Python tensorflow.Session() Examples

The following are 30 code examples of tensorflow.Session(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow , or try the search function .
Example #1
Source Project: Traffic_sign_detection_YOLO   Author: AmeyaWagh   File: build.py    License: MIT License 6 votes vote down vote up
def savepb(self):
		"""
		Create a standalone const graph def that 
		C++	can load and run.
		"""
		darknet_pb = self.to_darknet()
		flags_pb = self.FLAGS
		flags_pb.verbalise = False
		
		flags_pb.train = False
		# rebuild another tfnet. all const.
		tfnet_pb = TFNet(flags_pb, darknet_pb)		
		tfnet_pb.sess = tf.Session(graph = tfnet_pb.graph)
		# tfnet_pb.predict() # uncomment for unit testing
		name = 'built_graph/{}.pb'.format(self.meta['name'])
		os.makedirs(os.path.dirname(name), exist_ok=True)
		#Save dump of everything in meta
		with open('built_graph/{}.meta'.format(self.meta['name']), 'w') as fp:
			json.dump(self.meta, fp)
		self.say('Saving const graph def to {}'.format(name))
		graph_def = tfnet_pb.sess.graph_def
		tf.train.write_graph(graph_def,'./', name, False) 
Example #2
Source Project: disentangling_conditional_gans   Author: zalandoresearch   File: tfutil.py    License: MIT License 6 votes vote down vote up
def create_session(config_dict=dict(), force_as_default=False):
    config = tf.ConfigProto()
    for key, value in config_dict.items():
        fields = key.split('.')
        obj = config
        for field in fields[:-1]:
            obj = getattr(obj, field)
        setattr(obj, fields[-1], value)
    session = tf.Session(config=config)
    if force_as_default:
        session._default_session = session.as_default()
        session._default_session.enforce_nesting = False
        session._default_session.__enter__()
    return session

#----------------------------------------------------------------------------
# Initialize all tf.Variables that have not already been initialized.
# Equivalent to the following, but more efficient and does not bloat the tf graph:
#   tf.variables_initializer(tf.report_unitialized_variables()).run() 
Example #3
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_defenses.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def test_feature_pairing(self):
        fgsm = FastGradientMethod(self.model)
        attack = lambda x: fgsm.generate(x)
        loss = FeaturePairing(self.model, weight=0.1, attack=attack)
        l = loss.fprop(self.x, self.y)
        with tf.Session() as sess:
            vl1 = sess.run(l, feed_dict={self.x: self.vx, self.y: self.vy})
            vl2 = sess.run(l, feed_dict={self.x: self.vx, self.y: self.vy})
        self.assertClose(vl1, sum([4.296023369, 2.963884830]) / 2., atol=1e-6)
        self.assertClose(vl2, sum([4.296023369, 2.963884830]) / 2., atol=1e-6)

        loss = FeaturePairing(self.model, weight=10., attack=attack)
        l = loss.fprop(self.x, self.y)
        with tf.Session() as sess:
            vl1 = sess.run(l, feed_dict={self.x: self.vx, self.y: self.vy})
            vl2 = sess.run(l, feed_dict={self.x: self.vx, self.y: self.vy})
        self.assertClose(vl1, sum([4.333082676, 3.00094414]) / 2., atol=1e-6)
        self.assertClose(vl2, sum([4.333082676, 3.00094414]) / 2., atol=1e-6) 
Example #4
Source Project: neural-fingerprinting   Author: StephanZheng   File: enjoy-adv.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def __init__(self, env, dueling, noisy, fname):
        self.g = tf.Graph()
        self.noisy = noisy
        self.dueling = dueling
        self.env = env
        with self.g.as_default():
            self.act = deepq.build_act_enjoy(
                make_obs_ph=lambda name: U.Uint8Input(
                    env.observation_space.shape, name=name),
                q_func=dueling_model if dueling else model,
                num_actions=env.action_space.n,
                noisy=noisy
            )
            self.saver = tf.train.Saver()
        self.sess = tf.Session(graph=self.g)

