def test_cifar(data_path):
print 'loading data'
dl = CIFAR10DataLoader(cifar_file_path=data_path,
load_dtype='float32')
dp = LabeledMemoryDataProvider(data_loader=dl,
batch_size=batch_size,
max_gpu_train_samples=max_gpu_samples,
max_gpu_valid_samples=max_gpu_samples,
is_test=False,
epochwise_shuffle=False,
nvalid_samples=5000)
opt = optimizers.SGD_Momentum_Optimizer()
mlp = MLP(batch_size = batch_size, seed=seed,
network_structure=mlp_ns, network_cost=mlp_cost)
mlp.fit(data_provider=dp,
optimizer=opt,
train_epoch=training_epochs,
early_stop=10,
dump_freq=(50000//100+1)*10,
train_mean=dp.get_data_stats(),
batch_mean_subtraction=True)
mlp.save('alex_cifar_26_model.joblib')
# mlp.load('alex_cifar_26_model.joblib')
#
f = open(os.path.join(data_path,'test_batch'), 'rb')
data = cPickle.load(f)
f.close()
test_set = data['data']
test_y = data['labels']
test_set = numpy.asarray(test_set, dtype='float32')
tm = dp.get_data_stats()
test_set -= tm
pred = mlp.predict_from_memory_data(test_set)
print 'Error: ', numpy.mean(pred!=test_y)
dp = LabeledMemoryDataProvider(data_loader=dl,
batch_size=batch_size,
max_gpu_train_samples=max_gpu_samples,
max_gpu_valid_samples=max_gpu_samples,
is_test=True,
epochwise_shuffle=False,
nvalid_samples=0)
pred = mlp.predict_from_data_provider(dp, train_mean=tm, batch_mean_subtraction=True)
print 'Error: ', numpy.mean(pred!=test_y)
def test_finetune():
dl = MNISTDataLoader('/data/Research/datasets/mnist/mnist.pkl')
dp = LabeledMemoryDataProvider(data_loader=dl,
batch_size=batch_size,
max_gpu_train_samples=max_gpu_samples,
max_gpu_valid_samples=max_gpu_samples,
is_test=False,
epochwise_shuffle=False,
nvalid_samples=10000)
opt = optimizers.SGD_Rms_Optimizer()
init_params = hickle.load('testmodel_ae.joblib')
mlp = MLP(batch_size = batch_size, seed=seed,
network_structure=mlp_ns, network_cost=mlp_cost,
init_params=init_params)
mlp.fit(data_provider=dp,
optimizer=opt,
train_epoch=finetune_epochs,
early_stop=False,
dump_freq=(50000//100+1)*10)
mlp.save('mnist_mlp_model.joblib')
f = open('/data/Research/datasets/mnist/mnist.pkl', 'rb')
_, _, test_set = cPickle.load(f)
f.close()
pred = mlp.predict_from_memory_data(test_set[0])
print 'Error: ', numpy.mean(pred!=test_set[1])
dp = LabeledMemoryDataProvider(data_loader=dl,
batch_size=batch_size,
max_gpu_train_samples=max_gpu_samples,
max_gpu_valid_samples=max_gpu_samples,
is_test=True,
epochwise_shuffle=False,
nvalid_samples=0)
pred = mlp.predict_from_data_provider(dp)
print 'Error: ', numpy.mean(pred!=test_set[1])
