// Copyright (C) 2015 Davis E. King (davis@dlib.net) // License: Boost Software License See LICENSE.txt for the full license. #undef DLIB_DNn_LAYERS_ABSTRACT_H_ #ifdef DLIB_DNn_LAYERS_ABSTRACT_H_ #include "tensor_abstract.h" #include "core_abstract.h" namespace dlib { // ---------------------------------------------------------------------------------------- class SUBNET { /*! WHAT THIS OBJECT REPRESENTS This object represents a deep neural network. In particular, it is the simplified interface through which layer objects interact with their subnetworks. A layer's two important tasks are to (1) take outputs from its subnetwork and forward propagate them through itself and (2) to backwards propagate an error gradient through itself and onto its subnetwork. The idea of a subnetwork is illustrated in the following diagram: +---------------------------------------------------------+ | loss <-- layer1 <-- layer2 <-- ... <-- layern <-- input | +---------------------------------------------------------+ ^ ^ \__ subnetwork for layer1 __/ Therefore, by "subnetwork" we mean the part of the network closer to the input. Note that there is no dlib::SUBNET type. It is shown here purely to document the interface layer objects expect to see when they interact with a network. !*/ public: // You aren't allowed to copy subnetworks from inside a layer. SUBNET(const SUBNET&) = delete; SUBNET& operator=(const SUBNET&) = delete; const tensor& get_output( ) const; /*! ensures - returns the output of this subnetwork. This is the data that the next layer in the network will take as input. - have_same_dimensions(#get_gradient_input(), get_output()) == true !*/ tensor& get_gradient_input( ); /*! ensures - returns the error gradient for this subnetwork. That is, this is the error gradient that this network will use to update itself. Therefore, when performing back propagation, layers that sit on top of this subnetwork write their back propagated error gradients into get_gradient_input(). Or to put it another way, during back propagation, layers take the contents of their get_gradient_input() and back propagate it through themselves and store the results into their subnetwork's get_gradient_input(). !*/ const NEXT_SUBNET& subnet( ) const; /*! ensures - returns the subnetwork of *this network. With respect to the diagram above, if *this was layer1 then subnet() would return the network that begins with layer2. !*/ NEXT_SUBNET& subnet( ); /*! ensures - returns the subnetwork of *this network. With respect to the diagram above, if *this was layer1 then subnet() would return the network that begins with layer2. !*/ const layer_details_type& layer_details( ) const; /*! ensures - returns the layer_details_type instance that defines the behavior of the layer at the top of this network. I.e. returns the layer details that defines the behavior of the layer nearest to the network output rather than the input layer. For computational layers, this is the object implementing the EXAMPLE_COMPUTATIONAL_LAYER_ interface that defines the layer's behavior. !*/ unsigned int sample_expansion_factor ( ) const; /*! ensures - When to_tensor() is invoked on this network's input layer it converts N input objects into M samples, all stored inside a resizable_tensor. It is always the case that M is some integer multiple of N. sample_expansion_factor() returns the value of this multiplier. To be very specific, it is always true that M==I*N where I is some integer. This integer I is what is returned by sample_expansion_factor(). It should be noted that computational layers likely do not care about the sample expansion factor. It is only really of concern inside a loss layer where you need to know its value so that tensor samples can be matched against truth objects. Moreover, in most cases the sample expansion factor is 1. !*/ }; // ---------------------------------------------------------------------------------------- class EXAMPLE_COMPUTATIONAL_LAYER_ { /*! WHAT THIS OBJECT REPRESENTS Each computational layer in a deep neural network can be thought of as a function, f(data,parameters), that takes in a data tensor, some parameters, and produces an output tensor. You create an entire deep network by composing these functions. Importantly, you are able to use a wide range of different functions to accommodate the task you are trying to accomplish. Therefore, dlib includes a number of common layer types but if you want to define your own then you simply implement a class with the same interface as EXAMPLE_COMPUTATIONAL_LAYER_. Note that there is no dlib::EXAMPLE_COMPUTATIONAL_LAYER_ type. It is shown here purely to document the interface that a layer object must implement. The central work of defining a layer is implementing the forward and backward methods. When you do this you have four options: - Implement the forward() and backward() methods according to the specification shown below. Do not implement forward_inplace() and backward_inplace(). - Implement the forward() and backward() methods according to the specification shown below, except exclude the computed_output parameter from backward(). Doing this will allow dlib to make some layers execute in-place and therefore run a little faster and use less memory. Do not implement forward_inplace() and backward_inplace(). - Implement the forward_inplace() and backward_inplace() methods according to the specification shown below. Do not implement forward() and backward(). These in-place methods allow some types of layers to be implemented more efficiently. - Implement the forward_inplace() and backward_inplace() methods according to the specification shown below, except exclude the computed_output parameter from backward_inplace(). Doing this will allow dlib to make some layers execute in-place and therefore run a little faster and use less memory. Do not implement forward() and backward(). It should also be noted that layers may define additional layer specific fields and the solvers can use these fields as they see fit. For example, some layers define get_learning_rate_multiplier() and get_weight_decay_multiplier() methods. The solvers that come with dlib look at these methods, if they exist, and adjust the learning rate or weight decay for that layer according to the multiplier. Therefore, you can add these methods to your layer types if you want, or even define new fields and new solvers that use those fields in some way. !*/ public: EXAMPLE_COMPUTATIONAL_LAYER_( ); /*! ensures - Default constructs this object. This function is not required to do anything in particular but it must exist, that is, it is required that layer objects be default constructable. !