Represents a muti-layer autoencoder.
A deep autoencoder consists of an input layer, multiple encoding layers, and multiple decoding layers. It can be pre-trained as a stack of single layer autoencoders. Fine-tuning can performed on the entire autoencoder in an unsupervised manner using train(), or in a supervised manner using convert_to_neural_network().
If the autoencoder has N layers, encoding layers will be the layers following the input layer up to and including layer (N-1)/2. The rest of the layers are called the decoding layers. Note that the number of encoding layers is the same as the number of decoding layers.
The layers of the autoencoder must be symmetric in the number of neurons about the last encoding layer, that is, layer i must have the same number of neurons as layer N-i-1. For example, a valid structure could be something like: 500->250->100->250->500.
When finetuning the autoencoder in a unsupervised manner, denoising and contraction can also be used through set_contraction_coefficient() and noise_type and noise_parameter. See CAutoencoder for more details.
在文件 DeepAutoencoder.h 第 66 行定义.
Protected 成员函数 | |
virtual float64_t | compute_error (SGMatrix< float64_t > targets) |
virtual float64_t | compute_error (SGMatrix< float64_t > inputs, SGMatrix< float64_t > targets) |
virtual bool | train_machine (CFeatures *data=NULL) |
virtual bool | train_gradient_descent (SGMatrix< float64_t > inputs, SGMatrix< float64_t > targets) |
virtual bool | train_lbfgs (SGMatrix< float64_t > inputs, SGMatrix< float64_t > targets) |
virtual SGMatrix< float64_t > | forward_propagate (CFeatures *data, int32_t j=-1) |
virtual SGMatrix< float64_t > | forward_propagate (SGMatrix< float64_t > inputs, int32_t j=-1) |
virtual void | set_batch_size (int32_t batch_size) |
virtual float64_t | compute_gradients (SGMatrix< float64_t > inputs, SGMatrix< float64_t > targets, SGVector< float64_t > gradients) |
virtual bool | is_label_valid (CLabels *lab) const |
CNeuralLayer * | get_layer (int32_t i) |
SGMatrix< float64_t > | features_to_matrix (CFeatures *features) |
SGMatrix< float64_t > | labels_to_matrix (CLabels *labs) |
virtual void | store_model_features () |
virtual bool | train_require_labels () const |
virtual TParameter * | migrate (DynArray< TParameter * > *param_base, const SGParamInfo *target) |
virtual void | one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL) |
virtual void | load_serializable_pre () throw (ShogunException) |
virtual void | load_serializable_post () throw (ShogunException) |
virtual void | save_serializable_pre () throw (ShogunException) |
virtual void | save_serializable_post () throw (ShogunException) |
Protected 属性 | |
float64_t | m_sigma |
float64_t | m_contraction_coefficient |
int32_t | m_num_inputs |
int32_t | m_num_layers |
CDynamicObjectArray * | m_layers |
SGMatrix< bool > | m_adj_matrix |
int32_t | m_total_num_parameters |
SGVector< float64_t > | m_params |
SGVector< bool > | m_param_regularizable |
SGVector< int32_t > | m_index_offsets |
int32_t | m_batch_size |
bool | m_is_training |
float64_t | m_max_train_time |
CLabels * | m_labels |
ESolverType | m_solver_type |
bool | m_store_model_features |
bool | m_data_locked |
CDeepAutoencoder | ( | ) |
default constructor
在文件 DeepAutoencoder.cpp 第 46 行定义.
CDeepAutoencoder | ( | CDynamicObjectArray * | layers, |
float64_t | sigma = 0.01 |
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Constructs and initializes an autoencoder
layers | An array of CNeuralLayer objects specifying the layers of the autoencoder |
sigma | Standard deviation of the gaussian used to initialize the weights |
在文件 DeepAutoencoder.cpp 第 51 行定义.
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virtual |
在文件 DeepAutoencoder.h 第 81 行定义.
apply machine to data if data is not specified apply to the current features
data | (test)data to be classified |
在文件 Machine.cpp 第 160 行定义.
