def main():
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument('model_path',
help='Path to the converted model parameters (.npy)')
parser.add_argument('val_gt',
help='Path to validation set ground truth (.txt)')
parser.add_argument('imagenet_data_dir',
help='ImageNet validation set images directory path')
parser.add_argument('--model',
default='GoogleNet',
help='The name of the model to evaluate')
args = parser.parse_args()
# Load the network
net = load_model(args.model)
if net is None:
exit(-1)
# Load the dataset
data_spec = models.get_data_spec(model_instance=net)
image_producer = dataset.ImageNetProducer(val_path=args.val_gt,
data_path=args.imagenet_data_dir,
data_spec=data_spec)
# Evaluate its performance on the ILSVRC12 validation set
validate(net, args.model_path, image_producer)
def validate(net, model_path, image_producer, top_k=5):
'''Compute the top_k classification accuracy for the given network and images.'''
# Get the data specifications for given network
spec = models.get_data_spec(model_instance=net)
# Get the input node for feeding in the images
input_node = net.inputs['data']
# Create a placeholder for the ground truth labels
label_node = tf.placeholder(tf.int32)
# Get the output of the network (class probabilities)
probs = net.get_output()
# Create a top_k accuracy node
top_k_op = tf.nn.in_top_k(probs, label_node, top_k)
# The number of images processed
count = 0
# The number of correctly classified images
correct = 0
# The total number of images
total = len(image_producer)
with tf.Session() as sesh:
coordinator = tf.train.Coordinator()
# Load the converted parameters
net.load(data_path=model_path, session=sesh)
# Start the image processing workers
threads = image_producer.start(session=sesh, coordinator=coordinator)
# Iterate over and classify mini-batches
for (labels, images) in image_producer.batches(sesh):
correct += np.sum(
sesh.run(top_k_op,
feed_dict={
input_node: images,
label_node: labels
}))
count += len(labels)
cur_accuracy = float(correct) * 100 / count
print('{:>6}/{:<6} {:>6.2f}%'.format(count, total, cur_accuracy))
# Stop the worker threads
coordinator.request_stop()
coordinator.join(threads, stop_grace_period_secs=2)
print('Top {} Accuracy: {}'.format(top_k, float(correct) / total))
def load_model(name):
'''Creates and returns an instance of the model given its class name.
The created model has a single placeholder node for feeding images.
'''
# Find the model class from its name
all_models = models.get_models()
lut = {model.__name__: model for model in all_models}
if name not in lut:
print('Invalid model index. Options are:')
# Display a list of valid model names
for model in all_models:
print('\t* {}'.format(model.__name__))
return None
NetClass = lut[name]
# Create a placeholder for the input image
spec = models.get_data_spec(model_class=NetClass)
data_node = tf.placeholder(tf.float32,
shape=(None, spec.crop_size, spec.crop_size,
spec.channels))
# Construct and return the model
return NetClass({'data': data_node})
def resnet50_noisy(input_node, netparams, err_mean, err_stddev, train_vars):
weights_noisy, biases_noisy, err_w, err_b = helper.add_noise(netparams['weights'], netparams['biases'], err_mean, err_stddev, train_vars)
mean, variance, scale, offset = netparams['mean'], netparams['variance'], netparams['scale'], netparams['offset']
err_lyr = {}
layers_err = {}
data_spec = helper.get_data_spec('resnet50')
err_lyr['input'] = tf.get_variable(name='input_lyr_err', shape=(1, data_spec.crop_size, data_spec.crop_size, data_spec.channels), initializer=tf.random_normal_initializer(mean=err_mean[0], stddev=err_stddev[0]), trainable=train_vars[0])
input_node_noisy = tf.add(input_node, err_lyr['input'])
conv1 = conv(input_node_noisy, weights_noisy['conv1'], biases_noisy['conv1'], 2, 2, relu=False)
err_lyr['conv1'] = tf.get_variable(name='conv1_lyr_err', shape=conv1.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['conv1'] = tf.add(conv1, err_lyr['conv1'])
bn_conv1 = batch_normalization(layers_err['conv1'], scale['bn_conv1'], offset['bn_conv1'], mean['bn_conv1'], variance['bn_conv1'], relu=True)
pool1 = max_pool(bn_conv1, 3, 3, 2, 2)
res2a_branch1 = conv(pool1, weights_noisy['res2a_branch1'], biases_noisy['res2a_branch1'], 1, 1, biased=False, relu=False)
err_lyr['res2a_branch1'] = tf.get_variable(name='res2a_branch1_lyr_err', shape=res2a_branch1.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2a_branch1'] = tf.add(res2a_branch1, err_lyr['res2a_branch1'])
bn2a_branch1 = batch_normalization(layers_err['res2a_branch1'], scale['bn2a_branch1'], offset['bn2a_branch1'], mean['bn2a_branch1'], variance['bn2a_branch1'])
res2a_branch2a = conv(pool1, weights_noisy['res2a_branch2a'], biases_noisy['res2a_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res2a_branch2a'] = tf.get_variable(name='res2a_branch2a_lyr_err', shape=res2a_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2a_branch2a'] = tf.add(res2a_branch2a, err_lyr['res2a_branch2a'])