        if fname is not None:
            print('Loading Model...')
            self.saver.restore(self.sess, fname) 
Example #5
Source Project: neural-fingerprinting   Author: StephanZheng   File: utils.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def cleverhans_attack_wrapper(cleverhans_attack_fn, reset=True):
    def attack(a):
        session = tf.Session()
        with session.as_default():
            model = RVBCleverhansModel(a)
            adversarial_image = cleverhans_attack_fn(model, session, a)
            adversarial_image = np.squeeze(adversarial_image, axis=0)
            if reset:
                # optionally, reset to ignore other adversarials
                # found during the search
                a._reset()
            # run predictions to make sure the returned adversarial
            # is taken into account
            min_, max_ = a.bounds()
            adversarial_image = np.clip(adversarial_image, min_, max_)
            a.predictions(adversarial_image)
    return attack 
Example #6
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_runner.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def setUp(self):
        super(TestRunnerMultiGPU, self).setUp()
        self.sess = tf.Session()

        inputs = []
        outputs = []
        self.niter = 10
        niter = self.niter
        # A Simple graph with `niter` sub-graphs.
        with tf.variable_scope(None, 'runner'):
            for i in range(niter):
                v = tf.get_variable('v%d' % i, shape=(100, 10))
                w = tf.get_variable('w%d' % i, shape=(100, 1))

                inputs += [{'v': v, 'w': w}]
                outputs += [{'v': v, 'w': w}]

        self.runner = RunnerMultiGPU(inputs, outputs, sess=self.sess) 
Example #7
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_dropout.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def test_drop():
    # Make sure dropout is activated successfully

    # We would like to configure the test to deterministically drop,
    # so that the test does not need to use multiple runs.
    # However, tf.nn.dropout divides by include_prob, so zero or
    # infinitesimal include_prob causes NaNs.
    # 1e-8 does not cause NaNs and shouldn't be a significant source
    # of test flakiness relative to dependency downloads failing, etc.
    model = MLP(input_shape=[1, 1], layers=[Dropout(name='output',
                                                    include_prob=1e-8)])
    x = tf.constant([[1]], dtype=tf.float32)
    y = model.get_layer(x, 'output', dropout=True)
    sess = tf.Session()
    y_value = sess.run(y)
    # Subject to very rare random failure because include_prob is not exact 0
    assert y_value == 0., y_value 
Example #8
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_dropout.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def test_override():
    # Make sure dropout_dict changes dropout probabilities successful

    # We would like to configure the test to deterministically drop,
    # so that the test does not need to use multiple runs.
    # However, tf.nn.dropout divides by include_prob, so zero or
    # infinitesimal include_prob causes NaNs.
    # For this test, random failure to drop will not cause the test to fail.
    # The stochastic version should not even run if everything is working
    # right.
    model = MLP(input_shape=[1, 1], layers=[Dropout(name='output',
                                                    include_prob=1e-8)])
    x = tf.constant([[1]], dtype=tf.float32)
    dropout_dict = {'output': 1.}
    y = model.get_layer(x, 'output', dropout=True, dropout_dict=dropout_dict)
    sess = tf.Session()
    y_value = sess.run(y)
    assert y_value == 1., y_value 
Example #9
Source Project: spleeter   Author: deezer   File: test_separator.py    License: MIT License 6 votes vote down vote up
def test_separate(test_file, configuration, backend):
    """ Test separation from raw data. """
    with tf.Session() as sess:
        instruments = MODEL_TO_INST[configuration]
        adapter = get_default_audio_adapter()
        waveform, _ = adapter.load(test_file)
        separator = Separator(configuration, stft_backend=backend)
        prediction = separator.separate(waveform, test_file)
        assert len(prediction) == len(instruments)
        for instrument in instruments:
            assert instrument in prediction
        for instrument in instruments:
            track = prediction[instrument]
            assert waveform.shape[:-1] == track.shape[:-1]
            assert not np.allclose(waveform, track)
            for compared in instruments:
                if instrument != compared:
                    assert not np.allclose(track, prediction[compared]) 
Example #10
Source Project: deep-learning-note   Author: wdxtub   File: 16_basic_kernels.py    License: MIT License 6 votes vote down vote up
def main():
    rgb = False
    if rgb:
        kernels_list = [kernels.BLUR_FILTER_RGB,
                        kernels.SHARPEN_FILTER_RGB,
                        kernels.EDGE_FILTER_RGB,
                        kernels.TOP_SOBEL_RGB,
                        kernels.EMBOSS_FILTER_RGB]
    else:
        kernels_list = [kernels.BLUR_FILTER,
                        kernels.SHARPEN_FILTER,
                        kernels.EDGE_FILTER,
                        kernels.TOP_SOBEL,
                        kernels.EMBOSS_FILTER]