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virtualinherited |
apply machine to data in means of binary classification problem
重载 CMachine .
在文件 NeuralNetwork.cpp 第 156 行定义.
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virtualinherited |
apply machine to data in means of latent problem
被 CLinearLatentMachine 重载.
在文件 Machine.cpp 第 240 行定义.
Applies a locked machine on a set of indices. Error if machine is not locked
indices | index vector (of locked features) that is predicted |
在文件 Machine.cpp 第 195 行定义.
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virtualinherited |
applies a locked machine on a set of indices for binary problems
被 CKernelMachine , 以及 CMultitaskLinearMachine 重载.
在文件 Machine.cpp 第 246 行定义.
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virtualinherited |
applies a locked machine on a set of indices for latent problems
在文件 Machine.cpp 第 274 行定义.
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virtualinherited |
applies a locked machine on a set of indices for multiclass problems
在文件 Machine.cpp 第 260 行定义.
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virtualinherited |
applies a locked machine on a set of indices for regression problems
被 CKernelMachine 重载.
在文件 Machine.cpp 第 253 行定义.
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virtualinherited |
applies a locked machine on a set of indices for structured problems
在文件 Machine.cpp 第 267 行定义.
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virtualinherited |
apply machine to data in means of multiclass classification problem
重载 CMachine .
在文件 NeuralNetwork.cpp 第 191 行定义.
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virtualinherited |
applies to one vector
被 CKernelMachine, CRelaxedTree, CWDSVMOcas, COnlineLinearMachine, CLinearMachine, CMultitaskLinearMachine, CMulticlassMachine, CKNN, CDistanceMachine, CMultitaskLogisticRegression, CMultitaskLeastSquaresRegression, CScatterSVM, CGaussianNaiveBayes, CPluginEstimate , 以及 CFeatureBlockLogisticRegression 重载.
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virtualinherited |
apply machine to data in means of SO classification problem
被 CLinearStructuredOutputMachine 重载.
在文件 Machine.cpp 第 234 行定义.
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Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.
dict | dictionary of parameters to be built. |
在文件 SGObject.cpp 第 1185 行定义.
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virtualinherited |
Checks if the gradients computed using backpropagation are correct by comparing them with gradients computed using numerical approximation. Used for testing purposes only.
Gradients are numerically approximated according to:
\[ c = max(\epsilon x, s) \]
\[ f'(x) = \frac{f(x + c)-f(x - c)}{2c} \]
approx_epsilon | Constant used during gradient approximation |
s | Some small value, used to prevent division by zero |
在文件 NeuralNetwork.cpp 第 508 行定义.
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virtualinherited |
Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.
在文件 SGObject.cpp 第 1302 行定义.
Computes the error between the output layer's activations and the given target activations.
targets | desired values for the network's output, matrix of size num_neurons_output_layer*batch_size |
重载 CAutoencoder .
在文件 DeepAutoencoder.cpp 第 192 行定义.
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protectedvirtualinherited |
Forward propagates the inputs and computes the error between the output layer's activations and the given target activations.
inputs | inputs to the network, a matrix of size m_num_inputs*m_batch_size |
targets | desired values for the network's output, matrix of size num_neurons_output_layer*batch_size |
在文件 NeuralNetwork.cpp 第 500 行定义.
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protectedvirtualinherited |
Applies backpropagation to compute the gradients of the error with repsect to every parameter in the network.
inputs | inputs to the network, a matrix of size m_num_inputs*m_batch_size |
targets | desired values for the output layer's activations. matrix of size m_layers[m_num_layers-1].get_num_neurons()*m_batch_size |
gradients | array to be filled with gradient values. |
在文件 NeuralNetwork.cpp 第 421 行定义.
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virtualinherited |
Connects layer i as input to layer j. In order for forward and backpropagation to work correctly, i must be less that j
在文件 NeuralNetwork.cpp 第 73 行定义.
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virtual |
Converts the autoencoder into a neural network for supervised finetuning.
