contrastive_loss_layer.cu
Parameters
- Parameters (
ContrastiveLossParameter contrastive_loss_param) - From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/ContrastiveLossParameter.txt %}
{% endhighlight %}
title: Convolution Layer
Convolution Layer
- Layer type:
Convolution - Doxygen Documentation
- Header:
./include/caffe/layers/conv_layer.hpp - CPU implementation:
./src/caffe/layers/conv_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/conv_layer.cu - Input
n * c_i * h_i * w_i
- Output
n * c_o * h_o * w_o, whereh_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1andw_olikewise.
The Convolution layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
Sample
Sample (as seen in ./models/bvlc_reference_caffenet/train_val.prototxt):
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
# learning rate and decay multipliers for the filters
param { lr_mult: 1 decay_mult: 1 }
# learning rate and decay multipliers for the biases
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 96 # learn 96 filters
kernel_size: 11 # each filter is 11x11
stride: 4 # step 4 pixels between each filter application
weight_filler {
type: "gaussian" # initialize the filters from a Gaussian
std: 0.01 # distribution with stdev 0.01 (default mean: 0)
}
bias_filler {
type: "constant" # initialize the biases to zero (0)
value: 0
}
}
}
Parameters
- Parameters (
ConvolutionParameter convolution_param)- Required
num_output(c_o): the number of filterskernel_size(orkernel_handkernel_w): specifies height and width of each filter
- Strongly Recommended
weight_filler[defaulttype: 'constant' value: 0]
- Optional
bias_term[defaulttrue]: specifies whether to learn and apply a set of additive biases to the filter outputspad(orpad_handpad_w) [default 0]: specifies the number of pixels to (implicitly) add to each side of the inputstride(orstride_handstride_w) [default 1]: specifies the intervals at which to apply the filters to the inputgroup(g) [default 1]: If g > 1, we restrict the connectivity of each filter to a subset of the input. Specifically, the input and output channels are separated into g groups, and the i i ith output group channels will be only connected to the i i ith input group channels.
- Required
- From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/ConvolutionParameter.txt %}
{% endhighlight %}
title: Crop Layer
Crop Layer
- Layer type:
Crop - Doxygen Documentation
- Header:
./include/caffe/layers/crop_layer.hpp - CPU implementation:
./src/caffe/layers/crop_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/crop_layer.cu
Parameters
- Parameters (
CropParameter crop_param) - From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/CropParameter.txt %}
{% endhighlight %}
title: Database Layer
Database Layer
- Layer type:
Data - Doxygen Documentation
- Header:
./include/caffe/layers/data_layer.hpp - CPU implementation:
./src/caffe/layers/data_layer.cpp
Parameters
- Parameters (
DataParameter data_param) - From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/DataParameter.txt %}
{% endhighlight %}
- Parameters
- Required
source: the name of the directory containing the databasebatch_size: the number of inputs to process at one time
- Optional
rand_skip: skip up to this number of inputs at the beginning; useful for asynchronous sgdbackend[defaultLEVELDB]: choose whether to use aLEVELDBorLMDB
- Required
title: Deconvolution Layer
Deconvolution Layer
- Layer type:
Deconvolution - Doxygen Documentation
- Header:
./include/caffe/layers/deconv_layer.hpp - CPU implementation:
./src/caffe/layers/deconv_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/deconv_layer.cu
Parameters
Uses the same parameters as the Convolution layer.
