// Copyright (C) 2003--2004 Ronan Collobert (collober@idiap.ch)
//                
// This file is part of Torch 3.1.
//
// All rights reserved.
// 
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions
// are met:
// 1. Redistributions of source code must retain the above copyright
//    notice, this list of conditions and the following disclaimer.
// 2. 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.
// 3. The name of the author may not be used to endorse or promote products
//    derived from this software without specific prior written permission.
// 
// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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 MLP_INC
#define MLP_INC

#include "ConnectedMachine.h"

namespace Torch {

/** A Multi-Layer Perceptron.

   @author Ronan Collobert (collober@idiap.ch)
*/
class MLP : public ConnectedMachine
{
  public:
    GradientMachine **layers;
    int n_layers;
    bool *is_linear;

    /** Create a MLP with #n_layers# layers and #n_inputs_# inputs.
        The definitions of the layer come then: it's a string
        followed by an integer for the number of outputs of the layer.
        Valid strings are "linear", "tanh", "sigmoid", "softmax", "log-softmax",
        "exp" and "softplus".

        Example: to create an MLP with one linear layer and one softmax layer,
        MLP(2, n_inputs, "linear", n_outputs, "softmax", n_outputs);
    */
    MLP(int n_layers, int n_inputs_, ...);

    /// Set the weight decay in all Linear layers.
    void setWeightDecay(real weight_decay);

    virtual ~MLP();
};

}

#endif