The neural network is formed using the input layer and the encoding layers. If specified, another output layer will added on top of those layers
output_layer | If specified, this layer will be added on top of the last encoding layer |
sigma | Standard deviation used to initialize the parameters of the output layer |
在文件 DeepAutoencoder.cpp 第 167 行定义.
Locks the machine on given labels and data. After this call, only train_locked and apply_locked may be called
Only possible if supports_locking() returns true
labs | labels used for locking |
features | features used for locking |
被 CKernelMachine 重载.
在文件 Machine.cpp 第 120 行定义.
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virtualinherited |
Unlocks a locked machine and restores previous state
被 CKernelMachine 重载.
在文件 Machine.cpp 第 151 行定义.
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virtualinherited |
A deep copy. All the instance variables will also be copied.
在文件 SGObject.cpp 第 146 行定义.
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virtualinherited |
Disconnects layer i from layer j
在文件 NeuralNetwork.cpp 第 86 行定义.
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virtualinherited |
Removes all connections in the network
在文件 NeuralNetwork.cpp 第 91 行定义.
Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!
May be overwritten but please do with care! Should not be necessary in most cases.
other | object to compare with |
accuracy | accuracy to use for comparison (optional) |
tolerant | allows linient check on float equality (within accuracy) |
在文件 SGObject.cpp 第 1206 行定义.
Ensures the given features are suitable for use with the network and returns their feature matrix
在文件 NeuralNetwork.cpp 第 568 行定义.
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protectedvirtualinherited |
Applies forward propagation, computes the activations of each layer up to layer j
data | input features |
j | layer index at which the propagation should stop. If -1, the propagation continues up to the last layer |
在文件 NeuralNetwork.cpp 第 393 行定义.
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protectedvirtualinherited |
Applies forward propagation, computes the activations of each layer up to layer j
inputs | inputs to the network, a matrix of size m_num_inputs*m_batch_size |
j | layer index at which the propagation should stop. If -1, the propagation continues up to the last layer |
在文件 NeuralNetwork.cpp 第 400 行定义.
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virtualinherited |
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inherited |
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inherited |
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inherited |
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virtualinherited |
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protectedinherited |
returns a pointer to layer i in the network
在文件 NeuralNetwork.cpp 第 677 行定义.
returns a copy of a layer's parameters array
i | index of the layer |
在文件 NeuralNetwork.cpp 第 666 行定义.
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inherited |
Returns an array holding the network's layers
在文件 NeuralNetwork.cpp 第 698 行定义.
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virtualinherited |
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inherited |
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inherited |
在文件 SGObject.cpp 第 1077 行定义.
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inherited |
Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
在文件 SGObject.cpp 第 1101 行定义.
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inherited |
Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
在文件 SGObject.cpp 第 1114 行定义.
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virtual |
Returns the name of the SGSerializable instance. It MUST BE the CLASS NAME without the prefixed `C'.
重载 CAutoencoder .
在文件 DeepAutoencoder.h 第 158 行定义.
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inherited |
returns the number of inputs the network takes
在文件 NeuralNetwork.h 第 221 行定义.
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returns the number of neurons in the output layer
在文件 NeuralNetwork.cpp 第 693 行定义.
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inherited |
returns the totat number of parameters in the network
在文件 NeuralNetwork.h 第 215 行定义.
return the network's parameter array
在文件 NeuralNetwork.h 第 218 行定义.
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inherited |
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virtualinherited |
Initializes the network
sigma | standard deviation of the gaussian used to randomly initialize the parameters |
在文件 NeuralNetwork.cpp 第 96 行定义.
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virtualinherited |
If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
generic | set to the type of the generic if returning TRUE |
在文件 SGObject.cpp 第 243 行定义.
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protectedvirtualinherited |
check whether the labels is valid.
Subclasses can override this to implement their check of label types.
lab | the labels being checked, guaranteed to be non-NULL |
重载 CMachine .
在文件 NeuralNetwork.cpp 第 643 行定义.
converts the given labels into a matrix suitable for use with network
在文件 NeuralNetwork.cpp 第 584 行定义.