- Parameters (
ConvolutionParameter convolution_param) - From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/ConvolutionParameter.txt %}
{% endhighlight %}
title: Dropout Layer
Dropout Layer
- Layer type:
Dropout - Doxygen Documentation
- Header:
./include/caffe/layers/dropout_layer.hpp - CPU implementation:
./src/caffe/layers/dropout_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/dropout_layer.cu
Parameters
- Parameters (
DropoutParameter dropout_param) - From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/DropoutParameter.txt %}
{% endhighlight %}
title: Dummy Data Layer
Dummy Data Layer
- Layer type:
DummyData - Doxygen Documentation
- Header:
./include/caffe/layers/dummy_data_layer.hpp - CPU implementation:
./src/caffe/layers/dummy_data_layer.cpp
Parameters
- Parameters (
DummyDataParameter dummy_data_param) - From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/DummyDataParameter.txt %}
{% endhighlight %}
title: Eltwise Layer
Eltwise Layer
- Layer type:
Eltwise - Doxygen Documentation
- Header:
./include/caffe/layers/eltwise_layer.hpp - CPU implementation:
./src/caffe/layers/eltwise_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/eltwise_layer.cu
Parameters
- Parameters (
EltwiseParameter eltwise_param) - From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/EltwiseParameter.txt %}
{% endhighlight %}
title: ELU Layer
ELU Layer
- Layer type:
ELU - Doxygen Documentation
- Header:
./include/caffe/layers/elu_layer.hpp - CPU implementation:
./src/caffe/layers/elu_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/elu_layer.cu
References
- Clevert, Djork-Arne, Thomas Unterthiner, and Sepp Hochreiter.
“Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)” arXiv:1511.07289. (2015).
Parameters
- Parameters (
ELUParameter elu_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/ELUParameter.txt %}
{% endhighlight %}
title: Embed Layer
Embed Layer
- Layer type:
Embed - Doxygen Documentation
- Header:
./include/caffe/layers/embed_layer.hpp - CPU implementation:
./src/caffe/layers/embed_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/embed_layer.cu
Parameters
- Parameters (
EmbedParameter embed_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/EmbedParameter.txt %}
{% endhighlight %}
title: Euclidean Loss Layer
Sum-of-Squares / Euclidean Loss Layer
- Layer type:
EuclideanLoss - Doxygen Documentation
- Header:
./include/caffe/layers/euclidean_loss_layer.hpp - CPU implementation:
./src/caffe/layers/euclidean_loss_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/euclidean_loss_layer.cu
The Euclidean loss layer computes the sum of squares of differences of its two inputs, 1 2 N ∑ i = 1 N ∥ x i 1 − x i 2 ∥ 2 2 \frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2 2N1i=1∑N∥xi1−xi2∥22.
Parameters
Does not take any parameters.
title: Exponential Layer
Exponential Layer
- Layer type:
Exp - Doxygen Documentation
- Header:
./include/caffe/layers/exp_layer.hpp - CPU implementation:
./src/caffe/layers/exp_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/exp_layer.cu
Parameters
- Parameters (
Parameter exp_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/ExpParameter.txt %}
{% endhighlight %}
See also
- Power layer
title: Filter Layer
Filter Layer
- Layer type:
Filter - Doxygen Documentation
- Header:
./include/caffe/layers/filter_layer.hpp - CPU implementation:
./src/caffe/layers/filter_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/filter_layer.cu
Parameters
Does not take any parameters.
title: Flatten Layer
Flatten Layer
- Layer type:
Flatten - Doxygen Documentation
- Header:
./include/caffe/layers/flatten_layer.hpp - CPU implementation:
./src/caffe/layers/flatten_layer.cpp
The Flatten layer is a utility layer that flattens an input of shape n * c * h * w to a simple vector output of shape n * (c*h*w).
Parameters
- Parameters (
FlattenParameter flatten_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/FlattenParameter.txt %}
{% endhighlight %}
title: HDF5 Data Layer
HDF5 Data Layer
- Layer type:
HDF5Data - Doxygen Documentation
- Header:
./include/caffe/layers/hdf5_data_layer.hpp - CPU implementation:
./src/caffe/layers/hdf5_data_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/hdf5_data_layer.cu
Parameters
- Parameters (
HDF5DataParameter hdf5_data_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/HDF5DataParameter.txt %}
{% endhighlight %}
title: HDF5 Output Layer
HDF5 Output Layer
- Layer type:
HDF5Output - Doxygen Documentation
- Header:
./include/caffe/layers/hdf5_output_layer.hpp - CPU implementation:
./src/caffe/layers/hdf5_output_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/hdf5_output_layer.cu
The HDF5 output layer performs the opposite function of the other layers in this section: it writes its input blobs to disk.