    kernels_list = kernels_list[1:]
    image = read_one_image('data/images/naruto.jpeg')
    if not rgb:
        image = tf.image.rgb_to_grayscale(image)
    image = tf.expand_dims(image, 0) # make it into a batch of 1 element
    images = convolve(image, kernels_list, rgb)
    with tf.Session() as sess:
        images = sess.run(images) # convert images from tensors to float values
    show_images(images, rgb) 
Example #11
Source Project: Black-Box-Audio   Author: rtaori   File: run_audio_attack.py    License: MIT License 5 votes vote down vote up
def setup_graph(self, input_audio_batch, target_phrase): 
        batch_size = input_audio_batch.shape[0]
        weird = (input_audio_batch.shape[1] - 1) // 320 
        logits_arg2 = np.tile(weird, batch_size)
        dense_arg1 = np.array(np.tile(target_phrase, (batch_size, 1)), dtype=np.int32)
        dense_arg2 = np.array(np.tile(target_phrase.shape[0], batch_size), dtype=np.int32)
        
        pass_in = np.clip(input_audio_batch, -2**15, 2**15-1)
        seq_len = np.tile(weird, batch_size).astype(np.int32)
        
        with tf.variable_scope('', reuse=tf.AUTO_REUSE):
            
            inputs = tf.placeholder(tf.float32, shape=pass_in.shape, name='a')
            len_batch = tf.placeholder(tf.float32, name='b')
            arg2_logits = tf.placeholder(tf.int32, shape=logits_arg2.shape, name='c')
            arg1_dense = tf.placeholder(tf.float32, shape=dense_arg1.shape, name='d')
            arg2_dense = tf.placeholder(tf.int32, shape=dense_arg2.shape, name='e')
            len_seq = tf.placeholder(tf.int32, shape=seq_len.shape, name='f')
            
            logits = get_logits(inputs, arg2_logits)
            target = ctc_label_dense_to_sparse(arg1_dense, arg2_dense, len_batch)
            ctcloss = tf.nn.ctc_loss(labels=tf.cast(target, tf.int32), inputs=logits, sequence_length=len_seq)
            decoded, _ = tf.nn.ctc_greedy_decoder(logits, arg2_logits, merge_repeated=True)
            
            sess = tf.Session()
            saver = tf.train.Saver(tf.global_variables())
            saver.restore(sess, "models/session_dump")
            