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inherited |
maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)
file_version | parameter version of the file |
current_version | version from which mapping begins (you want to use Version::get_version_parameter() for this in most cases) |
file | file to load from |
prefix | prefix for members |
在文件 SGObject.cpp 第 648 行定义.
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inherited |
loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned
param_info | information of parameter |
file_version | parameter version of the file, must be <= provided parameter version |
file | file to load from |
prefix | prefix for members |
在文件 SGObject.cpp 第 489 行定义.
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virtualinherited |
Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
file | where to load from |
prefix | prefix for members |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
在文件 SGObject.cpp 第 320 行定义.
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protectedvirtualinherited |
Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.
ShogunException | Will be thrown if an error occurres. |
被 CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel , 以及 CExponentialKernel 重载.
在文件 SGObject.cpp 第 1004 行定义.
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protectedvirtualinherited |
Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.
ShogunException | Will be thrown if an error occurres. |
被 CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.
在文件 SGObject.cpp 第 999 行定义.
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inherited |
Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match
param_base | set of TParameter instances that are mapped to the provided target parameter infos |
base_version | version of the parameter base |
target_param_infos | set of SGParamInfo instances that specify the target parameter base |
在文件 SGObject.cpp 第 686 行定义.
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protectedvirtualinherited |
creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.
If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
在文件 SGObject.cpp 第 893 行定义.
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protectedvirtualinherited |
This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)
param_base | set of TParameter instances to use for migration |
target | parameter info for the resulting TParameter |
replacement | (used as output) here the TParameter instance which is returned by migration is created into |
to_migrate | the only source that is used for migration |
old_name | with this parameter, a name change may be specified |
在文件 SGObject.cpp 第 833 行定义.
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virtualinherited |
在文件 SGObject.cpp 第 209 行定义.
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Pre-trains the deep autoencoder as a stack of autoencoders
If the deep autoencoder has N layers, it is treated as a stack of (N-1)/2 single layer autoencoders. For all \( 1<i<(N-1)/2 \) an autoencoder is formed using layer i-1 as an input layer, layer i as encoding layer, and layer N-i as decoding layer.
For example, if the deep autoencoder has layers L0->L1->L2->L3->L4, two autoencoders will be formed: L0->L1->L4 and L1->L2->L3.
Training parameters for each autoencoder can be set using the pt_* public fields, i.e pt_optimization_method and pt_contraction_coefficient. Each of those fields is a vector of length (N-1)/2, where the first element sets the parameter for the first autoencoder, the second element set the parameter for the second autoencoder and so on. When required, the parameter can be set for all autoencoders using the SGVector::set_const() method.
data | Training examples |
在文件 DeepAutoencoder.cpp 第 74 行定义.
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inherited |
prints all parameter registered for model selection and their type
在文件 SGObject.cpp 第 1053 行定义.
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virtualinherited |
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virtualinherited |
Connects each layer to the layer after it. That is, connects layer i to as input to layer i+1 for all i.
在文件 NeuralNetwork.cpp 第 79 行定义.
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Forward propagates the data through the autoencoder and returns the activations of the last layer
data | Input features |
重载 CAutoencoder .
在文件 DeepAutoencoder.cpp 第 160 行定义.
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Save this object to file.
file | where to save the object; will be closed during returning if PREFIX is an empty string. |
prefix | prefix for members |
param_version | (optional) a parameter version different to (this is mainly for testing, better do not use) |
在文件 SGObject.cpp 第 261 行定义.
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protectedvirtualinherited |
Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.
ShogunException | Will be thrown if an error occurres. |
被 CKernel 重载.
在文件 SGObject.cpp 第 1014 行定义.
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protectedvirtualinherited |
Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.
ShogunException | Will be thrown if an error occurres. |
被 CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.
在文件 SGObject.cpp 第 1009 行定义.
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Sets the batch size (the number of train/test cases) the network is expected to deal with. Allocates memory for the activations, local gradients, input gradients if necessary (if the batch size is different from it's previous value)
batch_size | number of train/test cases the network is expected to deal with. |
在文件 NeuralNetwork.cpp 第 558 行定义.
