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