Parameters
-
Parameters (
HDF5OutputParameter hdf5_output_param)- Required
file_name: name of file to write to
- Required
-
From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/HDF5OutputParameter.txt %}
{% endhighlight %}
title: Hinge Loss Layer
Hinge (L1, L2) Loss Layer
- Layer type:
HingeLoss - Doxygen Documentation
- Header:
./include/caffe/layers/hinge_loss_layer.hpp - CPU implementation:
./src/caffe/layers/hinge_loss_layer.cpp
Parameters
- Parameters (
HingeLossParameter hinge_loss_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/HingeLossParameter.txt %}
{% endhighlight %}
title: Im2col Layer
im2col
- File type:
Im2col - Header:
./include/caffe/layers/im2col_layer.hpp - CPU implementation:
./src/caffe/layers/im2col_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/im2col_layer.cu
Im2col is a helper for doing the image-to-column transformation that you most
likely do not need to know about. This is used in Caffe’s original convolution
to do matrix multiplication by laying out all patches into a matrix.
title: ImageData Layer
ImageData Layer
- Layer type:
ImageData - Doxygen Documentation
- Header:
./include/caffe/layers/image_data_layer.hpp - CPU implementation:
./src/caffe/layers/image_data_layer.cpp
Parameters
-
Parameters (
ImageDataParameter image_data_parameter)- Required
source: name of a text file, with each line giving an image filename and labelbatch_size: number of images to batch together
- Optional
rand_skipshuffle[default false]new_height,new_width: if provided, resize all images to this size
- Required
-
From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/ImageDataParameter.txt %}
{% endhighlight %}
title: Infogain Loss Layer
Infogain Loss Layer
- Layer type:
InfogainLoss - Doxygen Documentation
- Header:
./include/caffe/layers/infogain_loss_layer.hpp - CPU implementation:
./src/caffe/layers/infogain_loss_layer.cpp
A generalization of MultinomialLogisticLossLayer that takes an “information gain” (infogain) matrix specifying the “value” of all label pairs.
Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the identity.
Parameters
- Parameters (
Parameter infogain_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/InfogainLossParameter.txt %}
{% endhighlight %}
title: Inner Product / Fully Connected Layer
Inner Product / Fully Connected Layer
-
Layer type:
InnerProduct -
Doxygen Documentation
-
Header:
./include/caffe/layers/inner_product_layer.hpp -
CPU implementation:
./src/caffe/layers/inner_product_layer.cpp -
CUDA GPU implementation:
./src/caffe/layers/inner_product_layer.cu -
Input
n * c_i * h_i * w_i
-
Output
n * c_o * 1 * 1
-
Sample
layer { name: "fc8" type: "InnerProduct" # learning rate and decay multipliers for the weights param { lr_mult: 1 decay_mult: 1 } # learning rate and decay multipliers for the biases param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1000 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } bottom: "fc7" top: "fc8" }
The InnerProduct layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob’s height and width set to 1).