        func1 = lambda a, b, c, d, e, f: sess.run(ctcloss, 
            feed_dict={inputs: a, len_batch: b, arg2_logits: c, arg1_dense: d, arg2_dense: e, len_seq: f})
        func2 = lambda a, b, c, d, e, f: sess.run([ctcloss, decoded], 
            feed_dict={inputs: a, len_batch: b, arg2_logits: c, arg1_dense: d, arg2_dense: e, len_seq: f})
        return (func1, func2) 
Example #12
Source Project: convseg   Author: chqiwang   File: server.py    License: MIT License 5 votes vote down vote up
def make_app(model_dir):
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    config.gpu_options.per_process_gpu_memory_fraction = 1.0
    config.allow_soft_placement = True
    config.log_device_placement = True
    sess = tf.Session(config=config)
    tagger = Tagger(sess=sess, model_dir=model_dir, scope=TASK.scope, batch_size=200)
    return tornado.web.Application([
        (r"/", MainHandler),
        (r"/%s" % TASK.scope, TaskHandler, {'tagger': tagger})
    ]) 
Example #13
Source Project: Traffic_sign_detection_YOLO   Author: AmeyaWagh   File: loader.py    License: MIT License 5 votes vote down vote up
def load(self, ckpt, ignore):
        meta = ckpt + '.meta'
        with tf.Graph().as_default() as graph:
            with tf.Session().as_default() as sess:
                saver = tf.train.import_meta_graph(meta)
                saver.restore(sess, ckpt)
                for var in tf.global_variables():
                    name = var.name.split(':')[0]
                    packet = [name, var.get_shape().as_list()]
                    self.src_key += [packet]
                    self.vals += [var.eval(sess)] 
Example #14
Source Project: Traffic_sign_detection_YOLO   Author: AmeyaWagh   File: build.py    License: MIT License 5 votes vote down vote up
def setup_meta_ops(self):
		cfg = dict({
			'allow_soft_placement': False,
			'log_device_placement': False
		})

		utility = min(self.FLAGS.gpu, 1.)
		if utility > 0.0:
			self.say('GPU mode with {} usage'.format(utility))
			cfg['gpu_options'] = tf.GPUOptions(
				per_process_gpu_memory_fraction = utility)
			cfg['allow_soft_placement'] = True
		else: 
			self.say('Running entirely on CPU')
			cfg['device_count'] = {'GPU': 0}

		if self.FLAGS.train: self.build_train_op()
		
		if self.FLAGS.summary:
			self.summary_op = tf.summary.merge_all()
			self.writer = tf.summary.FileWriter(self.FLAGS.summary + 'train')
		
		self.sess = tf.Session(config = tf.ConfigProto(**cfg))
		self.sess.run(tf.global_variables_initializer())

		if not self.ntrain: return
		self.saver = tf.train.Saver(tf.global_variables(), 
			max_to_keep = self.FLAGS.keep)
		if self.FLAGS.load != 0: self.load_from_ckpt()
		
		if self.FLAGS.summary:
			self.writer.add_graph(self.sess.graph) 
Example #15
Source Project: Adversarial-Face-Attack   Author: ppwwyyxx   File: face_attack.py    License: GNU General Public License v3.0 5 votes vote down vote up
def __init__(self):
        self.graph = tf.Graph()
        with self.graph.as_default():
            gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
            sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
            with sess.as_default():
                self.pnet, self.rnet, self.onet = FaceDet.create_mtcnn(sess, None) 
Example #16
Source Project: jiji-with-tensorflow-example   Author: unageanu   File: model.py    License: MIT License 5 votes vote down vote up
def __enter__(self):
        self.session = tf.Session()
        return self 
Example #17
Source Project: neural-fingerprinting   Author: StephanZheng   File: gen_noisy.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def evaluate_checkpoint(sess,model):
    dataset = 'cifar'

    #with tf.Session() as sess:
    # Iterate over the samples batch-by-batch
    num_batches = int(math.ceil(num_eval_examples / eval_batch_size))
    adv_x_samples=[]
    adv_y_samples=[]
    for ibatch in range(num_batches):
      bstart = ibatch * eval_batch_size
      bend = min(bstart + eval_batch_size, num_eval_examples)

      x_batch = mnist.test.images[bstart:bend,:]
      y_batch = mnist.test.labels[bstart:bend]