*/ EXAMPLE_COMPUTATIONAL_LAYER_ ( const EXAMPLE_COMPUTATIONAL_LAYER_& item ); /*! ensures - EXAMPLE_COMPUTATIONAL_LAYER_ objects are copy constructable !*/ EXAMPLE_COMPUTATIONAL_LAYER_( const some_other_layer_type& item ); /*! ensures - Constructs this object from item. This form of constructor is optional but it allows you to provide a conversion from one layer type to another. For example, the following code is valid only if my_layer2 can be constructed from my_layer1: relu>>>>> my_dnn1; relu>>>>> my_dnn2(my_dnn1); This kind of pattern is useful if you want to use one type of layer during training but a different type of layer during testing since it allows you to easily convert between related deep neural network types. Additionally, if you provide a constructor to build a layer from another layer type you should also write your layer's deserialize() routine such that it can read that other layer's serialized data in addition to your own serialized data. !*/ template void setup ( const SUBNET& sub ); /*! requires - SUBNET implements the SUBNET interface defined at the top of this file. ensures - performs any necessary initial memory allocations and/or sets parameters to their initial values prior to learning. Therefore, calling setup destroys any previously learned parameters. Also, typically setup() would look at the dimensions of the outputs of sub and configure the number of parameters in *this accordingly. !*/ template void forward( const SUBNET& sub, resizable_tensor& data_output ); /*! requires - SUBNET implements the SUBNET interface defined at the top of this file. - setup() has been called. ensures - Runs the output of the subnetwork through this layer and stores the results into #data_output. In particular, forward() can use any of the outputs in sub (e.g. sub.get_output(), sub.subnet().get_output(), etc.) to compute whatever it wants. !*/ template void backward( const tensor& computed_output, // this parameter is optional const tensor& gradient_input, SUBNET& sub, tensor& params_grad ); /*! requires - SUBNET implements the SUBNET interface defined at the top of this file. - setup() has been called. - computed_output is the tensor resulting from calling forward(sub,computed_output). Moreover, this was the most recent call to forward(). This means that forward() is allowed to cache intermediate results so they can be used during the backward computation. - have_same_dimensions(gradient_input, computed_output) == true - have_same_dimensions(sub.get_gradient_input(), sub.get_output()) == true - have_same_dimensions(params_grad, get_layer_params()) == true ensures - This function outputs the gradients of this layer with respect to the input data from sub and also with respect to this layer's parameters. These gradients are stored into #sub and #params_grad, respectively. To be precise, the gradients are taken of a function f(sub,get_layer_params()) which is defined thusly: - Recalling that computed_output is a function of both sub and get_layer_params(), since it is the result of calling forward(sub,computed_output): let f(sub,get_layer_params()) == dot(computed_output, gradient_input) Then we define the following gradient vectors: - PARAMETER_GRADIENT == gradient of f(sub,get_layer_params()) with respect to get_layer_params(). - for all valid I: - DATA_GRADIENT_I == gradient of f(sub,get_layer_params()) with respect to layer(sub).get_output() (recall that forward() can draw inputs from the immediate sub layer, sub.subnet(), or any earlier layer. So you must consider the gradients with respect to all inputs drawn from sub) Finally, backward() outputs these gradients by performing: - params_grad = PARAMETER_GRADIENT - for all valid I: - layer(sub).get_gradient_input() += DATA_GRADIENT_I !*/ void forward_inplace( const tensor& data_input, tensor& data_output ); /*! requires - have_same_dimensions(data_input,data_output) == true - setup() has been called. ensures - Runs the data_input tensor through this layer and stores the output into #data_output. - This function supports in-place operation, i.e. having is_same_object(data_input, data_output)==true !*/ void backward_inplace( const tensor& computed_output, // this parameter is optional const tensor& gradient_input, tensor& data_grad, tensor& params_grad ); /*! requires - setup() has been called. - computed_output is the tensor resulting from the most recent call to forward_inplace(). This means that forward_inplace() is allowed to cache intermediate results so they can be used during the backward computation. - have_same_dimensions(gradient_input, data_grad) == true - have_same_dimensions(gradient_input, computed_output) == true - have_same_dimensions(params_grad, get_layer_params()) == true ensures - This function supports in-place operation, i.e. having is_same_object(gradient_input, data_grad)==true - This function outputs the gradients of this layer with respect to the input data from a sublayer and also with respect to this layer's parameters. These gradients are stored into #data_grad and #params_grad, respectively. To be precise, the gradients are taken of a function f(data_input,get_layer_params()) which is defined thusly: - Recalling that computed_output is a function of both the input to forward_inplace() and get_layer_params(), since it is the result of calling forward_inplace(data_input,computed_output): let f(data_input,get_layer_params()) == dot(computed_output, gradient_input) Then we define the following gradient vectors: - PARAMETER_GRADIENT == gradient of f(data_input,get_layer_params()) with respect to get_layer_params(). - DATA_GRADIENT == gradient of f(data_input,get_layer_params()) with respect to data_input. Finally, backward_inplace() outputs these gradients by performing: - params_grad = PARAMETER_GRADIENT - if (is_same_object(gradient_input, data_grad)) then - data_grad = DATA_GRADIENT - else - data_grad += DATA_GRADIENT !*/ const tensor& get_layer_params( ) const; /*! ensures - returns the parameters that define the behavior of forward(). !*/ tensor& get_layer_params( ); /*! ensures - returns the parameters that define the behavior of forward(). !*/ point map_input_to_output(point p) const; point map_output_to_input(point p) const; /*! These two functions are optional. If provided, they should map between (column,row) coordinates in input and output tensors of forward(). Providing these functions allows you to use global utility functions like input_tensor_to_output_tensor(). !*/ }; std::ostream& operator<<(std::ostream& out, const EXAMPLE_COMPUTATIONAL_LAYER_& item); /*! print a string describing this layer. !