filename1 = 'adv_mnist_results_5-20-50-trial%s' % t
filename2 = 'adv_digits_results_5-20-50-trial%s' % t
mnist_results = {
'ensemble_cerror' : ensemble_cerror,
'ensemble_entropy' : entropy_ensemble
#'ensemble_adv_cerror' : ensemble_adv_cerror,
#'ensemble_adv_entropy' : entropy_adv_ensemble,
#'voting_entropy' : entropy_vote
#'voting_adv_entropy' : entropy_adv_vote
}
digits_results = {
'ensemble_cerror' : digits_cerror,
'ensemble_entropy' : digits_entropy
#'ensemble_adv_cerror' : digits_adv_cerror,
#'ensemble_adv_entropy' : digits_adv_entropy,
#'voting_entropy' : digits_vote
#'voting_adv_entropy' : digits_adv_vote
}
utils.save_processed_data(mnist_results,filename1)
utils.save_processed_data(digits_results,filename2)
return digits_taus, mnist_results, digits_results
utils.setup_gpu_session()
taus, mnist_results ,digits_results = experiment(network_model1, 'mlp')
"type" : "MaxPooling2D",
"pool_size" : [2,2],
"strides" : [2,2]
},
{
"type" : "Flatten"
},
{
"type" : "Dense",
"units" : 10,
"activation" : "softmax"
}
]
}
"""
utils.setup_gpu_session(True)
xtrain, ytrain, xtest, ytest = utils.load_mnist()
xtrain = xtrain.reshape(60000, 28, 28, 1)
xtest = xtest.reshape(10000, 28, 28, 1)
model_conf = json.loads(network_model1)
inputs, outputs, train_model, model_list, merge_model = ann.build_ensemble(
[model_conf])
ensemble_size = len(model_list)
lossfunctions = [
ann.adveserial_loss(klosses.categorical_crossentropy, m)
for m in model_list
]
train_model.compile(optimizer="adam", loss=lossfunctions, metrics=["accuracy"])
train_model.fit([xtrain] * ensemble_size, [ytrain] * ensemble_size,