def test_tensorflow_model(num_classes):
bounds = (0, 255)
channels = num_classes
def mean_brightness_net(images):
logits = tf.reduce_mean(images, axis=(1, 2))
return logits
g = tf.Graph()
with g.as_default():
images = tf.placeholder(tf.float32, (None, 5, 5, channels))
logits = mean_brightness_net(images)
with tf.Session(graph=g):
model = TensorFlowModel(
images,
logits,
bounds=bounds)
assert model.session is not None
test_images = np.random.rand(2, 5, 5, channels).astype(np.float32)
test_label = 7
assert model.batch_predictions(test_images).shape \
== (2, num_classes)
test_logits = model.predictions(test_images[0])
assert test_logits.shape == (num_classes,)
test_gradient = model.gradient(test_images[0], test_label)
assert test_gradient.shape == test_images[0].shape
np.testing.assert_almost_equal(
model.predictions_and_gradient(test_images[0], test_label)[0],
test_logits)
np.testing.assert_almost_equal(
model.predictions_and_gradient(test_images[0], test_label)[1],
test_gradient)
assert model.num_classes() == num_classes
graph, saver, images, logits = adv_model_resnet()
sess = tf.Session(graph=graph)
#sess.run(tf.global_variables_initializer())
#model.tf_load(sess, "./resnet18/checkpoints/model/")
path = os.path.join('resnet18', 'checkpoints', 'model')
saver.restore(sess, tf.train.latest_checkpoint(path))
data = dataset('../Defense_Model/tiny-imagenet-200/', normalize=False)
batch_size = 256
x_test, y_test = data.next_test_batch(batch_size)
with sess.as_default():
model = TensorFlowModel(images, logits, bounds=(0, 255))
y_logits = model.batch_predictions(x_test)
#y_prob=np.softmax(y_logits)
y_pred = np.argmax(y_logits, axis=1)
#print(y_pred)
y_label = np.argmax(y_test, axis=1)
print(np.sum(np.equal(y_pred, y_label)) / x_test.shape[0])
# In[ ]:
"""from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
# List ALL tensors example output: v0/Adam (DT_FLOAT) [3,3,1,80]
print_tensors_in_checkpoint_file(file_name=tf.train.latest_checkpoint(path), tensor_name='', all_tensors=True)"""
# In[ ]: