def conv2d(layer):
h, w = layer.get_config()['kernel_size']
d = layer.input_shape[-1]
n = layer.output_shape[-1]
s0, s1 = layer.get_config()['strides']
W = layer.get_weights()[0]
B = layer.get_weights()[1]
module = modules.Convolution(filtersize=(h, w, d, n), stride=(s0, s1))
module.W = W
module.B = B
activation_module = get_activation_lrpmodule(layer.activation)
return module, activation_module
def roar_kar(keep, random=False, train_only=False):
logdir = 'tf_logs/standard/'
def get_savedir():
savedir = logdir.replace('tf_logs', 'KAR' if keep else 'ROAR')
if not os.path.exists(savedir):
os.makedirs(savedir)
return savedir
# ratio = 0.1
percentiles = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
attribution_methods = ['normal', 'LRP', 'proposed_method']
if not train_only:
DNN = model_io.read('../models/MNIST/LeNet-5.nn')
for v in attribution_methods:
batch_size = 128
print("{} Step is start".format(v))
if random:
print("{} percentile Remove".format(v))
occlude_dataset(DNN=DNN,
attribution=v,
percentiles=percentiles,
random=True,
keep=keep,
batch_size=batch_size,
savedir=get_savedir())
else:
print("{} Random Remove".format(v))
occlude_dataset(DNN=DNN,
attribution=v,
percentiles=percentiles,
random=False,
keep=keep,
batch_size=batch_size,
savedir=get_savedir())
print("{} : occlude step is done".format(v))
print("ress record")
ress = {k: [] for k in attribution_methods}
for _ in range(3):
for v in attribution_methods:
res = []
for p in percentiles:
occdir = get_savedir() + '{}_{}_{}.pickle'.format('{}', v, p)
occdir_y = get_savedir() + '{}_{}_{}_{}.pickle'.format(
'{}', v, p, 'label')
data_train = unpickle(occdir.format('train'))
# data_test = unpickle(occdir.format('test'))
Xtrain = np.array(data_train)
Ytrain = unpickle(occdir_y.format('train'))
Ytrain = np.array(Ytrain)
Xtest = data_io.read('../data/MNIST/test_images.npy')
Ytest = data_io.read('../data/MNIST/test_labels.npy')
print("check : {}".format(Ytrain.shape))
Xtest = scale(Xtest)
Xtest = np.reshape(Xtest, [Xtest.shape[0], 28, 28, 1])
Xtest = np.pad(Xtest, ((0, 0), (2, 2), (2, 2), (0, 0)),
'constant',
constant_values=(-1., ))
Ix = Ytest[:, 0].astype(int)
Ytest = np.zeros([Xtest.shape[0], np.unique(Ytest).size])
Ytest[np.arange(Ytest.shape[0]), Ix] = 1
print(occdir)
# DNN = model_io.read('../models/MNIST/LeNet-5.nn')
DNN = modules.Sequential([
modules.Convolution(filtersize=(5,5,1,10),stride = (1,1)),\
modules.Rect(),\
modules.SumPool(pool=(2,2),stride=(2,2)),\
modules.Convolution(filtersize=(5,5,10,25),stride = (1,1)),\
modules.Rect(),\
modules.SumPool(pool=(2,2),stride=(2,2)),\
modules.Convolution(filtersize=(4,4,25,100),stride = (1,1)),\
modules.Rect(),\
modules.SumPool(pool=(2,2),stride=(2,2)),\
modules.Convolution(filtersize=(1,1,100,10),stride = (1,1)),\
modules.Flatten()
])
print("training...")
DNN.train(X=Xtrain,\
Y=Ytrain,\
Xval=Xtest,\
Yval=Ytest,\
iters=10**5,\
lrate=0.0001,\
# status = 2,\
batchsize = 128
)
# ypred = DNN.forward(Xtest)
acc = np.mean(
np.argmax(DNN.forward(Xtest), axis=1) == np.argmax(Ytest,
axis=1))
del DNN
print('metric model test accuracy is: {:0.4f}'.format(acc))
res.append(acc)
print("End of {}:training, accuracy...".format(_))
ress[v].append(res)
print("metric...")
res_mean = {v: np.mean(v, axis=0) for v in ress.item()}
print(res_mean)
return res_mean
Xtest = np.pad(Xtest, ((0, 0), (2, 2), (2, 2), (0, 0)),
'constant',
constant_values=(-1., ))
#transform numeric class labels to indicator vectors.
I = Ytrain[:, 0].astype(int)
Ytrain = np.zeros([Xtrain.shape[0], np.unique(Ytrain).size])
Ytrain[np.arange(Ytrain.shape[0]), I] = 1
I = Ytest[:, 0].astype(int)
Ytest = np.zeros([Xtest.shape[0], np.unique(Ytest).size])
Ytest[np.arange(Ytest.shape[0]), I] = 1
#model a network according to LeNet-5 architecture
lenet = modules.Sequential([
modules.Convolution(filtersize=(5,5,1,10),stride = (1,1)),\
modules.Rect(),\
modules.SumPool(pool=(2,2),stride=(2,2)),\
modules.Convolution(filtersize=(5,5,10,25),stride = (1,1)),\
modules.Rect(),\
modules.SumPool(pool=(2,2),stride=(2,2)),\
modules.Convolution(filtersize=(4,4,25,100),stride = (1,1)),\
modules.Rect(),\
modules.SumPool(pool=(2,2),stride=(2,2)),\
modules.Convolution(filtersize=(1,1,100,10),stride = (1,1)),\
modules.Flatten()
])
#train the network.
lenet.train( X=Xtrain,\
Y=Ytrain,\