def get_grads(model_options, x_vals, label):
x_vals = x_vals.reshape((-1, 28 ** 2))
params, x, y, layers = build_model(model_options)
softmax_layer = layers[-1]
softmax_label = softmax_layer[:, label]
grads_input = tf.gradients(softmax_label, x)[0]
with tf.Session():
tf.initialize_all_variables().run()
res = grads_input.eval(feed_dict={x: x_vals})
# print(softmax_label.eval(feed_dict={
# x: x_vals
# }))
return res
def get_grads(model_options, x_vals, label):
x_vals = x_vals.reshape((-1, 28**2))
params, x, y, layers = build_model(model_options)
softmax_layer = layers[-1]
softmax_label = softmax_layer[:, label]
grads_input = tf.gradients(softmax_label, x)[0]
with tf.Session():
tf.initialize_all_variables().run()
res = grads_input.eval(feed_dict={x: x_vals})
# print(softmax_label.eval(feed_dict={
# x: x_vals
# }))
return res
def get_faulty_input_layer(data, model_options):
save_freq = model_options.get('save_freq', 20)
label = model_options['label']
num_features = model_options['num_features']
min_num_steps = model_options.get('min_num_steps', 0)
max_num_steps = model_options.get('max_num_steps', np.inf)
tolerance = model_options.get('tolerance', 0)
prob_cap_mean = model_options.get('prob_cap_mean', 1)
prob_cap_min = model_options.get('prob_cap_min', 1)
num_mono_dec_saves = model_options.get('num_mono_dec_saves', np.inf)
batch_size = model_options.get('num_examples', 1)
init_w_train_vals = model_options.get('init_w_train_vals', False)
x_vals = None
if init_w_train_vals:
train_examples = get_data_label(data, label)
x_vals = np.random.permutation(train_examples)
x_vals = np.array_split(x_vals, 8)
x_vals = np.array([np.average(xv, axis=0) for xv in x_vals])
# Note: convolution + activation + pooling is considered 1 layer
params, x, y, layers = build_model(model_options, const_params=True, x_values=x_vals)
params_pf, x_pf, y_pf, layers_pf = build_model(model_options)
softmax_layer_pf = layers_pf[-1]
if not (max_num_steps < np.inf or (tolerance > 0 or prob_cap_mean < 1 or prob_cap_min < 1) and min_num_steps < np.inf):
logger.warn('no exit conditions for backworking CNN inputs')
eval_cnn_wrapper = functools.partial(eval_cnn, x_pf, softmax_layer_pf, num_features)
l = []
with tf.Session() as sess:
logger.info('building model')
costs = build_cost(data, x, layers, sess, model_options)
cost = sum(costs)
optimize_input_layer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(cost)
logger.info('starting backworking')
tf.initialize_all_variables().run()
t_start = time.time()
cost_val_old = np.inf
i = 0
probs_correct_tracker = ProbTracker(num_mono_dec_saves, batch_size)
try:
while True:
if i >= max_num_steps:
break
t0 = time.time()
optimize_input_layer.run()
cost_val_new = cost.eval()
logger.info('%f seconds in optimization cycle %i with cost %f', time.time() - t0, i, cost_val_new)
for idx, c in enumerate(costs):
layer = layers[idx // 2]
logger.debug('layer %s cost %f', layer.name, c.eval())
if (save_freq and i % save_freq == 0) or i == max_num_steps - 1:
input_vals, softmax = eval_cnn_wrapper(x)
probs_correct = softmax[:, label]
probs_correct_tracker(probs_correct)
l.append((input_vals, softmax))
prob_correct_mean, prob_correct_min = probs_correct.mean(), probs_correct.min()
logger.info('%f / %f min / mean probability of correct label', prob_correct_min, prob_correct_mean)
if i >= min_num_steps and (prob_correct_mean > prob_cap_mean or prob_correct_min > prob_cap_min):
break
m = probs_correct_tracker.get_mask()
if m.any():
logger.info('reassignment mask %s', m)
logger.debug('probability history of %i steps: %s',
num_mono_dec_saves, probs_correct_tracker.values[:, m])
