/*************************************************************************** *   Copyright (C) 2008 by Yann LeCun and Pierre Sermanet * *   yann@cs.nyu.edu, pierre.sermanet@gmail.com * * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: *     * Redistributions of source code must retain the above copyright *       notice, this list of conditions and the following disclaimer. *     * Redistributions in binary form must reproduce the above copyright *       notice, this list of conditions and the following disclaimer in the *       documentation and/or other materials provided with the distribution. *     * Redistribution under a license not approved by the Open Source *       Initiative (http://www.opensource.org) must display the *       following acknowledgement in all advertising material: *        This product includes software developed at the Courant *        Institute of Mathematical Sciences (http://cims.nyu.edu). *     * The names of the authors may not be used to endorse or promote products *       derived from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESS OR IMPLIED * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE * DISCLAIMED. IN NO EVENT SHALL ThE AUTHORS BE LIABLE FOR ANY * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ***************************************************************************/#ifndef NET_H_#define NET_H_#include "Ebm.h"namespace ebl {//! Standard LeNet5-type architecture without the final e-dist RBF layer.class net_cscscf : public module_1_1<state_idx, state_idx> {public:	c_layer 	*c0_module;	state_idx *c0_state;	module_1_1<state_idx,state_idx> *c0_squash;	s_layer 	*s0_module;	state_idx *s0_state;	module_1_1<state_idx,state_idx> *s0_squash;	c_layer 	*c1_module;	state_idx *c1_state;	module_1_1<state_idx,state_idx> *c1_squash;	s_layer 	*s1_module;	state_idx *s1_state;	module_1_1<state_idx,state_idx> *s1_squash;	c_layer 	*c2_module;	state_idx *c2_state;	module_1_1<state_idx,state_idx> *c2_squash;	f_layer 	*f_module;	module_1_1<state_idx,state_idx> *f_squash;	//! makes a new net-cscscf module.	//! <ini> <inj>: expected max size of input for preallocation of internal states	//! <ki0> <kj0>: kernel size for first convolutional layer	//! <tbl0>: table of connections between input anf feature maps for first layer	//! <si0> <sj0>: subsampling for first layer	//! <ki1> <kj1> <tbl1> <si1> <sj1>: same for next 2 layers	//! <ki2> <kj2> <tbl2>: same for last convolution layer	//! <outthick>: number of outputs.	//! <prm> an idx1-ddparam in which the parameters will be allocated.	net_cscscf() {}	net_cscscf(parameter *prm, intg ini, intg inj, 			intg ki0, intg kj0, Idx<intg> *tbl0, intg si0, intg sj0,			intg ki1, intg kj1, Idx<intg> *tbl1, intg si1, intg sj1,			intg ki2, intg kj2, Idx<intg> *tbl2, 			intg outthick);	virtual void init(parameter *prm, intg ini, intg inj, 			intg ki0, intg kj0, Idx<intg> *tbl0, intg si0, intg sj0,			intg ki1, intg kj1, Idx<intg> *tbl1, intg si1, intg sj1,			intg ki2, intg kj2, Idx<intg> *tbl2, 			intg outthick);		virtual ~net_cscscf();		//! initialize the weights to random values	void forget(forget_param_linear &fp);	//! fprop from in to out	virtual void fprop(state_idx *in, state_idx *out);	//! bprop	virtual void bprop(state_idx *in, state_idx *out);	//! bbprop	virtual void bbprop(state_idx *in, state_idx *out);};//////////////////////////////////////////////////////////////////! create a new instance of net-cscscf implementing a LeNet-5 type//! convolutional neural net. This network has regular sigmoid //! units on the output, not an extra RBF layer as described //! in the Proc. IEEE paper. The network has 6 feature//! maps at the first layer and 16 feature maps at the second layer//! with a connection matrix between feature maps as described in //! the paper.//! Arguments://! {<pre>//!  <image-height> <image-width>: height and width of input image//!  <ki0> <kj0>: height and with of convolutional kernel, first layer.//!  <si0> <sj0>: subsampling ratio of subsampling layer, second layer.//!  <ki1> <kj1>: height and with of convolutional kernel, third layer.//!  <si1> <sj1>: subsampling ratio of subsampling layer, fourth layer.//!  <hid>: number of hidden units, fifth layer//!  <output-size>: number of output units//!  <net-param>: idx1-ddparam that will hold the trainable parameters//!               of the network//! </pre>}//! example//! {<code>//!  (setq p (new idx1-ddparam 0 0.1 0.02 0.02 80000))//!  (setq z (new-lenet5 32 32 5 5 2 2 5 5 2 2 120 10 p))//! </code>}class lenet5 : public net_cscscf {public:	Idx<intg> table0;		Idx<intg> table1;		Idx<intg> table2;				lenet5(parameter *net_param, intg image_height, intg image_width,			intg ki0, intg kj0, intg si0, intg sj0,			intg ki1, intg kj1, intg si1, intg sj1,			intg hid, intg output_size);	virtual ~lenet5() {}};//////////////////////////////////////////////////////////////////! Lenet7, similar to lenet5 with different neural connections.class lenet7 : public net_cscscf {public:	Idx<intg> table0;		Idx<intg> table1;		Idx<intg> table2;				lenet7(parameter *net_param, intg image_height, intg image_width);	virtual ~lenet7() {}};//////////////////////////////////////////////////////////////////! a module that takes an idx3 as input, runs it through//! a machine, and runs the output of the machine through//! a cost function whose second output is the desired label//! stored in an idx0 of int.class idx3_supervised_module {public:  net_cscscf 		*machine;  state_idx			*mout;  edist_cost 		*cost;  max_classer 		*classifier;  idx3_supervised_module(net_cscscf *m, edist_cost *c, max_classer *cl);  virtual ~idx3_supervised_module();  void fprop(state_idx *input, class_state *output,   		Idx<ubyte> *desired, state_idx *energy);  void use(state_idx *input, class_state *output);  void bprop(state_idx *input, class_state *output,   		Idx<ubyte> *desired, state_idx *energy);  void bbprop(state_idx *input, class_state *output,   		Idx<ubyte> *desired, state_idx *energy);};////////////////////////////////////////////////////////////////} // end namespace ebl#endif /* NET_H_ */