Parameters
- Parameters (
InnerProductParameter inner_product_param)- Required
num_output(c_o): the number of filters
- Strongly recommended
weight_filler[defaulttype: 'constant' value: 0]
- Optional
bias_filler[defaulttype: 'constant' value: 0]bias_term[defaulttrue]: specifies whether to learn and apply a set of additive biases to the filter outputs
- Required
- From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/InnerProductParameter.txt %}
{% endhighlight %}
title: Input Layer
Input Layer
- Layer type:
Input - Doxygen Documentation
- Header:
./include/caffe/layers/input_layer.hpp - CPU implementation:
./src/caffe/layers/input_layer.cpp
Parameters
- Parameters (
InputParameter input_param) - From
./src/caffe/proto/caffe.proto):
{% highlight Protobuf %}
{% include proto/InputParameter.txt %}
{% endhighlight %}
title: Log Layer
Log Layer
- Layer type:
Log - Doxygen Documentation
- Header:
./include/caffe/layers/log_layer.hpp - CPU implementation:
./src/caffe/layers/log_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/log_layer.cu
Parameters
- Parameters (
Parameter log_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/LogParameter.txt %}
{% endhighlight %}
title: Local Response Normalization (LRN)
Local Response Normalization (LRN)
- Layer type:
LRN - Doxygen Documentation
- Header:
./include/caffe/layers/lrn_layer.hpp - CPU Implementation:
./src/caffe/layers/lrn_layer.cpp - CUDA GPU Implementation:
./src/caffe/layers/lrn_layer.cu - Parameters (
LRNParameter lrn_param)- Optional
local_size[default 5]: the number of channels to sum over (for cross channel LRN) or the side length of the square region to sum over (for within channel LRN)alpha[default 1]: the scaling parameter (see below)beta[default 5]: the exponent (see below)norm_region[defaultACROSS_CHANNELS]: whether to sum over adjacent channels (ACROSS_CHANNELS) or nearby spatial locations (WITHIN_CHANNEL)
- Optional
The local response normalization layer performs a kind of “lateral inhibition” by normalizing over local input regions. In ACROSS_CHANNELS mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape local_size x 1 x 1). In WITHIN_CHANNEL mode, the local regions extend spatially, but are in separate channels (i.e., they have shape 1 x local_size x local_size). Each input value is divided by ( 1 + ( α / n ) ∑ i x i 2 ) β (1 + (\alpha/n) \sum_i x_i^2)^\beta (1+(α/n)i∑xi2)β, where n n n is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).
Parameters
- Parameters (
LRNParameter lrn_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/LRNParameter.txt %}
{% endhighlight %}
title: LSTM Layer
LSTM Layer
- Layer type:
LSTM - Doxygen Documentation
- Header:
./include/caffe/layers/lstm_layer.hpp - CPU implementation:
./src/caffe/layers/lstm_layer.cpp - CPU implementation (helper):
./src/caffe/layers/lstm_unit_layer.cpp - CUDA GPU implementation (helper):
./src/caffe/layers/lstm_unit_layer.cu
Parameters
- Parameters (
Parameter recurrent_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/RecurrentParameter.txt %}
{% endhighlight %}
title: Memory Data Layer
Memory Data Layer
- Layer type:
MemoryData - Doxygen Documentation
- Header:
./include/caffe/layers/memory_data_layer.hpp - CPU implementation:
./src/caffe/layers/memory_data_layer.cpp
The memory data layer reads data directly from memory, without copying it. In order to use it, one must call MemoryDataLayer::Reset (from C++) or Net.set_input_arrays (from Python) in order to specify a source of contiguous data (as 4D row major array), which is read one batch-sized chunk at a time.
Parameters
- Parameters (
MemoryDataParameter memory_data_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/MemoryDataParameter.txt %}
{% endhighlight %}
- Parameters
- Required
batch_size,channels,height,width: specify the size of input chunks to read from memory
- Required
title: Multinomial Logistic Loss Layer
Multinomial Logistic Loss Layer
- Layer type:
MultinomialLogisticLoss - Doxygen Documentation
- Header:
./include/caffe/layers/multinomial_logistic_loss_layer.hpp - CPU implementation:
./src/caffe/layers/multinomial_logistic_loss_layer.cpp
Parameters
- Parameters (
LossParameter loss_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/LossParameter.txt %}
{% endhighlight %}
title: Mean-Variance Normalization (MVN) Layer
Mean-Variance Normalization (MVN) Layer
- Layer type:
MVN - Doxygen Documentation
- Header:
./include/caffe/layers/mvn_layer.hpp - CPU implementation:
./src/caffe/layers/mvn_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/mvn_layer.cu
Parameters
- Parameters (
MVNParameter mvn_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/MVNParameter.txt %}
{% endhighlight %}
title: Parameter Layer
Parameter Layer
- Layer type:
Parameter - Doxygen Documentation
- Header:
./include/caffe/layers/parameter_layer.hpp - CPU implementation:
./src/caffe/layers/parameter_layer.cpp
See https://github.com/BVLC/caffe/pull/2079.