*/ void to_xml(const EXAMPLE_COMPUTATIONAL_LAYER_& item, std::ostream& out); /*! This function is optional, but required if you want to print your networks with net_to_xml(). Therefore, to_xml() prints a layer as XML. !*/ void serialize(const EXAMPLE_COMPUTATIONAL_LAYER_& item, std::ostream& out); void deserialize(EXAMPLE_COMPUTATIONAL_LAYER_& item, std::istream& in); /*! provides serialization support !*/ // For each layer you define, always define an add_layer template so that layers can be // easily composed. Moreover, the convention is that the layer class ends with an _ // while the add_layer template has the same name but without the trailing _. template using EXAMPLE_COMPUTATIONAL_LAYER = add_layer; // ---------------------------------------------------------------------------------------- // ---------------------------------------------------------------------------------------- // ---------------------------------------------------------------------------------------- enum fc_bias_mode { FC_HAS_BIAS = 0, FC_NO_BIAS = 1 }; struct num_fc_outputs { num_fc_outputs(unsigned long n) : num_outputs(n) {} unsigned long num_outputs; }; template < unsigned long num_outputs, fc_bias_mode bias_mode > class fc_ { /*! REQUIREMENTS ON num_outputs num_outputs > 0 WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it defines a fully connected layer that takes an input tensor and multiplies it by a weight matrix and outputs the results. !*/ public: fc_( ); /*! ensures - #get_num_outputs() == num_outputs - #get_bias_mode() == bias_mode - #get_learning_rate_multiplier() == 1 - #get_weight_decay_multiplier() == 1 - #get_bias_learning_rate_multiplier() == 1 - #get_bias_weight_decay_multiplier() == 0 !*/ fc_( num_fc_outputs o ); /*! ensures - #get_num_outputs() == o.num_outputs - #get_bias_mode() == bias_mode - #get_learning_rate_multiplier() == 1 - #get_weight_decay_multiplier() == 1 - #get_bias_learning_rate_multiplier() == 1 - #get_bias_weight_decay_multiplier() == 0 !*/ unsigned long get_num_outputs ( ) const; /*! ensures - This layer outputs column vectors that contain get_num_outputs() elements. That is, the output tensor T from forward() will be such that: - T.num_samples() == however many samples were given to forward(). - T.k() == get_num_outputs() - The rest of the dimensions of T will be 1. !*/ fc_bias_mode get_bias_mode ( ) const; /*! ensures - returns the bias mode which determines if this layer includes bias terms. That is, if the bias mode is FC_HAS_BIAS then a different constant scalar is added to each of the outputs of this layer. !*/ double get_learning_rate_multiplier( ) const; /*! ensures - returns a multiplier number. The interpretation is that this object is requesting that the learning rate used to optimize its parameters be multiplied by get_learning_rate_multiplier(). !*/ double get_weight_decay_multiplier( ) const; /*! ensures - returns a multiplier number. The interpretation is that this object is requesting that the weight decay used to optimize its parameters be multiplied by get_weight_decay_multiplier(). !*/ void set_learning_rate_multiplier( double val ); /*! requires - val >= 0 ensures - #get_learning_rate_multiplier() == val !*/ void set_weight_decay_multiplier( double val ); /*! requires - val >= 0 ensures - #get_weight_decay_multiplier() == val !*/ double get_bias_learning_rate_multiplier( ) const; /*! ensures - returns a multiplier number. The interpretation is that this object is requesting that the learning rate used to optimize its bias parameters be multiplied by get_learning_rate_multiplier()*get_bias_learning_rate_multiplier(). !*/ double get_bias_weight_decay_multiplier( ) const; /*! ensures - returns a multiplier number. The interpretation is that this object is requesting that the weight decay used to optimize its bias parameters be multiplied by get_weight_decay_multiplier()*get_bias_weight_decay_multiplier(). !*/ void set_bias_learning_rate_multiplier( double val ); /*! requires - val >= 0 ensures - #get_bias_learning_rate_multiplier() == val !*/ void set_bias_weight_decay_multiplier( double val ); /*! requires - val >= 0 ensures - #get_bias_weight_decay_multiplier() == val !*/ alias_tensor_const_instance get_weights( ) const; /*! ensures - returns an alias of get_layer_params(), containing the weights matrix of the fully connected layer. - #get_weights().num_samples() is the number of elements in input sample, i.e. sublayer's output's k * nc * nr. - #get_bias().k() == #get_num_outputs() - if get_bias_mode() == FC_HAS_BIAS: - #get_layer_params().size() == (#get_weights().size() + #get_biases().size()) - else: - #get_layer_params().size() == #get_weights().size() !*/ alias_tensor_instance get_weights( ); /*! ensures - returns an alias of get_layer_params(), containing the weights matrix of the fully connected layer. - #get_weights().num_samples() is the number of elements in input sample, i.e. sublayer's output's k * nc * nr. - #get_bias().k() == #get_num_outputs() - if get_bias_mode() == FC_HAS_BIAS: - #get_layer_params().size() == (#get_weights().size() + #get_biases().size()) - else: - #get_layer_params().size() == #get_weights().size() !*/ alias_tensor_const_instance get_biases( ) const; /*! requires - #get_bias_mode() == FC_HAS_BIAS ensures - returns an alias of get_layer_params(), containing the bias vector of the fully connected layer. - #get_bias().num_samples() == 1 - #get_bias().k() == #get_num_outputs() - #get_layer_params().size() == (#get_weights().size() + #get_biases().size()) !*/ alias_tensor_instance get_biases( ); /*! requires - #get_bias_mode() == FC_HAS_BIAS ensures - returns an alias of get_layer_params(), containing the bias vector of the fully connected layer. - #get_bias().num_samples() == 1 - #get_bias().k() == #get_num_outputs() - #get_layer_params().size() == (#get_weights().size() + #get_biases().size()) !*/ template void setup (const SUBNET& sub); template void forward(const SUBNET& sub, resizable_tensor& output); template void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad); const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. !*/ }; template < unsigned long num_outputs, typename SUBNET > using fc = add_layer, SUBNET>; template < unsigned long num_outputs, typename SUBNET > using fc_no_bias = add_layer, SUBNET>; // ---------------------------------------------------------------------------------------- template < long _num_filters, long _nr, long _nc, int _stride_y, int _stride_x, int _padding_y = _stride_y!=1? 0 : _nr/2, int _padding_x = _stride_x!=1? 