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Sets the contraction coefficient
For contractive autoencoders [Rifai, 2011], a term:
\[ \frac{\lambda}{N} \sum_{k=0}^{N-1} \left \| J(x_k) \right \|^2_F \]
is added to the error, where \( \left \| J(x_k)) \right \|^2_F \) is the Frobenius norm of the Jacobian of the activations of the each encoding layer with respect to its inputs, \( N \) is the batch size, and \( \lambda \) is the contraction coefficient.
coeff | Contraction coefficient |
重载 CAutoencoder .
在文件 DeepAutoencoder.cpp 第 205 行定义.
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在文件 SGObject.cpp 第 38 行定义.
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在文件 SGObject.cpp 第 43 行定义.
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在文件 SGObject.cpp 第 48 行定义.
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在文件 SGObject.cpp 第 53 行定义.
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在文件 SGObject.cpp 第 58 行定义.
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在文件 SGObject.cpp 第 63 行定义.
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在文件 SGObject.cpp 第 68 行定义.
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在文件 SGObject.cpp 第 73 行定义.
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inherited |
在文件 SGObject.cpp 第 78 行定义.
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inherited |
在文件 SGObject.cpp 第 83 行定义.
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inherited |
在文件 SGObject.cpp 第 88 行定义.
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inherited |
在文件 SGObject.cpp 第 93 行定义.
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inherited |
在文件 SGObject.cpp 第 98 行定义.
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在文件 SGObject.cpp 第 103 行定义.
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在文件 SGObject.cpp 第 108 行定义.
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set generic type to T
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Sets the layers of the autoencoder
layers | An array of CNeuralLayer objects specifying the layers of the autoencoder |
重载 CNeuralNetwork .
在文件 DeepAutoencoder.cpp 第 61 行定义.
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Setter for store-model-features-after-training flag
store_model | whether model should be stored after training |
在文件 Machine.cpp 第 115 行定义.
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virtualinherited |
A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
被 CGaussianKernel 重载.
在文件 SGObject.cpp 第 140 行定义.
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protectedvirtualinherited |
Stores feature data of underlying model. After this method has been called, it is possible to change the machine's feature data and call apply(), which is then performed on the training feature data that is part of the machine's model.
Base method, has to be implemented in order to allow cross-validation and model selection.
NOT IMPLEMENTED! Has to be done in subclasses
被 CKernelMachine, CKNN, CLinearMulticlassMachine, CTreeMachine< T >, CTreeMachine< ConditionalProbabilityTreeNodeData >, CTreeMachine< RelaxedTreeNodeData >, CTreeMachine< id3TreeNodeData >, CTreeMachine< VwConditionalProbabilityTreeNodeData >, CTreeMachine< CARTreeNodeData >, CTreeMachine< C45TreeNodeData >, CTreeMachine< CHAIDTreeNodeData >, CTreeMachine< NbodyTreeNodeData >, CLinearMachine, CHierarchical, CDistanceMachine, CGaussianProcessMachine, CKernelMulticlassMachine , 以及 CLinearStructuredOutputMachine 重载.
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virtualinherited |
被 CKernelMachine , 以及 CMultitaskLinearMachine 重载.
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virtualinherited |
Trains the autoencoder
data | Training examples |
重载 CMachine .
在文件 Autoencoder.cpp 第 67 行定义.
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protectedvirtualinherited |
trains the network using gradient descent
在文件 NeuralNetwork.cpp 第 244 行定义.
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protectedvirtualinherited |
trains the network using L-BFGS
在文件 NeuralNetwork.cpp 第 324 行定义.
Trains a locked machine on a set of indices. Error if machine is not locked
NOT IMPLEMENTED
indices | index vector (of locked features) that is used for training |
被 CKernelMachine , 以及 CMultitaskLinearMachine 重载.
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protectedvirtualinherited |
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protectedvirtualinherited |
returns whether machine require labels for training
被 COnlineLinearMachine, CHierarchical, CLinearLatentMachine, CVwConditionalProbabilityTree, CConditionalProbabilityTree , 以及 CLibSVMOneClass 重载.