Parameters
- Parameters (
ParameterParameter parameter_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/ParameterParameter.txt %}
{% endhighlight %}
title: Pooling Layer
Pooling
-
Layer type:
Pooling -
Doxygen Documentation
-
Header:
./include/caffe/layers/pooling_layer.hpp -
CPU implementation:
./src/caffe/layers/pooling_layer.cpp -
CUDA GPU implementation:
./src/caffe/layers/pooling_layer.cu -
Input
n * c * h_i * w_i
-
Output
n * c * h_o * w_o, where h_o and w_o are computed in the same way as convolution.
Parameters
-
Parameters (
PoolingParameter pooling_param)- Required
kernel_size(orkernel_handkernel_w): specifies height and width of each filter
- Optional
pool[default MAX]: the pooling method. Currently MAX, AVE, or STOCHASTICpad(orpad_handpad_w) [default 0]: specifies the number of pixels to (implicitly) add to each side of the inputstride(orstride_handstride_w) [default 1]: specifies the intervals at which to apply the filters to the input
- Required
-
From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/PoolingParameter.txt %}
{% endhighlight %}
Sample
-
Sample (as seen in
./models/bvlc_reference_caffenet/train_val.prototxt)layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 # pool over a 3x3 region stride: 2 # step two pixels (in the bottom blob) between pooling regions } }
title: Power Layer
Power Layer
- Layer type:
Power - Doxygen Documentation
- Header:
./include/caffe/layers/power_layer.hpp - CPU implementation:
./src/caffe/layers/power_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/power_layer.cu
The Power layer computes the output as (shift + scale * x) ^ power for each input element x.
Parameters
-
Parameters (
PowerParameter power_param)- Optional
power[default 1]scale[default 1]shift[default 0]
- Optional
-
From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/PowerParameter.txt %}
{% endhighlight %}
Sample
layer {
name: "layer"
bottom: "in"
top: "out"
type: "Power"
power_param {
power: 1
scale: 1
shift: 0
}
}
See also
- Exponential layer
title: PReLU Layer
PReLU Layer
- Layer type:
PReLU - Doxygen Documentation
- Header:
./include/caffe/layers/prelu_layer.hpp - CPU implementation:
./src/caffe/layers/prelu_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/prelu_layer.cu
Parameters
- Parameters (
PReLUParameter prelu_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/PReLUParameter.txt %}
{% endhighlight %}
title: Python Layer
Python Layer
- Layer type:
Python - Doxygen Documentation
- Header:
./include/caffe/layers/python_layer.hpp
The Python layer allows users to add customized layers without modifying the Caffe core code.