0 : _nc/2 > class con_ { /*! REQUIREMENTS ON TEMPLATE ARGUMENTS All of them must be > 0. Also, we require that: - 0 <= _padding_y && _padding_y < _nr - 0 <= _padding_x && _padding_x < _nc WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it defines a convolution layer that takes an input tensor (nominally representing an image) and convolves it with a set of filters and then outputs the results. The dimensions of the tensors output by this layer are as follows (letting IN be the input tensor and OUT the output tensor): - OUT.num_samples() == IN.num_samples() - OUT.k() == num_filters() - OUT.nr() == 1+(IN.nr() + 2*padding_y() - nr())/stride_y() - OUT.nc() == 1+(IN.nc() + 2*padding_x() - nc())/stride_x() !*/ public: con_( ); /*! ensures - #num_filters() == _num_filters - #nr() == _nr - #nc() == _nc - #stride_y() == _stride_y - #stride_x() == _stride_x - #padding_y() == _padding_y - #padding_x() == _padding_x - #get_learning_rate_multiplier() == 1 - #get_weight_decay_multiplier() == 1 - #get_bias_learning_rate_multiplier() == 1 - #get_bias_weight_decay_multiplier() == 0 !*/ long num_filters( ) const; /*! ensures - returns the number of filters contained in this layer. The k dimension of the output tensors produced by this layer will be equal to the number of filters. !*/ long nr( ) const; /*! ensures - returns the number of rows in the filters in this layer. !*/ long nc( ) const; /*! ensures - returns the number of columns in the filters in this layer. !*/ long stride_y( ) const; /*! ensures - returns the vertical stride used when convolving the filters over an image. That is, each filter will be moved stride_y() pixels down at a time when it moves over the image. !*/ long stride_x( ) const; /*! ensures - returns the horizontal stride used when convolving the filters over an image. That is, each filter will be moved stride_x() pixels right at a time when it moves over the image. !*/ long padding_y( ) const; /*! ensures - returns the number of pixels of zero padding added to the top and bottom sides of the image. !*/ long padding_x( ) const; /*! ensures - returns the number of pixels of zero padding added to the left and right sides of the image. !*/ double get_learning_rate_multiplier( ) const; /*! ensures - returns a multiplier number. The interpretation is that this object is requesting that the learning rate used to optimize its parameters be multiplied by get_learning_rate_multiplier(). !*/ double get_weight_decay_multiplier( ) const; /*! ensures - returns a multiplier number. The interpretation is that this object is requesting that the weight decay used to optimize its parameters be multiplied by get_weight_decay_multiplier(). !*/ void set_learning_rate_multiplier( double val ); /*! requires - val >= 0 ensures - #get_learning_rate_multiplier() == val !*/ void set_weight_decay_multiplier( double val ); /*! requires - val >= 0 ensures - #get_weight_decay_multiplier() == val !*/ double get_bias_learning_rate_multiplier( ) const; /*! ensures - returns a multiplier number. The interpretation is that this object is requesting that the learning rate used to optimize its bias parameters be multiplied by get_learning_rate_multiplier()*get_bias_learning_rate_multiplier(). !*/ double get_bias_weight_decay_multiplier( ) const; /*! ensures - returns a multiplier number. The interpretation is that this object is requesting that the weight decay used to optimize its bias parameters be multiplied by get_weight_decay_multiplier()*get_bias_weight_decay_multiplier(). !*/ void set_bias_learning_rate_multiplier( double val ); /*! requires - val >= 0 ensures - #get_bias_learning_rate_multiplier() == val !*/ void set_bias_weight_decay_multiplier( double val ); /*! requires - val >= 0 ensures - #get_bias_weight_decay_multiplier() == val !*/ template void setup (const SUBNET& sub); template void forward(const SUBNET& sub, resizable_tensor& output); template void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad); point map_input_to_output(point p) const; point map_output_to_input(point p) const; const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. !*/ }; template < long num_filters, long nr, long nc, int stride_y, int stride_x, typename SUBNET > using con = add_layer, SUBNET>; // ---------------------------------------------------------------------------------------- class dropout_ { /*! WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it defines a dropout layer. Therefore, it passes its inputs through the stochastic function f(x) which outputs either 0 or x. The probability of 0 being output is given by the drop_rate argument to this object's constructor. Note that, after you finish training a network with dropout, it is a good idea to replace each dropout_ layer with a multiply_ layer because the multiply_ layer is faster and deterministic. !*/ public: explicit dropout_( float drop_rate = 0.5 ); /*! requires - 0 <= drop_rate <= 1 ensures - #get_drop_rate() == drop_rate !*/ float get_drop_rate ( ) const; /*! ensures - returns the probability that an individual input value to this layer will be replaced with 0. !*/ template void setup (const SUBNET& sub); void forward_inplace(const tensor& input, tensor& output); void backward_inplace(const tensor& gradient_input, tensor& data_grad, tensor& params_grad); point map_input_to_output(point p) const; point map_output_to_input(point p) const; const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. !*/ }; template using dropout = add_layer; // ---------------------------------------------------------------------------------------- class multiply_ { /*! WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it defines a basic layer that just multiplies its input tensor with a constant value and returns the result. It therefore has no learnable parameters. !*/ public: explicit multiply_( float val = 0.5 ); /*! ensures - #get_multiply_value() == val !*/ multiply_ ( const dropout_& item ); /*! ensures - #get_multiply_value() == 1-item.get_drop_rate() (i.e. We construct the multiply_ layer so that it is essentially a deterministic version of the given dropout_ layer) !*/ float get_multiply_value ( ) const; /*! ensures - this layer simply multiplies its input tensor by get_multiply_value() and produces the result as output. !*/ template void setup (const SUBNET& sub); void forward_inplace(const tensor& input, tensor& output); void backward_inplace(const tensor& gradient_input, tensor& data_grad, tensor& params_grad); point map_input_to_output(point p) const; point map_output_to_input(point p) const; const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. !