<jupyter_start><jupyter_text>CIFAR Net<jupyter_code>from sys import path
path.insert(0, '/home/clement/Documents/Maitrise/Code/src/lib/')
from utilities import log, Process
log<jupyter_output><empty_output><jupyter_text>Import<jupyter_code>import numpy as np

from IO_tools.dataset import cifar10_datasets
from NNet.Layers import *
from flow.utils import Concat, CropConcat
from NNet import MultipleIOClassifier
from NNet.engine import TrainEngine
from NNet import load_znet
from NNet.optimizer import *
from NNet.engine import Context
from NNet.engine.context import Timestamp
import pickle<jupyter_output>Using cuDNN version 7103 on context None
Mapped name None to device cuda: GeForce GTX 860M (0000:01:00.0)<jupyter_text>Constante  Initialisation<jupyter_code>ini_process = Process('Initialisation', start=True)<jupyter_output><empty_output><jupyter_text>Initialisation Dataset<jupyter_code>d = cifar10_datasets()
d_train = d['train'].split_sets(train=0.8, valid=0.2)
datasets = {'train': d_train['train'], 'validation': d_train['valid'], 'test': d['test']}
train = datasets['train']<jupyter_output>ERROR:root:Internal Python error in the inspect module.
Below is the traceback from this internal error.<jupyter_text>Définition Réseau<jupyter_code>with Process('Réseau'):
    net = MultipleIOClassifier()
    
    (Input(net, 'x') << ConvLayer(64, 5, activation='relu') 
                     << MaxPoolLayer() << LRN(k=1., alpha=1e-3/9., beta=0.75)
                     << ConvLayer(64, 5, activation='relu')
                     << LRN(k=1., alpha=1e-3/9., beta=0.75) << MaxPoolLayer()
                     << ConvLayer(64, 3, BN=True, activation='relu') << MaxPoolLayer()
                     << FullyConnected(384,  activation='relu')
                     << FullyConnected(192,  activation='relu')
                     << FullyConnected(10,  activation=None)
                     << ClassificationOutput('y', nb_class=10, label_datatype=DataType.SCALAR, group_by='notGrouped'))

    net.end_net()
    
    net.build_model_from_dataset(train)<jupyter_output><empty_output><jupyter_text>Fin d'initialisation<jupyter_code>ini_process.succeed()<jupyter_output><empty_output><jupyter_text>Apprentissage<jupyter_code>e = TrainEngine(net, datasets, batch_size=64, optimizer=Adam(),
                macro_batch_size=100)
c = Context()
e.train(10, c=c, test_period=500)<jupyter_output><empty_output>
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