chainer_chemistry.models.Regressor¶
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class
chainer_chemistry.models.
Regressor
(predictor, lossfun=<function mean_squared_error>, metrics_fun=None, label_key=-1, device=-1)[source]¶ A simple regressor model.
This is an example of chain that wraps another chain. It computes the loss and metrics based on a given input/label pair.
Parameters: - predictor (Link) – Predictor network.
- lossfun (function) – Loss function.
- metrics_fun (function or dict or None) – Function that computes metrics.
- label_key (int or str) – Key to specify label variable from arguments.
When it is
int
, a variable in positional arguments is used. And when it isstr
, a variable in keyword arguments is used. - device (int) – GPU device id of this Regressor to be used. -1 indicates to use in CPU.
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predictor
¶ Predictor network.
Type: Link
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lossfun
¶ Loss function.
Type: function
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y
¶ Prediction for the last minibatch.
Type: Variable
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loss
¶ Loss value for the last minibatch.
Type: Variable
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compute_metrics
¶ If
True
, compute metrics on the forward computation. The default value isTrue
.Type: bool
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__init__
(predictor, lossfun=<function mean_squared_error>, metrics_fun=None, label_key=-1, device=-1)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(predictor[, lossfun, metrics_fun, …])Initialize self. add_hook
(hook[, name])Registers a link hook. add_link
(name, link)Registers a child link to this chain. add_param
(name[, shape, dtype, initializer])Registers a parameter to the link. add_persistent
(name, value)Registers a persistent value to the link. addgrads
(link)Accumulates gradient values from given link. children
()Returns a generator of all child links. cleargrads
()Clears all gradient arrays. copy
([mode])Copies the link hierarchy to new one. copyparams
(link[, copy_persistent])Copies all parameters from given link. count_params
()Counts the total number of parameters. delete_hook
(name)Unregisters the link hook. disable_update
()Disables update rules of all parameters under the link hierarchy. enable_update
()Enables update rules of all parameters under the link hierarchy. get_device
()init_scope
()Creates an initialization scope. initialize
([device])Initialization of the model. links
([skipself])Returns a generator of all links under the hierarchy. load_pickle
(filepath[, device])Load the model from filepath of pickle file, and send to device namedlinks
([skipself])Returns a generator of all (path, link) pairs under the hierarchy. namedparams
([include_uninit])Returns a generator of all (path, param) pairs under the hierarchy. params
([include_uninit])Returns a generator of all parameters under the link hierarchy. predict
(data[, batchsize, converter, …])Predict label of each category by taking . register_persistent
(name)Registers an attribute of a given name as a persistent value. repeat
(n_repeat[, mode])Repeats this link multiple times to make a Sequential
.save_pickle
(filepath[, protocol])Save the model to filepath as a pickle file serialize
(serializer)Serializes the link object. to_cpu
()Copies parameter variables and persistent values to CPU. to_gpu
([device])Copies parameter variables and persistent values to GPU. to_intel64
()Copies parameter variables and persistent values to CPU. update_device
([device])zerograds
()Initializes all gradient arrays by zero. Attributes
compute_metrics
local_link_hooks
Ordered dictionary of registered link hooks. update_enabled
True
if at least one parameter has an update rule enabled.within_init_scope
True if the current code is inside of an initialization scope. xp
Array module for this link.