*/ }; template using multiply = add_layer; // ---------------------------------------------------------------------------------------- enum layer_mode { CONV_MODE = 0, // convolutional mode FC_MODE = 1 // fully connected mode }; const double DEFAULT_BATCH_NORM_EPS = 0.0001; template < layer_mode mode > class bn_ { /*! WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it defines a batch normalization layer that implements the method described in the paper: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift by Sergey Ioffe and Christian Szegedy In particular, this layer produces output tensors with the same dimensionality as the input tensors, except that the mean and variances of the elements have been standardized to 0 and 1 respectively. It should also be noted that when tensors with a num_samples() dimension of 1 are passed to this layer it doesn't perform batch normalization. Instead, it runs in "inference mode" where the learned linear normalizing transformation is used to transform the tensor. Finally, after you finish training a batch normalized network, it is a good idea to replace each bn_ layer with an affine_ layer because the affine_ layer is faster and will never surprise you by performing batch normalization on tensors that have a num_samples() dimension > 1. This allows you to run large mini-batches of samples through your final network without batch normalization executing at all. !*/ public: bn_( ); /*! ensures - #get_mode() == mode - #get_running_stats_window_size() == 100 - #get_learning_rate_multiplier() == 1 - #get_weight_decay_multiplier() == 0 - #get_bias_learning_rate_multiplier() == 1 - #get_bias_weight_decay_multiplier() == 1 - #get_eps() == tt::DEFAULT_BATCH_NORM_EPS !*/ explicit bn_( unsigned long window_size, double eps = tt::DEFAULT_BATCH_NORM_EPS ); /*! requires - eps > 0 - window_size > 0 ensures - #get_mode() == mode - #get_running_stats_window_size() == window_size - #get_learning_rate_multiplier() == 1 - #get_weight_decay_multiplier() == 0 - #get_bias_learning_rate_multiplier() == 1 - #get_bias_weight_decay_multiplier() == 1 - #get_eps() == eps !*/ layer_mode get_mode( ) const; /*! ensures - returns the mode of this layer, either CONV_MODE or FC_MODE. If the mode is FC_MODE then the normalization is applied across the samples in a tensor (i.e. k()*nr()*nc() different things will be normalized). Otherwise, normalization is applied across everything except for the k() dimension, resulting in there being only k() normalization equations that are applied spatially over the tensor. Therefore, if you are putting batch normalization after a fully connected layer you should use FC_MODE. Otherwise, if you are putting batch normalization after a convolutional layer you should use CONV_MODE. !*/ double get_eps( ) const; /*! ensures - When doing batch normalization, we are dividing by the standard deviation. This epsilon value returned by this function is added to the variance to prevent the division from dividing by zero. !*/ unsigned long get_running_stats_window_size ( ) const; /*! ensures - Just as recommended in the batch normalization paper, this object keeps a running average of the mean and standard deviations of the features. These averages are used during "inference mode" so you can run a single object through a batch normalized network. They are also what is used to initialize an affine_ layer that is constructed from a bn_ layer. This function returns the effective number of recent samples used to compute the running average. !*/ void set_running_stats_window_size ( unsigned long new_window_size ); /*! requires - new_window_size > 0 ensures - #get_running_stats_window_size() == new_window_size !*/ double get_learning_rate_multiplier( ) const; /*! ensures - returns a multiplier number. The interpretation is that this object is requesting that the learning rate used to optimize its parameters be multiplied by get_learning_rate_multiplier(). !*/ double get_weight_decay_multiplier( ) const; /*! ensures - returns a multiplier number. The interpretation is that this object is requesting that the weight decay used to optimize its parameters be multiplied by get_weight_decay_multiplier(). !*/ void set_learning_rate_multiplier( double val ); /*! requires - val >= 0 ensures - #get_learning_rate_multiplier() == val !*/ void set_weight_decay_multiplier( double val ); /*! requires - val >= 0 ensures - #get_weight_decay_multiplier() == val !*/ double get_bias_learning_rate_multiplier( ) const; /*! ensures - returns a multiplier number. The interpretation is that this object is requesting that the learning rate used to optimize its bias parameters be multiplied by get_learning_rate_multiplier()*get_bias_learning_rate_multiplier(). !*/ double get_bias_weight_decay_multiplier( ) const; /*! ensures - returns a multiplier number. The interpretation is that this object is requesting that the weight decay used to optimize its bias parameters be multiplied by get_weight_decay_multiplier()*get_bias_weight_decay_multiplier(). !*/ void set_bias_learning_rate_multiplier( double val ); /*! requires - val >= 0 ensures - #get_bias_learning_rate_multiplier() == val !*/ void set_bias_weight_decay_multiplier( double val ); /*! requires - val >= 0 ensures - #get_bias_weight_decay_multiplier() == val !*/ template void setup (const SUBNET& sub); template void forward(const SUBNET& sub, resizable_tensor& output); template void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad); point map_input_to_output(point p) const; point map_output_to_input(point p) const; const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. !*/ }; template using bn_con = add_layer, SUBNET>; template using bn_fc = add_layer, SUBNET>; // ---------------------------------------------------------------------------------------- template void set_all_bn_running_stats_window_sizes ( const net_type& net, unsigned long new_window_size ); /*! requires - new_window_size > 0 - net_type is an object of type add_layer, add_loss_layer, add_skip_layer, or add_tag_layer. ensures - Sets the get_running_stats_window_size() field of all bn_ layers in net to new_window_size. !*/ // ---------------------------------------------------------------------------------------- class affine_ { /*! WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it applies a simple pointwise linear transformation to an input tensor. You can think of it as having two parameter tensors, A and B. If the input tensor is called INPUT then the output of this layer is: A*INPUT+B where all operations are performed element wise and each sample in the INPUT tensor is processed separately. Moreover, this object has two modes that effect the dimensionalities of A and B and how they are applied to compute A*INPUT+B. If get_mode()==FC_MODE then A and B each have the same dimensionality as the input tensor, except their num_samples() dimensions are 1. If get_mode()==CONV_MODE then A and B have all their dimensions set to 1 except for k(), which is equal to INPUT.k(). In either case, the computation of A*INPUT+B is performed pointwise over all the elements of INPUT using either: OUTPUT(n,k,r,c) == A(1,k,r,c)*INPUT(n,k,r,c)+B(1,k,r,c) or OUTPUT(n,k,r,c) == A(1,k,1,1)*INPUT(n,k,r,c)+B(1,k,1,1) as appropriate. Finally, note that the parameters of this layer are not learnable and therefore not modified during network updates. Instead, the layer will perform the identity transformation unless it is initialized with a bn_ layer, in which case it will perform whatever transformation the bn_ layer has learned. !