Я пытаюсь провести глубокую атаку с помощью автоэнкодера, но она выдает ошибку ниже:
InvalidArgumentError Traceback (most recent call
last)
c:\users\MrUserMan\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\client\session.py
in _do_call(self, fn, *args) 1333 try:
-> 1334 return fn(*args) 1335 except errors.OpError as e:
c:\users\MrUserMan\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\client\session.py
in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1318 return self._call_tf_sessionrun(
-> 1319 options, feed_dict, fetch_list, target_list, run_metadata) 1320
c:\users\MrUserMan\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\client\session.py
in _call_tf_sessionrun(self, options, feed_dict, fetch_list,
target_list, run_metadata) 1406 self._session, options,
feed_dict, fetch_list, target_list,
-> 1407 run_metadata) 1408
InvalidArgumentError: ValueError: could not broadcast input array from
shape (28,28,28) into shape (28,28,1) Traceback (most recent call
last):
File
"c:\users\MrUserMan\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\ops\script_ops.py",
line 206, in __call__
ret = func(*args)
File
"c:\users\MrUserMan\appdata\local\programs\python\python35\lib\site-packages\cleverhans\attacks\__init__.py",
line 1463, in deepfool_wrap
self.nb_classes)
File
"c:\users\MrUserMan\appdata\local\programs\python\python35\lib\site-packages\cleverhans\attacks_tf.py",
line 1222, in deepfool_batch
feed=feed)
File
"c:\users\MrUserMan\appdata\local\programs\python\python35\lib\site-packages\cleverhans\attacks_tf.py",
line 1294, in deepfool_attack
r_tot[idx, ...] = r_tot[idx, ...] + r_i
ValueError: could not broadcast input array from shape (28,28,28) into
shape (28,28,1)
[[{{node PyFunc}} = PyFunc[Tin=[DT_FLOAT], Tout=[DT_FLOAT],
token="pyfunc_0",
_device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_Placeholder_2_0_0)]]
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call
last) <ipython-input-19-650e99de52f0> in <module>
1 deepfool = DeepFool(model, sess=sess)
2
----> 3 adv_x = deepfool.generate_np(X_test, overshoot=0.02, max_iter=50, nb_candidate=2)
c:\users\MrUserMan\appdata\local\programs\python\python35\lib\site-packages\cleverhans\attacks\__init__.py
in generate_np(self, x_val, **kwargs)
201 feed_dict[new_kwargs[name]] = feedable[name]
202
--> 203 return self.sess.run(x_adv, feed_dict)
204
205 def construct_variables(self, kwargs):
c:\users\MrUserMan\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\client\session.py
in run(self, fetches, feed_dict, options, run_metadata)
927 try:
928 result = self._run(None, fetches, feed_dict, options_ptr,
--> 929 run_metadata_ptr)
930 if run_metadata:
931 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
c:\users\MrUserMan\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\client\session.py
in _run(self, handle, fetches, feed_dict, options, run_metadata)
1150 if final_fetches or final_targets or (handle and
feed_dict_tensor): 1151 results = self._do_run(handle,
final_targets, final_fetches,
-> 1152 feed_dict_tensor, options, run_metadata) 1153 else: 1154 results = []
c:\users\MrUserMan\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\client\session.py
in _do_run(self, handle, target_list, fetch_list, feed_dict, options,
run_metadata) 1326 if handle is None: 1327 return
self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1328 run_metadata) 1329 else: 1330 return self._do_call(_prun_fn, handle, feeds, fetches)
c:\users\MrUserMan\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\client\session.py
in _do_call(self, fn, *args) 1346 pass 1347
message = error_interpolation.interpolate(message, self._graph)
-> 1348 raise type(e)(node_def, op, message) 1349 1350 def _extend_graph(self):
InvalidArgumentError: ValueError: could not broadcast input array from
shape (28,28,28) into shape (28,28,1) Traceback (most recent call
last):
Вот мой код:
import numpy as np
from keras import backend
import tensorflow as tf
from keras.callbacks import ModelCheckpoint
from matplotlib import gridspec
from cleverhans.attacks import FastGradientMethod
from cleverhans.attacks import BasicIterativeMethod
from cleverhans.attacks import SaliencyMapMethod
from cleverhans.attacks import DeepFool
from cleverhans.utils_keras import KerasModelWrapper
from matplotlib import pyplot as plt
from sklearn.metrics import confusion_matrix, classification_report
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils
sess = backend.get_session()
x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
y = tf.placeholder(tf.float32, shape=(None, 10))
(X_train, y_train), (X_test, y_test) = mnist.load_data()
x_train = X_train.astype('float32') / 255.
x_test = X_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
X_train = x_train
X_test = x_test
# one hot encode outputs
y_train_cat = np_utils.to_categorical(y_train)
y_test_cat = np_utils.to_categorical(y_test)
num_classes = y_test_cat.shape[1]
input_img = Input(shape=(784,)) # 784
encoded = Dense(784, activation='relu')(input_img)
encoded = Dense(504, activation='relu')(encoded)
encoded = Dense(140, activation='relu')(encoded)
decoded = Dense(140, activation='relu')(encoded)
decoded = Dense(504, activation='softplus')(decoded)
decoded = Dense(784, activation='sigmoid')(decoded
autoencoder = Model(input_img, decoded)
autoencoder.summary()
autoencoder.compile(optimizer='adam', loss='categorical_crossentropy')
history = autoencoder.fit(X_train, X_train, epochs=35, batch_size=1024, validation_data=(X_test, X_test), verbose=1)
decoded_imgs = (autoencoder.predict(X_train))
decoded_imgs_test = (autoencoder.predict(X_test))
decoded_int = np.round(decoded_imgs*255).astype(int)
decoded_int_test = np.round(decoded_imgs_test*255).astype(int)
X_train = decoded_int
X_test = decoded_int_test
#Making Input eligible for DeepFool
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
model = Sequential()
model.add(Conv2D(32, (5, 5), input_shape=(28,28,1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(num_classes, activation='sigmoid'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
history = model.fit(X_train, y_train_cat, epochs=35, batch_size=1024, verbose=1,
validation_split=0.7)
deepfool = DeepFool(model, sess=sess)
adv_x = deepfool.generate_np(X_test, overshoot=0.02, max_iter=50, nb_candidate=2)