Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and convolution parameters in the opposite sense as ConvolutionLayer. More...
#include <deconv_layer.hpp>
Inheritance diagram for caffe::DeconvolutionLayer< Dtype >:
Public Member Functions | |
| DeconvolutionLayer (const LayerParameter ¶m) | |
| virtual const char * | type () const |
| Returns the layer type. | |
 Public Member Functions inherited from caffe::BaseConvolutionLayer< Dtype > | |
| BaseConvolutionLayer (const LayerParameter ¶m) | |
| virtual void | LayerSetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
| Does layer-specific setup: your layer should implement this function as well as Reshape. More... | |
| virtual void | Reshape (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
| Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs. More... | |
| virtual int | MinBottomBlobs () const |
| Returns the minimum number of bottom blobs required by the layer, or -1 if no minimum number is required. More... | |
| virtual int | MinTopBlobs () const |
| Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required. More... | |
| virtual bool | EqualNumBottomTopBlobs () const |
| Returns true if the layer requires an equal number of bottom and top blobs. More... | |
| Public Member Functions inherited from caffe::Layer< Dtype > | |
| Layer (const LayerParameter ¶m) | |
| void | SetUp (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
| Implements common layer setup functionality. More... | |
| Dtype | Forward (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
| Given the bottom blobs, compute the top blobs and the loss. More... | |
| void | Backward (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom) |
| Given the top blob error gradients, compute the bottom blob error gradients. More... | |
| vector< shared_ptr< Blob< Dtype > > > & | blobs () |
| Returns the vector of learnable parameter blobs. | |
| const LayerParameter & | layer_param () const |
| Returns the layer parameter. | |
| virtual void | ToProto (LayerParameter *param, bool write_diff=false) |
| Writes the layer parameter to a protocol buffer. | |
| Dtype | loss (const int top_index) const |
| Returns the scalar loss associated with a top blob at a given index. | |
| void | set_loss (const int top_index, const Dtype value) |
| Sets the loss associated with a top blob at a given index. | |
| virtual int | ExactNumBottomBlobs () const |
| Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required. More... | |
| virtual int | MaxBottomBlobs () const |
| Returns the maximum number of bottom blobs required by the layer, or -1 if no maximum number is required. More... | |
| virtual int | ExactNumTopBlobs () const |
| Returns the exact number of top blobs required by the layer, or -1 if no exact number is required. More... | |
| virtual int | MaxTopBlobs () const |
| Returns the maximum number of top blobs required by the layer, or -1 if no maximum number is required. More... | |
| virtual bool | AutoTopBlobs () const |
| Return whether "anonymous" top blobs are created automatically by the layer. More... | |
| virtual bool | AllowForceBackward (const int bottom_index) const |
| Return whether to allow force_backward for a given bottom blob index. More... | |
| bool | param_propagate_down (const int param_id) |
| Specifies whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. More... | |
| void | set_param_propagate_down (const int param_id, const bool value) |
| Sets whether the layer should compute gradients w.r.t. a parameter at a particular index given by param_id. | |
Protected Member Functions | |
| virtual void | Forward_cpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
| Using the CPU device, compute the layer output. | |
| virtual void | Forward_gpu (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
| Using the GPU device, compute the layer output. Fall back to Forward_cpu() if unavailable. | |
| virtual void | Backward_cpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom) |
| Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. | |
| virtual void | Backward_gpu (const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom) |
| Using the GPU device, compute the gradients for any parameters and for the bottom blobs if propagate_down is true. Fall back to Backward_cpu() if unavailable. | |
| virtual bool | reverse_dimensions () |
| virtual void | compute_output_shape () |
| Protected Member Functions inherited from caffe::BaseConvolutionLayer< Dtype > | |
| void | forward_cpu_gemm (const Dtype *input, const Dtype *weights, Dtype *output, bool skip_im2col=false) |
| void | forward_cpu_bias (Dtype *output, const Dtype *bias) |
| void | backward_cpu_gemm (const Dtype *input, const Dtype *weights, Dtype *output) |
| void | weight_cpu_gemm (const Dtype *input, const Dtype *output, Dtype *weights) |
| void | backward_cpu_bias (Dtype *bias, const Dtype *input) |
| void | forward_gpu_gemm (const Dtype *col_input, const Dtype *weights, Dtype *output, bool skip_im2col=false) |
| void | forward_gpu_bias (Dtype *output, const Dtype *bias) |
| void | backward_gpu_gemm (const Dtype *input, const Dtype *weights, Dtype *col_output) |
| void | weight_gpu_gemm (const Dtype *col_input, const Dtype *output, Dtype *weights) |
| void | backward_gpu_bias (Dtype *bias, const Dtype *input) |
| int | input_shape (int i) |
| The spatial dimensions of the input. | |
| Protected Member Functions inherited from caffe::Layer< Dtype > | |
| virtual void | CheckBlobCounts (const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top) |
| void | SetLossWeights (const vector< Blob< Dtype > *> &top) |
Additional Inherited Members | |
| Protected Attributes inherited from caffe::BaseConvolutionLayer< Dtype > | |
| Blob< int > | kernel_shape_ |
| The spatial dimensions of a filter kernel. | |
| Blob< int > | stride_ |
| The spatial dimensions of the stride. | |
| Blob< int > | pad_ |
| The spatial dimensions of the padding. | |
| Blob< int > | dilation_ |
| The spatial dimensions of the dilation. | |
| Blob< int > | conv_input_shape_ |
| The spatial dimensions of the convolution input. | |
| vector< int > | col_buffer_shape_ |
| The spatial dimensions of the col_buffer. | |
| vector< int > | output_shape_ |
| The spatial dimensions of the output. | |
| const vector< int > * | bottom_shape_ |
| int | num_spatial_axes_ |
| int | bottom_dim_ |
| int | top_dim_ |
| int | channel_axis_ |
| int | num_ |
| int | channels_ |
| int | group_ |
| int | out_spatial_dim_ |
| int | weight_offset_ |
| int | num_output_ |
| bool | bias_term_ |
| bool | is_1x1_ |
| bool | force_nd_im2col_ |
| Protected Attributes inherited from caffe::Layer< Dtype > | |
| LayerParameter | layer_param_ |
| Phase | phase_ |
| vector< shared_ptr< Blob< Dtype > > > | blobs_ |
| vector< bool > | param_propagate_down_ |
| vector< Dtype > | loss_ |
Detailed Description
template<typename Dtype>
class caffe::DeconvolutionLayer< Dtype >
Convolve the input with a bank of learned filters, and (optionally) add biases, treating filters and convolution parameters in the opposite sense as ConvolutionLayer.
ConvolutionLayer computes each output value by dotting an input window with a filter; DeconvolutionLayer multiplies each input value by a filter elementwise, and sums over the resulting output windows. In other words, DeconvolutionLayer is ConvolutionLayer with the forward and backward passes reversed. DeconvolutionLayer reuses ConvolutionParameter for its parameters, but they take the opposite sense as in ConvolutionLayer (so padding is removed from the output rather than added to the input, and stride results in upsampling rather than downsampling).
The documentation for this class was generated from the following files:
- include/caffe/layers/deconv_layer.hpp
- src/caffe/layers/deconv_layer.cpp