*/ public: affine_( ); /*! ensures - #get_mode() == FC_MODE !*/ affine_( layer_mode mode ); /*! ensures - #get_mode() == mode !*/ template < layer_mode mode > affine_( const bn_& layer ); /*! ensures - Constructs affine_ so that it performs the same transformation as the supplied batch normalization layer. You would want to do this after you finish training a network with bn_ layers because the affine_ layer will execute faster. - #get_mode() == layer.get_mode() !*/ layer_mode get_mode( ) const; /*! ensures - returns the mode of this layer, either CONV_MODE or FC_MODE. !*/ template void setup (const SUBNET& sub); void forward_inplace(const tensor& input, tensor& output); void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad); point map_input_to_output(point p) const; point map_output_to_input(point p) const; const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. Also note that get_layer_params() always returns an empty tensor since there are no learnable parameters in this object. !*/ }; template using affine = add_layer; // ---------------------------------------------------------------------------------------- template < long _nr, long _nc, int _stride_y, int _stride_x, int _padding_y = _stride_y!=1? 0 : _nr/2, int _padding_x = _stride_x!=1? 0 : _nc/2 > class max_pool_ { /*! REQUIREMENTS ON TEMPLATE ARGUMENTS - _nr >= 0 - _nc >= 0 - _stride_y > 0 - _stride_x > 0 - _padding_y >= 0 - _padding_x >= 0 - if (_nr != 0) then - _padding_y < _nr - else - _padding_y == 0 - if (_nc != 0) then - _padding_x < _nr - else - _padding_x == 0 WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it defines a max pooling layer that takes an input tensor and downsamples it. It does this by sliding a window over the images in an input tensor and outputting, for each channel, the maximum element within the window. If _nr == 0 then it means the filter size covers all the rows in the input tensor, similarly for the _nc parameter. To be precise, if we call the input tensor IN and the output tensor OUT, then OUT is defined as follows: - let FILT_NR == (nr()==0) ? IN.nr() : nr() - let FILT_NC == (nc()==0) ? IN.nc() : nc() - OUT.num_samples() == IN.num_samples() - OUT.k() == IN.k() - OUT.nr() == 1+(IN.nr() + 2*padding_y() - FILT_NR)/stride_y() - OUT.nc() == 1+(IN.nc() + 2*padding_x() - FILT_NC)/stride_x() - for all valid s, k, r, and c: - image_plane(OUT,s,k)(r,c) == max(subm_clipped(image_plane(IN,s,k), centered_rect(x*stride_x() + FILT_NC/2 - padding_x(), y*stride_y() + FILT_NR/2 - padding_y(), FILT_NC, FILT_NR))) !*/ public: max_pool_ ( ); /*! ensures - #nr() == _nr - #nc() == _nc - #stride_y() == _stride_y - #stride_x() == _stride_x - #padding_y() == _padding_y - #padding_x() == _padding_x !*/ long nr( ) const; /*! ensures - returns the number of rows in the pooling window or 0 if the window size is "the entire input tensor". !*/ long nc( ) const; /*! ensures - returns the number of rows in the pooling window or 0 if the window size is "the entire input tensor". !*/ long stride_y( ) const; /*! ensures - returns the vertical stride used when scanning the max pooling window over an image. That is, each window will be moved stride_y() pixels down at a time when it moves over the image. !*/ long stride_x( ) const; /*! ensures - returns the horizontal stride used when scanning the max pooling window over an image. That is, each window will be moved stride_x() pixels down at a time when it moves over the image. !*/ long padding_y( ) const; /*! ensures - returns the number of pixels of zero padding added to the top and bottom sides of the image. !*/ long padding_x( ) const; /*! ensures - returns the number of pixels of zero padding added to the left and right sides of the image. !*/ template void setup (const SUBNET& sub); template void forward(const SUBNET& sub, resizable_tensor& output); template void backward(const tensor& computed_output, const tensor& gradient_input, SUBNET& sub, tensor& params_grad); point map_input_to_output(point p) const; point map_output_to_input(point p) const; const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. Note that this layer doesn't have any parameters, so the tensor returned by get_layer_params() is always empty. !*/ }; template < long nr, long nc, int stride_y, int stride_x, typename SUBNET > using max_pool = add_layer, SUBNET>; template < typename SUBNET > using max_pool_everything = add_layer, SUBNET>; // ---------------------------------------------------------------------------------------- template < long _nr, long _nc, int _stride_y, int _stride_x, int _padding_y = _stride_y!=1? 0 : _nr/2, int _padding_x = _stride_x!=1? 0 : _nc/2 > class avg_pool_ { /*! REQUIREMENTS ON TEMPLATE ARGUMENTS - _nr >= 0 - _nc >= 0 - _stride_y > 0 - _stride_x > 0 - _padding_y >= 0 - _padding_x >= 0 - if (_nr != 0) then - _padding_y < _nr - else - _padding_y == 0 - if (_nc != 0) then - _padding_x < _nr - else - _padding_x == 0 WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it defines an average pooling layer that takes an input tensor and downsamples it. It does this by sliding a window over the images in an input tensor and outputting, for each channel, the average element within the window. If _nr == 0 then it means the filter size covers all the rows in the input tensor, similarly for the _nc parameter. To be precise, if we call the input tensor IN and the output tensor OUT, then OUT is defined as follows: - let FILT_NR == (nr()==0) ? IN.nr() : nr() - let FILT_NC == (nc()==0) ? IN.nc() : nc() - OUT.num_samples() == IN.num_samples() - OUT.k() == IN.k() - OUT.nr() == 1+(IN.nr() + 2*padding_y() - FILT_NR)/stride_y() - OUT.nc() == 1+(IN.nc() + 2*padding_x() - FILT_NC)/stride_x() - for all valid s, k, r, and c: - image_plane(OUT,s,k)(r,c) == mean(subm_clipped(image_plane(IN,s,k), centered_rect(x*stride_x() + FILT_NC/2 - padding_x(), y*stride_y() + FILT_NR/2 - padding_y(), FILT_NC, FILT_NR))) !*/ public: avg_pool_ ( ); /*! ensures - #nr() == _nr - #nc() == _nc - #stride_y() == _stride_y - #stride_x() == _stride_x - #padding_y() == _padding_y - #padding_x() == _padding_x !