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Forward propagates the data through the autoencoder and returns the activations of the last encoding layer (layer (N-1)/2)
data | Input features |
重载 CAutoencoder .
在文件 DeepAutoencoder.cpp 第 153 行定义.
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inherited |
unset generic type
this has to be called in classes specializing a template class
在文件 SGObject.cpp 第 250 行定义.
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virtualinherited |
Updates the hash of current parameter combination
在文件 SGObject.cpp 第 196 行定义.
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inherited |
Probabilty that a hidden layer neuron will be dropped out When using this, the recommended value is 0.5
default value 0.0 (no dropout)
For more details on dropout, see paper [Hinton, 2012]
在文件 NeuralNetwork.h 第 372 行定义.
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Probabilty that a input layer neuron will be dropped out When using this, a good value might be 0.2
default value 0.0 (no dropout)
For more details on dropout, see this paper [Hinton, 2012]
在文件 NeuralNetwork.h 第 382 行定义.
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convergence criteria training stops when (E'- E)/E < epsilon where E is the error at the current iterations and E' is the error at the previous iteration default value is 1.0e-5
在文件 NeuralNetwork.h 第 397 行定义.
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Used to damp the error fluctuations when stochastic gradient descent is used. damping is done according to: error_damped(i) = c*error(i) + (1-c)*error_damped(i-1) where c is the damping coefficient
If -1, the damping coefficient is automatically computed according to: c = 0.99*gd_mini_batch_size/training_set_size + 1e-2;
default value is -1
在文件 NeuralNetwork.h 第 441 行定义.
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gradient descent learning rate, defualt value 0.1
在文件 NeuralNetwork.h 第 412 行定义.
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gradient descent learning rate decay learning rate is updated at each iteration i according to: alpha(i)=decay*alpha(i-1) default value is 1.0 (no decay)
在文件 NeuralNetwork.h 第 419 行定义.
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size of the mini-batch used during gradient descent training, if 0 full-batch training is performed default value is 0
在文件 NeuralNetwork.h 第 409 行定义.
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gradient descent momentum multiplier
default value is 0.9
For more details on momentum, see this paper [Sutskever, 2013]
在文件 NeuralNetwork.h 第 429 行定义.
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io
在文件 SGObject.h 第 461 行定义.
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L1 Regularization coeff, default value is 0.0
在文件 NeuralNetwork.h 第 362 行定义.
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L2 Regularization coeff, default value is 0.0
在文件 NeuralNetwork.h 第 359 行定义.
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Describes the connections in the network: if there's a connection from layer i to layer j then m_adj_matrix(i,j) = 1.
在文件 NeuralNetwork.h 第 455 行定义.
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number of train/test cases the network is expected to deal with. Default value is 1
在文件 NeuralNetwork.h 第 477 行定义.
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For contractive autoencoders [Rifai, 2011], a term:
\[ \frac{\lambda}{N} \sum_{k=0}^{N-1} \left \| J(x_k) \right \|^2_F \]
is added to the error, where \( \left \| J(x_k)) \right \|^2_F \) is the Frobenius norm of the Jacobian of the activations of the hidden layer with respect to its inputs, \( N \) is the batch size, and \( \lambda \) is the contraction coefficient.
Default value is 0.0.
在文件 Autoencoder.h 第 188 行定义.
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parameters wrt which we can compute gradients
在文件 SGObject.h 第 476 行定义.
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Hash of parameter values
在文件 SGObject.h 第 482 行定义.
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offsets specifying where each layer's parameters and parameter gradients are stored, i.e layer i's parameters are stored at m_params + m_index_offsets[i]
在文件 NeuralNetwork.h 第 472 行定义.
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True if the network is currently being trained initial value is false
在文件 NeuralNetwork.h 第 482 行定义.
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network's layers
在文件 NeuralNetwork.h 第 450 行定义.
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model selection parameters
在文件 SGObject.h 第 473 行定义.
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number of neurons in the input layer
在文件 NeuralNetwork.h 第 444 行定义.
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number of layer
在文件 NeuralNetwork.h 第 447 行定义.
