Model¶
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yadll.model.
save_model
(model, file=None)[source]¶ Save the model to file with cPickle This function is used by the training function to save the model. Parameters ———- model :
yadll.model.Model
model to be saved in file- file : string
- file name
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yadll.model.
load_model
(file)[source]¶ load (unpickle) a saved model
Parameters: file : `string’
file name
Returns: Examples
>>> my_model = load_model('my_best_model.ym')
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class
yadll.model.
Model
(network=None, data=None, hyperparameters=None, name='model', updates=<function sgd>, objective=<function categorical_crossentropy_error>, evaluation_metric=<function categorical_accuracy>, file=None)[source]¶ The
yadll.model.Model
contains the data, the network, the hyperparameters, and the report. It pre-trains unsupervised layers, trains the network and save it to file.Parameters: network :
yadll.network.Network
the network to be trained
data :
yadll.data.Data
the training, validating and testing set
name : string
the name of the model
updates :
yadll.updates()
an update function
file : string
name of the file to save the model. If omitted a name is generated with the model name + date + time of training
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compile
(*args, **kwargs)[source]¶ Compile theano functions of the model
Parameters: compile_arg: `string` or `List` of `string`
value can be ‘train’, ‘validate’, ‘test’, ‘predict’ and ‘all’
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pretrain
(*args, **kwargs)[source]¶ Pre-training of the unsupervised layers sequentially
Returns: update unsupervised layers weights
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train
(*args, **kwargs)[source]¶ Training the network
Parameters: unsupervised_training: `bool`, (default is True)
pre-training of the unsupervised layers if any
save_mode : {None, ‘end’, ‘each’}
None (default), model will not be saved unless name specified in the model definition. ‘end’, model will only be saved at the end of the training ‘each’, model will be saved each time the model is improved
early_stop : bool, (default is True)
early stopping when validation score is not improving
shuffle : bool, (default is True)
reshuffle the training set at each epoch. Batches will then be different from one epoch to another
Returns: report
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