*/ long nr( ) const; /*! ensures - returns the number of rows in the pooling window or 0 if the window size is "the entire input tensor". !*/ long nc( ) const; /*! ensures - returns the number of rows in the pooling window or 0 if the window size is "the entire input tensor". !*/ long stride_y( ) const; /*! ensures - returns the vertical stride used when scanning the pooling window over an image. That is, each window will be moved stride_y() pixels down at a time when it moves over the image. !*/ long stride_x( ) const; /*! ensures - returns the horizontal stride used when scanning the pooling window over an image. That is, each window will be moved stride_x() pixels down at a time when it moves over the image. !*/ long padding_y( ) const; /*! ensures - returns the number of pixels of zero padding added to the top and bottom sides of the image. !*/ long padding_x( ) const; /*! ensures - returns the number of pixels of zero padding added to the left and right sides of the image. !*/ template void setup (const SUBNET& sub); template void forward(const SUBNET& sub, resizable_tensor& output); template void backward(const tensor& computed_output, const tensor& gradient_input, SUBNET& sub, tensor& params_grad); point map_input_to_output(point p) const; point map_output_to_input(point p) const; const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. Note that this layer doesn't have any parameters, so the tensor returned by get_layer_params() is always empty. !*/ }; template < long nr, long nc, int stride_y, int stride_x, typename SUBNET > using avg_pool = add_layer, SUBNET>; template < typename SUBNET > using avg_pool_everything = add_layer, SUBNET>; // ---------------------------------------------------------------------------------------- class relu_ { /*! WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it defines a rectified linear layer. Therefore, it passes its inputs through the function f(x)=max(x,0) where f() is applied pointwise across the input tensor. !*/ public: relu_( ); template void setup (const SUBNET& sub); void forward_inplace(const tensor& input, tensor& output); void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad); point map_input_to_output(point p) const; point map_output_to_input(point p) const; const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. Note that this layer doesn't have any parameters, so the tensor returned by get_layer_params() is always empty. !*/ }; template using relu = add_layer; // ---------------------------------------------------------------------------------------- class prelu_ { /*! WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it defines a parametric rectified linear layer. Therefore, it passes its inputs through the function f(x) = x>0 ? x : p*x where f() is applied pointwise across the input tensor and p is a scalar parameter learned by this layer. This is the layer type introduced in the paper: He, Kaiming, et al. "Delving deep into rectifiers: Surpassing human-level performance on imagenet classification." Proceedings of the IEEE International Conference on Computer Vision. 2015. !*/ public: explicit prelu_( float initial_param_value = 0.25 ); /*! ensures - The p parameter will be initialized with initial_param_value. - #get_initial_param_value() == initial_param_value. !*/ float get_initial_param_value ( ) const; /*! ensures - returns the initial value of the prelu parameter. !*/ template void setup (const SUBNET& sub); void forward_inplace(const tensor& input, tensor& output); void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad); point map_input_to_output(point p) const; point map_output_to_input(point p) const; const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. !*/ }; template using prelu = add_layer; // ---------------------------------------------------------------------------------------- class sig_ { /*! WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it defines a sigmoid layer. Therefore, it passes its inputs through the function f(x)=1/(1+exp(-x)) where f() is applied pointwise across the input tensor. !*/ public: sig_( ); template void setup (const SUBNET& sub); void forward_inplace(const tensor& input, tensor& output); void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad); point map_input_to_output(point p) const; point map_output_to_input(point p) const; const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. Note that this layer doesn't have any parameters, so the tensor returned by get_layer_params() is always empty. !*/ }; template using sig = add_layer; // ---------------------------------------------------------------------------------------- class htan_ { /*! WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it defines a hyperbolic tangent layer. Therefore, it passes its inputs through the function f(x)=std::tanh(x) where f() is applied pointwise across the input tensor. !*/ public: htan_( ); template void setup (const SUBNET& sub); void forward_inplace(const tensor& input, tensor& output); void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad); point map_input_to_output(point p) const; point map_output_to_input(point p) const; const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. Note that this layer doesn't have any parameters, so the tensor returned by get_layer_params() is always empty. !*/ }; template using htan = add_layer; // ---------------------------------------------------------------------------------------- class softmax_ { /*! WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. In particular, it defines a softmax layer. To be precise, we define the softmax function s(x) as: s(x) == exp(x)/sum(exp(x)) where x is a vector. Then this layer treats its input tensor as a collection of multi-channel images and applies s() to each spatial location in each image. In each application, the tensor::k() channel elements at each position are input to s() and then replaced by the outputs of s(). This means that, for example, if you collapsed each output image to a 1 channel image by adding the channels then you would end up with images where each pixel value was 1. This is because the sum of the outputs of s() will always be equal to 1. !*/ public: softmax_( ); template void setup (const SUBNET& sub); void forward_inplace(const tensor& input, tensor& output); void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad); const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. Note that this layer doesn't have any parameters, so the tensor returned by get_layer_params() is always empty. !