Parameters
- Parameters (
PythonParameter python_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/PythonParameter.txt %}
{% endhighlight %}
Examples and tutorials
- Simple Euclidean loss example
** Python code
** Prototxt - Tutorial for writing Python layers with DIGITS
title: Recurrent Layer
Recurrent Layer
- Layer type:
Recurrent - Doxygen Documentation
- Header:
./include/caffe/layers/recurrent_layer.hpp - CPU implementation:
./src/caffe/layers/recurrent_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/recurrent_layer.cu
Parameters
- Parameters (
RecurrentParameter recurrent_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/RecurrentParameter.txt %}
{% endhighlight %}
title: Reduction Layer
Reduction Layer
- Layer type:
Reduction - Doxygen Documentation
- Header:
./include/caffe/layers/reduction_layer.hpp - CPU implementation:
./src/caffe/layers/reduction_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/reduction_layer.cu
Parameters
- Parameters (
ReductionParameter reduction_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/ReductionParameter.txt %}
{% endhighlight %}
title: ReLU / Rectified-Linear and Leaky-ReLU Layer
ReLU / Rectified-Linear and Leaky-ReLU Layer
-
Layer type:
ReLU -
Doxygen Documentation
-
Header:
./include/caffe/layers/relu_layer.hpp -
CPU implementation:
./src/caffe/layers/relu_layer.cpp -
CUDA GPU implementation:
./src/caffe/layers/relu_layer.cu -
Sample (as seen in
./models/bvlc_reference_caffenet/train_val.prototxt)layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1" }
Given an input value x, The ReLU layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
Parameters
- Parameters (
ReLUParameter relu_param)- Optional
negative_slope[default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
- Optional
- From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/ReLUParameter.txt %}
{% endhighlight %}
title: Reshape Layer
Reshape Layer
-
Layer type:
Reshape -
Doxygen Documentation
-
Header:
./include/caffe/layers/reshape_layer.hpp -
Implementation:
./src/caffe/layers/reshape_layer.cpp -
Input
- a single blob with arbitrary dimensions
-
Output
- the same blob, with modified dimensions, as specified by
reshape_param
- the same blob, with modified dimensions, as specified by
-
Sample
layer { name: "reshape" type: "Reshape" bottom: "input" top: "output" reshape_param { shape { dim: 0 # copy the dimension from below dim: 2 dim: 3 dim: -1 # infer it from the other dimensions } } }
The Reshape layer can be used to change the dimensions of its input, without changing its data. Just like the Flatten layer, only the dimensions are changed; no data is copied in the process.
Output dimensions are specified by the ReshapeParam proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:
- 0 means “copy the respective dimension of the bottom layer”. That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given
dim: 0as the 1st target dimension. - -1 stands for “infer this from the other dimensions”. This behavior is similar to that of -1 in numpy’s or
[]for MATLAB’s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation.
As another example, specifying reshape_param { shape { dim: 0 dim: -1 } } makes the layer behave in exactly the same way as the Flatten layer.
Parameters
- Parameters (
ReshapeParameter reshape_param)- Optional: (also see detailed description below)
shape
- Optional: (also see detailed description below)
- From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/ReshapeParameter.txt %}
{% endhighlight %}
title: RNN Layer
RNN Layer
- Layer type:
RNN - Doxygen Documentation
- Header:
./include/caffe/layers/rnn_layer.hpp - CPU implementation:
./src/caffe/layers/rnn_layer.cpp
Parameters
- Parameters (
RecurrentParameter recurrent_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/RecurrentParameter.txt %}
{% endhighlight %}
title: Scale Layer
Scale Layer
- Layer type:
Scale - Doxygen Documentation
- Header:
./include/caffe/layers/scale_layer.hpp - CPU implementation:
./src/caffe/layers/scale_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/scale_layer.cu
Parameters
- Parameters (
ScaleParameter scale_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/ScaleParameter.txt %}
{% endhighlight %}
title: Sigmoid Layer
Sigmoid Layer
-
Layer type:
Sigmoid -
Doxygen Documentation
-
Header:
./include/caffe/layers/sigmoid_layer.hpp -
CPU implementation:
./src/caffe/layers/sigmoid_layer.cpp -
CUDA GPU implementation:
./src/caffe/layers/sigmoid_layer.cu -
Example (from
./examples/mnist/mnist_autoencoder.prototxt):layer { name: "encode1neuron" bottom: "encode1" top: "encode1neuron" type: "Sigmoid" }
The Sigmoid layer computes sigmoid(x) for each element x in the bottom blob.
Parameters
- Parameters (
SigmoidParameter sigmoid_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/SigmoidParameter.txt %}
{% endhighlight %}
title: Sigmoid Cross-Entropy Loss Layer
Sigmoid Cross-Entropy Loss Layer
- Layer type:
SigmoidCrossEntropyLoss - Doxygen Documentation
- Header:
./include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp - CPU implementation:
./src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
To-do.
title: Silence Layer
Silence Layer
- Layer type:
Silence - Doxygen Documentation
- Header:
./include/caffe/layers/silence_layer.hpp - CPU implementation:
./src/caffe/layers/silence_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/silence_layer.cu
Silences a blob, so that it is not printed.
