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| -rw-r--r-- | ml/dlib/dlib/dnn/utilities_abstract.h | 127 |
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diff --git a/ml/dlib/dlib/dnn/utilities_abstract.h b/ml/dlib/dlib/dnn/utilities_abstract.h new file mode 100644 index 000000000..2a9a3d3fc --- /dev/null +++ b/ml/dlib/dlib/dnn/utilities_abstract.h @@ -0,0 +1,127 @@ +// Copyright (C) 2016 Davis E. King (davis@dlib.net) +// License: Boost Software License See LICENSE.txt for the full license. +#undef DLIB_DNn_UTILITIES_ABSTRACT_H_ +#ifdef DLIB_DNn_UTILITIES_ABSTRACT_H_ + +#include "core_abstract.h" +#include "../geometry/vector_abstract.h" + +namespace dlib +{ + +// ---------------------------------------------------------------------------------------- + + double log1pexp( + double x + ); + /*! + ensures + - returns log(1+exp(x)) + (except computes it using a numerically accurate method) + !*/ + +// ---------------------------------------------------------------------------------------- + + void randomize_parameters ( + tensor& params, + unsigned long num_inputs_and_outputs, + dlib::rand& rnd + ); + /*! + ensures + - This function assigns random values into params based on the given random + number generator. In particular, it uses the parameter initialization method + of formula 16 from the paper "Understanding the difficulty of training deep + feedforward neural networks" by Xavier Glorot and Yoshua Bengio. + - It is assumed that the total number of inputs and outputs from the layer is + num_inputs_and_outputs. That is, you should set num_inputs_and_outputs to + the sum of the dimensionalities of the vectors going into and out of the + layer that uses params as its parameters. + !*/ + +// ---------------------------------------------------------------------------------------- + + template <typename net_type> + void net_to_xml ( + const net_type& net, + std::ostream& out + ); + /*! + requires + - net_type is an object of type add_layer, add_loss_layer, add_skip_layer, or + add_tag_layer. + - All layers in the net must provide to_xml() functions. + ensures + - Prints the given neural network object as an XML document to the given output + stream. + !*/ + + template <typename net_type> + void net_to_xml ( + const net_type& net, + const std::string& filename + ); + /*! + requires + - net_type is an object of type add_layer, add_loss_layer, add_skip_layer, or + add_tag_layer. + - All layers in the net must provide to_xml() functions. + ensures + - This function is just like the above net_to_xml(), except it writes to a file + rather than an ostream. + !*/ + +// ---------------------------------------------------------------------------------------- + + template <typename net_type> + dpoint input_tensor_to_output_tensor( + const net_type& net, + dpoint p + ); + /*! + requires + - net_type is an object of type add_layer, add_skip_layer, or add_tag_layer. + - All layers in the net must provide map_input_to_output() functions. + ensures + - Given a dpoint (i.e. a row,column coordinate) in the input tensor given to + net, this function returns the corresponding dpoint in the output tensor + net.get_output(). This kind of mapping is useful when working with fully + convolutional networks as you will often want to know what parts of the + output feature maps correspond to what parts of the input. + - If the network contains skip layers then any layers skipped over by the skip + layer are ignored for the purpose of computing this coordinate mapping. That + is, if you walk the network from the output layer to the input layer, where + each time you encounter a skip layer you jump to the layer indicated by the + skip layer, you will visit exactly the layers in the network involved in the + input_tensor_to_output_tensor() calculation. This behavior is useful since it + allows you to compute some auxiliary DNN as a separate branch of computation + that is separate from the main network's job of running some kind of fully + convolutional network over an image. For instance, you might want to have a + branch in your network that computes some global image level + summarization/feature. + !*/ + +// ---------------------------------------------------------------------------------------- + + template <typename net_type> + dpoint output_tensor_to_input_tensor( + const net_type& net, + dpoint p + ); + /*! + requires + - net_type is an object of type add_layer, add_skip_layer, or add_tag_layer. + - All layers in the net must provide map_output_to_input() functions. + ensures + - This function provides the reverse mapping of input_tensor_to_output_tensor(). + That is, given a dpoint in net.get_output(), what is the corresponding dpoint + in the input tensor? + !*/ + +// ---------------------------------------------------------------------------------------- + +} + +#endif // DLIB_DNn_UTILITIES_ABSTRACT_H_ + + |