shape = [m.sum()] + [model_options['image_dim_size']] * 2
input_vals[m] = np.random.rand(*shape)
probs_correct_tracker.mark_replaced_inputs(m)
sess.run(x.assign(input_vals))
if abs(cost_val_new - cost_val_old) < tolerance and i >= min_num_steps:
break
i += 1
cost_val_old = cost_val_new
except KeyboardInterrupt:
pass
logger.info('Average of %f seconds/optimization cycle over %f optimization cycles', (time.time() - t_start) / i, i)
return l
def get_faulty_input_layer(data, model_options):
save_freq = model_options.get('save_freq', 20)
label = model_options['label']
num_features = model_options['num_features']
min_num_steps = model_options.get('min_num_steps', 0)
max_num_steps = model_options.get('max_num_steps', np.inf)
tolerance = model_options.get('tolerance', 0)
prob_cap_mean = model_options.get('prob_cap_mean', 1)
prob_cap_min = model_options.get('prob_cap_min', 1)
num_mono_dec_saves = model_options.get('num_mono_dec_saves', np.inf)
batch_size = model_options.get('num_examples', 1)
init_w_train_vals = model_options.get('init_w_train_vals', False)
x_vals = None
if init_w_train_vals:
train_examples = get_data_label(data, label)
x_vals = np.random.permutation(train_examples)
x_vals = np.array_split(x_vals, 8)
x_vals = np.array([np.average(xv, axis=0) for xv in x_vals])
# Note: convolution + activation + pooling is considered 1 layer
params, x, y, layers = build_model(model_options,
const_params=True,
x_values=x_vals)
params_pf, x_pf, y_pf, layers_pf = build_model(model_options)
softmax_layer_pf = layers_pf[-1]
if not (max_num_steps < np.inf or
(tolerance > 0 or prob_cap_mean < 1 or prob_cap_min < 1)
and min_num_steps < np.inf):
logger.warn('no exit conditions for backworking CNN inputs')
eval_cnn_wrapper = functools.partial(eval_cnn, x_pf, softmax_layer_pf,
num_features)
l = []
with tf.Session() as sess:
logger.info('building model')
costs = build_cost(data, x, layers, sess, model_options)
cost = sum(costs)
optimize_input_layer = tf.train.AdamOptimizer(
learning_rate=0.01).minimize(cost)
logger.info('starting backworking')
tf.initialize_all_variables().run()
t_start = time.time()
cost_val_old = np.inf
i = 0
probs_correct_tracker = ProbTracker(num_mono_dec_saves, batch_size)
try:
while True:
if i >= max_num_steps:
break
t0 = time.time()
optimize_input_layer.run()
cost_val_new = cost.eval()
logger.info('%f seconds in optimization cycle %i with cost %f',
time.time() - t0, i, cost_val_new)
for idx, c in enumerate(costs):
layer = layers[idx // 2]
logger.debug('layer %s cost %f', layer.name, c.eval())
if (save_freq
and i % save_freq == 0) or i == max_num_steps - 1:
input_vals, softmax = eval_cnn_wrapper(x)
probs_correct = softmax[:, label]
probs_correct_tracker(probs_correct)
l.append((input_vals, softmax))
prob_correct_mean, prob_correct_min = probs_correct.mean(
), probs_correct.min()
logger.info(
'%f / %f min / mean probability of correct label',
prob_correct_min, prob_correct_mean)
if i >= min_num_steps and (
prob_correct_mean > prob_cap_mean
or prob_correct_min > prob_cap_min):
break
m = probs_correct_tracker.get_mask()
if m.any():
logger.info('reassignment mask %s', m)
logger.debug('probability history of %i steps: %s',
num_mono_dec_saves,
probs_correct_tracker.values[:, m])
shape = [m.sum()
] + [model_options['image_dim_size']] * 2
input_vals[m] = np.random.rand(*shape)
probs_correct_tracker.mark_replaced_inputs(m)
sess.run(x.assign(input_vals))
if abs(cost_val_new -
cost_val_old) < tolerance and i >= min_num_steps:
break
i += 1
cost_val_old = cost_val_new
except KeyboardInterrupt:
pass
logger.info(
'Average of %f seconds/optimization cycle over %f optimization cycles',
(time.time() - t_start) / i, i)
return l