Parameters
No parameters.
title: Slice Layer
Slice Layer
- Layer type:
Slice - Doxygen Documentation
- Header:
./include/caffe/layers/slice_layer.hpp - CPU implementation:
./src/caffe/layers/slice_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/slice_layer.cu
The Slice layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
-
Sample
layer { name: "slicer_label" type: "Slice" bottom: "label" ## Example of label with a shape N x 3 x 1 x 1 top: "label1" top: "label2" top: "label3" slice_param { axis: 1 slice_point: 1 slice_point: 2 } }
axis indicates the target axis; slice_point indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one).
Parameters
- Parameters (
SliceParameter slice_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/SliceParameter.txt %}
{% endhighlight %}
title: Softmax Layer
Softmax Layer
- Layer type:
Softmax - Doxygen Documentation
- Header:
./include/caffe/layers/softmax_layer.hpp - CPU implementation:
./src/caffe/layers/softmax_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/softmax_layer.cu
Parameters
- Parameters (
SoftmaxParameter softmax_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/SoftmaxParameter.txt %}
{% endhighlight %}
See also
- Softmax loss layer
title: Softmax with Loss Layer
Softmax with Loss Layer
- Layer type:
SoftmaxWithLoss - Doxygen Documentation
- Header:
./include/caffe/layers/softmax_loss_layer.hpp - CPU implementation:
./src/caffe/layers/softmax_loss_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/softmax_loss_layer.cu
The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It’s conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
Parameters
- Parameters (
SoftmaxParameter softmax_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/SoftmaxParameter.txt %}
{% endhighlight %}
- Parameters (
LossParameter loss_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/LossParameter.txt %}
{% endhighlight %}
See also
- Softmax layer
title: Split Layer
Split Layer
- Layer type:
Split - Doxygen Documentation
- Header:
./include/caffe/layers/split_layer.hpp - CPU implementation:
./src/caffe/layers/split_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/split_layer.cu
The Split layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
Parameters
Does not take any parameters.
title: Spatial Pyramid Pooling Layer
Spatial Pyramid Pooling Layer
- Layer type:
SPP - Doxygen Documentation
- Header:
./include/caffe/layers/spp_layer.hpp - CPU implementation:
./src/caffe/layers/spp_layer.cpp
Parameters
- Parameters (
SPPParameter spp_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/SPPParameter.txt %}
{% endhighlight %}
title: TanH Layer
TanH Layer
- Header:
./include/caffe/layers/tanh_layer.hpp - CPU implementation:
./src/caffe/layers/tanh_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/tanh_layer.cu
Parameters
- Parameters (
TanHParameter tanh_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/TanHParameter.txt %}
{% endhighlight %}
title: Threshold Layer
Threshold Layer
- Header:
./include/caffe/layers/threshold_layer.hpp - CPU implementation:
./src/caffe/layers/threshold_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/threshold_layer.cu
Parameters
- Parameters (
ThresholdParameter threshold_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/ThresholdParameter.txt %}
{% endhighlight %}
title: Tile Layer
Tile Layer
- Layer type:
Tile - Doxygen Documentation
- Header:
./include/caffe/layers/tile_layer.hpp - CPU implementation:
./src/caffe/layers/tile_layer.cpp - CUDA GPU implementation:
./src/caffe/layers/tile_layer.cu
Parameters
- Parameters (
TileParameter tile_param) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/TileParameter.txt %}
{% endhighlight %}
title: WindowData Layer
WindowData Layer
- Layer type:
WindowData - Doxygen Documentation
- Header:
./include/caffe/layers/window_data_layer.hpp - CPU implementation:
./src/caffe/layers/window_data_layer.cpp
Parameters
- Parameters (
WindowDataParameter) - From
./src/caffe/proto/caffe.proto:
{% highlight Protobuf %}
{% include proto/WindowDataParameter.txt %}
{% endhighlight %}