      x_batch_adv = attack.perturb(x_batch, y_batch, sess)
      if(ibatch == 0):
          adv_x_samples = x_batch_adv
          adv_y_samples = y_batch
      else:
          adv_x_samples = np.concatenate((adv_x_samples, x_batch_adv), axis = 0)
          adv_y_samples = np.concatenate((adv_y_samples, y_batch), axis = 0)
    if(args.attack == 'xent'):
      atck = 'pgd'
      f = open(os.path.join(args.log_dir, 'Adv_%s_%s.p' % (dataset, atck)), "w")
    elif(args.attack == 'cw_pgd'):
      atck = 'cw_pgd'
      f = open(os.path.join(args.log_dir, 'Adv_%s_%s.p' % (dataset, atck)), "w")
    else:
      f = open(os.path.join(args.log_dir, "custom.p"), "w")
    pickle.dump({"adv_input":adv_x_samples,"adv_labels":adv_y_samples},f)
    f.close() 
Example #18
Source Project: neural-fingerprinting   Author: StephanZheng   File: gen_whitebox_adv.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def evaluate_checkpoint(sess,model):
    dataset = 'cifar'

    #with tf.Session() as sess:
    # Iterate over the samples batch-by-batch
    num_batches = int(math.ceil(num_eval_examples / eval_batch_size))
    adv_x_samples=[]
    adv_y_samples=[]
    for ibatch in range(num_batches):
      bstart = ibatch * eval_batch_size
      bend = min(bstart + eval_batch_size, num_eval_examples)

      x_batch = mnist.test.images[bstart:bend,:]
      y_batch = mnist.test.labels[bstart:bend]

      x_batch_adv = attack.perturb(x_batch, y_batch, sess)
      if(ibatch == 0):
          adv_x_samples = x_batch_adv
          adv_y_samples = y_batch
      else:
          adv_x_samples = np.concatenate((adv_x_samples, x_batch_adv), axis = 0)
          adv_y_samples = np.concatenate((adv_y_samples, y_batch), axis = 0)
    if(args.attack == 'xent'):
      atck = 'pgd'
      f = open(os.path.join(args.log_dir, 'Adv_%s_%s.p' % (dataset, atck)), "w")
    elif(args.attack == 'cw_pgd'):
      atck = 'cw_pgd'
      f = open(os.path.join(args.log_dir, 'Adv_%s_%s.p' % (dataset, atck)), "w")
    else:
      f = open(os.path.join(args.log_dir, "custom.p"), "w")
    pickle.dump({"adv_input":adv_x_samples,"adv_labels":adv_y_samples},f)
    f.close() 
Example #19
Source Project: neural-fingerprinting   Author: StephanZheng   File: gen_whitebox_adv.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def evaluate_checkpoint(sess,model):
    dataset = 'mnist'

    #with tf.Session() as sess:
    # Iterate over the samples batch-by-batch
    num_batches = int(math.ceil(num_eval_examples / eval_batch_size))
    adv_x_samples=[]
    adv_y_samples=[]
    for ibatch in range(num_batches):
      bstart = ibatch * eval_batch_size
      bend = min(bstart + eval_batch_size, num_eval_examples)

      x_batch = mnist.test.images[bstart:bend,:]
      y_batch = mnist.test.labels[bstart:bend]