*/ }; template using softmax = add_layer; // ---------------------------------------------------------------------------------------- template < template class tag > class add_prev_ { /*! WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. This layer simply adds the output of two previous layers. In particular, it adds the tensor from its immediate predecessor layer, sub.get_output(), with the tensor from a deeper layer, layer(sub).get_output(). Therefore, you supply a tag via add_prev_'s template argument that tells it what layer to add to the output of the previous layer. The result of this addition is output by add_prev_. Finally, the addition happens pointwise according to 4D tensor arithmetic. If the dimensions don't match then missing elements are presumed to be equal to 0. Moreover, each dimension of the output tensor is equal to the maximum dimension of either of the inputs. That is, if the tensors A and B are being added to produce C then: - C.num_samples() == max(A.num_samples(), B.num_samples()) - C.k() == max(A.k(), B.k()) - C.nr() == max(A.nr(), B.nr()) - C.nc() == max(A.nc(), B.nc()) !*/ public: add_prev_( ); template void setup (const SUBNET& sub); template void forward(const SUBNET& sub, resizable_tensor& output); template void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad); const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. !*/ }; template < template class tag, typename SUBNET > using add_prev = add_layer, SUBNET>; // Here we add some convenient aliases for using add_prev_ with the tag layers. template using add_prev1 = add_prev; template using add_prev2 = add_prev; template using add_prev3 = add_prev; template using add_prev4 = add_prev; template using add_prev5 = add_prev; template using add_prev6 = add_prev; template using add_prev7 = add_prev; template using add_prev8 = add_prev; template using add_prev9 = add_prev; template using add_prev10 = add_prev; using add_prev1_ = add_prev_; using add_prev2_ = add_prev_; using add_prev3_ = add_prev_; using add_prev4_ = add_prev_; using add_prev5_ = add_prev_; using add_prev6_ = add_prev_; using add_prev7_ = add_prev_; using add_prev8_ = add_prev_; using add_prev9_ = add_prev_; using add_prev10_ = add_prev_; // ---------------------------------------------------------------------------------------- template< template class... TAG_TYPES > class concat_ { /*! WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. This layer simply concatenates the output of tagged layers. Importantly, each input layer must have the same dimensions (i.e. num_samples, nr, and nc) except for the k channel, which may vary. This is because the concatenation happens along the k dimension. That is, the output of this network is a tensor, OUT, that is the concatenation of the tensors: for each (tag in TAG_TYPES) layer(subnet).get_output() Therefore, out.num_samples(), out.nr(), and out.nc() match the dimensions of the input tensors while OUT.k() is the sum of the input layer's k() dimensions. !*/ public: template void setup (const SUBNET& sub); template void forward(const SUBNET& sub, resizable_tensor& output); template void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad); point map_input_to_output(point p) const; point map_output_to_input(point p) const; const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. !*/ }; // concat layer definitions template class TAG1, template class TAG2, typename SUBNET> using concat2 = add_layer, SUBNET>; template class TAG1, template class TAG2, template class TAG3, typename SUBNET> using concat3 = add_layer, SUBNET>; template class TAG1, template class TAG2, template class TAG3, template class TAG4, typename SUBNET> using concat4 = add_layer, SUBNET>; template class TAG1, template class TAG2, template class TAG3, template class TAG4, template class TAG5, typename SUBNET> using concat5 = add_layer, SUBNET>; // ---------------------------------------------------------------------------------------- /*!A inception layer definitions !*/ // Now define inception layer tag types. These layer aliases allow creating // the networks described in the paper: // Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of // the IEEE Conference on Computer Vision and Pattern Recognition. 2015. // See the dnn_inception_ex.cpp example for a complete example of their use. Note also // that we use tag ID numbers >= 1000 to avoid conflict with user's tag layers. template using itag0 = add_tag_layer< 1000 + 0, SUBNET>; template using itag1 = add_tag_layer< 1000 + 1, SUBNET>; template using itag2 = add_tag_layer< 1000 + 2, SUBNET>; template using itag3 = add_tag_layer< 1000 + 3, SUBNET>; template using itag4 = add_tag_layer< 1000 + 4, SUBNET>; template using itag5 = add_tag_layer< 1000 + 5, SUBNET>; // skip to inception input template using iskip = add_skip_layer< itag0, SUBNET>; // here are some templates to be used for creating inception layer groups template class B1, templateclass B2, typename SUBNET> using inception2 = concat2>>>>>>; template class B1, templateclass B2, templateclass B3, typename SUBNET> using inception3 = concat3>>>>>>>>>; template class B1, templateclass B2, templateclass B3, templateclass B4, typename SUBNET> using inception4 = concat4>>>>>>>>>>>>; template class B1, templateclass B2, templateclass B3, templateclass B4, templateclass B5, typename SUBNET> using inception5 = concat5>>>>>>>>>>>>>>>; // ---------------------------------------------------------------------------------------- const double DEFAULT_L2_NORM_EPS = 1e-5; class l2normalize_ { /*! WHAT THIS OBJECT REPRESENTS This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface defined above. It takes tensors as input and L2 normalizes them. In particular, it has the following properties: - The output tensors from this layer have the same dimenstions as the input tensors. - If you think of each input tensor as a set of tensor::num_samples() vectors, then the output tensor contains the same vectors except they have been length normlized so that their L2 norms are all 1. I.e. for each vector v we will have ||v||==1. !*/ public: explicit l2normalize_( double eps = tt::DEFAULT_L2_NORM_EPS ); /*! requires - eps > 0 ensures - #get_eps() == eps !*/ double get_eps( ) const; /*! ensures - When we normalize a vector we divide it by its L2 norm. However, the get_eps() value is added to the squared norm prior to division to avoid ever dividing by zero. !*/ template void setup (const SUBNET& sub); void forward_inplace(const tensor& input, tensor& output); void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad); const tensor& get_layer_params() const; tensor& get_layer_params(); /*! These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface. !*/ }; // ---------------------------------------------------------------------------------------- } #endif // DLIB_DNn_LAYERS_ABSTRACT_H_