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Array that specifies which parameters are to be regularized. This is used to turn off regularization for bias parameters
在文件 NeuralNetwork.h 第 466 行定义.
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map for different parameter versions
在文件 SGObject.h 第 479 行定义.
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parameters
在文件 SGObject.h 第 470 行定义.
array where all the parameters of the network are stored
在文件 NeuralNetwork.h 第 461 行定义.
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Standard deviation of the gaussian used to initialize the parameters
在文件 DeepAutoencoder.h 第 246 行定义.
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total number of parameters in the network
在文件 NeuralNetwork.h 第 458 行定义.
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Maximum allowable L2 norm for a neurons weights When using this, a good value might be 15
default value -1 (max-norm regularization disabled)
在文件 NeuralNetwork.h 第 389 行定义.
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maximum number of iterations over the training set. If 0, training will continue until convergence. defualt value is 0
在文件 NeuralNetwork.h 第 403 行定义.
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Controls the strength of the noise, depending on noise_type
在文件 Autoencoder.h 第 176 行定义.
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Noise type for denoising autoencoders.
If set to AENT_DROPOUT, inputs are randomly set to zero during each iteration of training with probability noise_parameter.
If set to AENT_GAUSSIAN, gaussian noise with zero mean and noise_parameter standard deviation is added to the inputs.
Default value is AENT_NONE
在文件 Autoencoder.h 第 173 行定义.
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Optimization method, default is NNOM_LBFGS
在文件 NeuralNetwork.h 第 356 行定义.
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parallel
在文件 SGObject.h 第 464 行定义.
Contraction coefficient (see CAutoencoder::set_contraction_coefficient()) for pre-training each encoding layer Default value is 0.0 for all layers
在文件 DeepAutoencoder.h 第 191 行定义.
CAutoencoder::epsilon for pre-training each encoding layer Default value is 1.0e-5 for all layers
在文件 DeepAutoencoder.h 第 211 行定义.
CAutoencoder::gd_error_damping_coeff for pre-training each encoding layer Default value is -1 for all layers
在文件 DeepAutoencoder.h 第 241 行定义.
CAutoencoder::gd_learning_rate for pre-training each encoding layer Default value is 0.1 for all layers
在文件 DeepAutoencoder.h 第 226 行定义.
CAutoencoder::gd_learning_rate_decay for pre-training each encoding layer Default value is 1.0 for all layers
在文件 DeepAutoencoder.h 第 231 行定义.
SGVector<int32_t> pt_gd_mini_batch_size |
CAutoencoder::gd_mini_batch_size for pre-training each encoding layer Default value is 0 for all layers
在文件 DeepAutoencoder.h 第 221 行定义.
CAutoencoder::gd_momentum for pre-training each encoding layer Default value is 0.9 for all layers
在文件 DeepAutoencoder.h 第 236 行定义.
CAutoencoder::l1_coefficient for pre-training each encoding layer Default value is 0.0 for all layers
在文件 DeepAutoencoder.h 第 206 行定义.
CAutoencoder::l2_coefficient for pre-training each encoding layer Default value is 0.0 for all layers
在文件 DeepAutoencoder.h 第 201 行定义.
SGVector<int32_t> pt_max_num_epochs |
CAutoencoder::max_num_epochs for pre-training each encoding layer Default value is 0 for all layers
在文件 DeepAutoencoder.h 第 216 行定义.
CAutoencoder::noise_parameter for pre-training each encoding layer Default value is 0.0 for all layers
在文件 DeepAutoencoder.h 第 185 行定义.
SGVector<int32_t> pt_noise_type |
CAutoencoder::noise_type for pre-training each encoding layer Default value is AENT_NONE for all layers
在文件 DeepAutoencoder.h 第 180 行定义.
SGVector<int32_t> pt_optimization_method |
CAutoencoder::optimization_method for pre-training each encoding layer Default value is NNOM_LBFGS for all layers
在文件 DeepAutoencoder.h 第 196 行定义.
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version
在文件 SGObject.h 第 467 行定义.