      dict_nat = {model.x_input: x_batch,
                  model.y_input: y_batch}

      x_batch_adv = attack.perturb(x_batch, y_batch, sess)
      if(ibatch == 0):
          adv_x_samples = x_batch_adv
          adv_y_samples = y_batch
      else:
          adv_x_samples = np.concatenate((adv_x_samples, x_batch_adv), axis = 0)
          adv_y_samples = np.concatenate((adv_y_samples, y_batch), axis = 0)
    if(args.attack == 'xent'):
      atck = 'pgd'
      f = open(os.path.join(args.log_dir, 'Adv_%s_%s.p' % (dataset, atck)), "w")
    elif(args.attack == 'cw_pgd'):
      atck = 'cw_pgd'
      f = open(os.path.join(args.log_dir, 'Adv_%s_%s.p' % (dataset, atck)), "w")
    else:
      f = open(os.path.join(args.log_dir, "custom.p"), "w")
    pickle.dump({"adv_input":adv_x_samples,"adv_labels":adv_y_samples},f)
    f.close() 
Example #20
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_defenses.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def test_xe(self):
        loss = CrossEntropy(self.model, smoothing=0.)
        l = loss.fprop(self.x, self.y)
        with tf.Session() as sess:
            vl1 = sess.run(l, feed_dict={self.x: self.vx, self.y: self.vy})
            vl2 = sess.run(l, feed_dict={self.x: self.vx, self.y: self.vy})
        self.assertClose(vl1, sum([2.210599660, 1.53666997]) / 2., atol=1e-6)
        self.assertClose(vl2, sum([2.210599660, 1.53666997]) / 2., atol=1e-6) 
Example #21
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_defenses.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def test_xe_smoothing(self):
        loss = CrossEntropy(self.model, smoothing=0.1)
        l = loss.fprop(self.x, self.y)
        with tf.Session() as sess:
            vl1 = sess.run(l, feed_dict={self.x: self.vx, self.y: self.vy})
            vl2 = sess.run(l, feed_dict={self.x: self.vx, self.y: self.vy})
        self.assertClose(vl1, sum([2.10587597, 1.47194624]) / 2., atol=1e-6)
        self.assertClose(vl2, sum([2.10587597, 1.47194624]) / 2., atol=1e-6) 
Example #22
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_utils_keras.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def setUp(self):
        from keras.models import Sequential
        from keras.layers import Dense, Activation
        import tensorflow as tf

        def dummy_model():
            input_shape = (100,)
            return Sequential([Dense(20, name='l1',
                                     input_shape=input_shape),
                               Dense(10, name='l2'),
                               Activation('softmax', name='softmax')])

        self.sess = tf.Session()
        self.sess.as_default()
        self.model = dummy_model() 
Example #23
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_attacks.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def test_model(self):
        sess = tf.Session()

        # Exception is thrown when model does not have __call__ attribute
        with self.assertRaises(Exception) as context:
            model = tf.placeholder(tf.float32, shape=(None, 10))
            Attack(model, back='tf', sess=sess)
        self.assertTrue(context.exception) 
Example #24
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_attacks.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def test_parse(self):
        sess = tf.Session()

        test_attack = Attack(Model('model', 10, {}), back='tf', sess=sess)
        self.assertTrue(test_attack.parse_params({})) 
Example #25
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_attacks.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def setUp(self):
        super(TestVirtualAdversarialMethod, self).setUp()

        self.sess = tf.Session()
        self.sess.as_default()
        self.model = DummyModel()
        self.attack = VirtualAdversarialMethod(self.model, sess=self.sess)

        # initialize model
        with tf.name_scope('dummy_model'):
            self.model(tf.placeholder(tf.float32, shape=(None, 1000)))
        self.sess.run(tf.global_variables_initializer()) 
Example #26
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_attacks.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def setUp(self):
        super(TestSPSA, self).setUp()

        self.sess = tf.Session()
        self.model = SimpleModel()
        self.attack = SPSA(self.model, sess=self.sess) 
Example #27
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_attacks.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def setUp(self):
        super(TestBasicIterativeMethod, self).setUp()

        self.sess = tf.Session()
        self.model = SimpleModel()
        self.attack = BasicIterativeMethod(self.model, sess=self.sess) 
Example #28
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_attacks.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def setUp(self):
        super(TestMomentumIterativeMethod, self).setUp()

        self.sess = tf.Session()
        self.model = SimpleModel()
        self.attack = MomentumIterativeMethod(self.model, sess=self.sess) 
Example #29
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_attacks.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def setUp(self):
        super(TestCarliniWagnerL2, self).setUp()

        self.sess = tf.Session()
        self.model = SimpleModel()
        self.attack = CarliniWagnerL2(self.model, sess=self.sess) 
Example #30
Source Project: neural-fingerprinting   Author: StephanZheng   File: test_attacks.py    License: BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up
def setUp(self):
        super(TestElasticNetMethod, self).setUp()

        self.sess = tf.Session()
        self.model = SimpleModel()
        self.attack = ElasticNetMethod(self.model, sess=self.sess)