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src/p/y/pylearn2-HEAD/pylearn2/scripts/gsn_example.py   pylearn2(Download)
from pylearn2.models.gsn import GSN, JointGSN
from pylearn2.termination_criteria import EpochCounter
from pylearn2.train import Train
from pylearn2.training_algorithms.sgd import SGD, MonitorBasedLRAdjuster
from pylearn2.utils import image, safe_zip
   )
 
    trainer = Train(ds, gsn, algorithm=alg, save_path="gsn_ae_example.pkl",
                    save_freq=5)
    trainer.main_loop()
    )
 
    trainer = Train(ds, gsn, algorithm=alg,
                    save_path="gsn_sup_example.pkl", save_freq=10,
                    extensions=[MonitorBasedLRAdjuster()])

src/p/y/pylearn2-HEAD/pylearn2/scripts/tutorials/deep_trainer/run_deep_trainer.py   pylearn2(Download)
from pylearn2.costs.ebm_estimation import SMD
from pylearn2.training_algorithms.sgd import MonitorBasedLRAdjuster
from pylearn2.train import Train
from optparse import OptionParser
 
    train_algo = SGD(**config)
    model = layer
    return Train(model = model,
            dataset = trainset,
            algorithm = train_algo,
    model = layer
    extensions = None
    return Train(model = model,
            algorithm = train_algo,
            extensions = extensions,
    model = layer
    extensions = [MonitorBasedLRAdjuster()]
    return Train(model = model, algorithm = train_algo,
                 save_path='grbm.pkl',save_freq=1,
                 extensions = extensions, dataset = trainset)

src/k/a/kaggle-cifar10-HEAD/kaggle_train.py   kaggle-cifar10(Download)
from pylearn2.datasets.preprocessing import GlobalContrastNormalization
from pylearn2.space import Conv2DSpace
from pylearn2.train import Train
from pylearn2.train_extensions import best_params, window_flip
from pylearn2.utils import serial
                                    center=[tst])
 
experiment = Train(dataset=trn,
                   model=mdl,
                   algorithm=trainer,

src/k/a/kaggle-dogs-vs-cats-HEAD/kaggle_train_full.py   kaggle-dogs-vs-cats(Download)
from pylearn2.termination_criteria import EpochCounter
from pylearn2.datasets import DenseDesignMatrix
from pylearn2.train import Train
from pylearn2.train_extensions import best_params
from pylearn2.space import VectorSpace
                                 decay_factor=lr*.05)
 
experiment = Train(dataset=full,
                   model=mdl,
                   algorithm=trainer,

src/p/y/pylearn2-HEAD/pylearn2/cross_validation/__init__.py   pylearn2(Download)
 
from pylearn2.cross_validation.mlp import PretrainedLayerCV
from pylearn2.train import Train, SerializationGuard
from pylearn2.utils import serial
 
            try:
                assert isinstance(datasets, dict)
                trainer = Train(datasets['train'], this_model, algorithm,
                                this_save_path, this_save_freq, extensions,
                                allow_overwrite)

src/p/y/pylearn2-HEAD/pylearn2/training_algorithms/tests/test_sgd.py   pylearn2(Download)
from pylearn2.testing.cost import CallbackCost, SumOfParams
from pylearn2.testing.datasets import ArangeDataset
from pylearn2.train import Train
from pylearn2.training_algorithms.sgd import (ExponentialDecay,
                                              MomentumAdjustor,
                    set_batch_size=False)
 
    train = Train(dataset,
                  model,
                  algorithm,
                    set_batch_size=False)
 
    train = Train(dataset,
                  model,
                  algorithm,
                    set_batch_size=False)
 
    train = Train(dataset,
                  model,
                  algorithm,
                                        decay_factor=decay_factor)
 
    train = Train(dataset,
                  model,
                  algorithm,

src/p/y/pylearn2-HEAD/pylearn2/training_algorithms/tests/test_bgd.py   pylearn2(Download)
from pylearn2.train import Train
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
from pylearn2.models.model import Model
from pylearn2.space import CompositeSpace, VectorSpace
from pylearn2.utils import sharedX
                termination_criterion = termination_criterion)
 
    train = Train(dataset, model, algorithm, save_path=None,
                 save_freq=0, extensions=None)
 
 
        train_object = Train(
                dataset=train,
                model=model,
                algorithm=algorithm,

src/p/y/pylearn2-HEAD/pylearn2/datasets/tests/test_sparse_dataset.py   pylearn2(Download)
import numpy as np
from pylearn2.datasets.sparse_dataset import SparseDataset
from pylearn2.train import Train
from pylearn2.models.model import Model
from pylearn2.space import VectorSpace
                    set_batch_size=False)
 
    train = Train(dataset, model, algorithm, save_path=None,
                  save_freq=0, extensions=None)
 

src/p/y/pylearn2-HEAD/pylearn2/tests/test_train.py   pylearn2(Download)
import numpy as np
from pylearn2.monitor import Monitor
from pylearn2.train import Train
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
from pylearn2.models.model import Model
    algorithm = DummyAlgorithm()
 
    train = Train(dataset, model, algorithm, save_path='_tmp_unit_test.pkl',
                 save_freq=1, extensions=None)
 

src/p/y/pylearn2-HEAD/pylearn2/termination_criteria/tests/test_init.py   pylearn2(Download)
from pylearn2.models.mlp import MLP, Softmax
from pylearn2.monitor import push_monitor
from pylearn2.train import Train
from pylearn2.training_algorithms.sgd import SGD
 
                        termination_criterion=epoch_counter)
 
        return Train(dataset=dataset,
                     model=model,
                     algorithm=algorithm)