bn2a_branch2a = batch_normalization(layers_err['res2a_branch2a'], scale['bn2a_branch2a'], offset['bn2a_branch2a'], mean['bn2a_branch2a'], variance['bn2a_branch2a'], relu=True)
res2a_branch2b = conv(bn2a_branch2a, weights_noisy['res2a_branch2b'], biases_noisy['res2a_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res2a_branch2b'] = tf.get_variable(name='res2a_branch2b_lyr_err', shape=res2a_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2a_branch2b'] = tf.add(res2a_branch2b, err_lyr['res2a_branch2b'])
bn2a_branch2b = batch_normalization(layers_err['res2a_branch2b'], scale['bn2a_branch2b'], offset['bn2a_branch2b'], mean['bn2a_branch2b'], variance['bn2a_branch2b'], relu=True)
res2a_branch2c = conv(bn2a_branch2b, weights_noisy['res2a_branch2c'], biases_noisy['res2a_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res2a_branch2c'] = tf.get_variable(name='res2a_branch2c_lyr_err', shape=res2a_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2a_branch2c'] = tf.add(res2a_branch2c, err_lyr['res2a_branch2c'])
bn2a_branch2c = batch_normalization(layers_err['res2a_branch2c'], scale['bn2a_branch2c'], offset['bn2a_branch2c'], mean['bn2a_branch2c'], variance['bn2a_branch2c'])
res2a = add([bn2a_branch1, bn2a_branch2c])
err_lyr['res2a'] = tf.get_variable(name='res2a_lyr_err', shape=res2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2a'] = tf.add(res2a, err_lyr['res2a'])
res2a_relu = relu(layers_err['res2a'])
res2b_branch2a = conv(res2a_relu, weights_noisy['res2b_branch2a'], biases_noisy['res2b_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res2b_branch2a'] = tf.get_variable(name='res2b_branch2a_lyr_err', shape=res2b_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2b_branch2a'] = tf.add(res2b_branch2a, err_lyr['res2b_branch2a'])
bn2b_branch2a = batch_normalization(layers_err['res2b_branch2a'], scale['bn2b_branch2a'], offset['bn2b_branch2a'], mean['bn2b_branch2a'], variance['bn2b_branch2a'], relu=True)
res2b_branch2b = conv(bn2b_branch2a, weights_noisy['res2b_branch2b'], biases_noisy['res2b_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res2b_branch2b'] = tf.get_variable(name='res2b_branch2b_lyr_err', shape=res2b_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2b_branch2b'] = tf.add(res2b_branch2b, err_lyr['res2b_branch2b'])
bn2b_branch2b = batch_normalization(layers_err['res2b_branch2b'], scale['bn2b_branch2b'], offset['bn2b_branch2b'], mean['bn2b_branch2b'], variance['bn2b_branch2b'], relu=True)
res2b_branch2c = conv(bn2b_branch2b, weights_noisy['res2b_branch2c'], biases_noisy['res2b_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res2b_branch2c'] = tf.get_variable(name='res2b_branch2c_lyr_err', shape=res2b_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2b_branch2c'] = tf.add(res2b_branch2c, err_lyr['res2b_branch2c'])
bn2b_branch2c = batch_normalization(layers_err['res2b_branch2c'], scale['bn2b_branch2c'], offset['bn2b_branch2c'], mean['bn2b_branch2c'], variance['bn2b_branch2c'])
res2b = add([res2a_relu, bn2b_branch2c])
err_lyr['res2b'] = tf.get_variable(name='res2b_lyr_err', shape=res2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2b'] = tf.add(res2b, err_lyr['res2b'])
res2b_relu = relu(layers_err['res2b'])
res2c_branch2a = conv(res2b_relu, weights_noisy['res2c_branch2a'], biases_noisy['res2c_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res2c_branch2a'] = tf.get_variable(name='res2c_branch2a_lyr_err', shape=res2c_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2c_branch2a'] = tf.add(res2c_branch2a, err_lyr['res2c_branch2a'])
bn2c_branch2a = batch_normalization(layers_err['res2c_branch2a'], scale['bn2c_branch2a'], offset['bn2c_branch2a'], mean['bn2c_branch2a'], variance['bn2c_branch2a'], relu=True)
res2c_branch2b = conv(bn2c_branch2a, weights_noisy['res2c_branch2b'], biases_noisy['res2c_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res2c_branch2b'] = tf.get_variable(name='res2c_branch2b_lyr_err', shape=res2c_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2c_branch2b'] = tf.add(res2c_branch2b, err_lyr['res2c_branch2b'])
bn2c_branch2b = batch_normalization(layers_err['res2c_branch2b'], scale['bn2c_branch2b'], offset['bn2c_branch2b'], mean['bn2c_branch2b'], variance['bn2c_branch2b'], relu=True)
res2c_branch2c = conv(bn2c_branch2b, weights_noisy['res2c_branch2c'], biases_noisy['res2c_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res2c_branch2c'] = tf.get_variable(name='res2c_branch2c_lyr_err', shape=res2c_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2c_branch2c'] = tf.add(res2c_branch2c, err_lyr['res2c_branch2c'])
bn2c_branch2c = batch_normalization(layers_err['res2c_branch2c'], scale['bn2c_branch2c'], offset['bn2c_branch2c'], mean['bn2c_branch2c'], variance['bn2c_branch2c'])
res2c = add([res2b_relu, bn2c_branch2c])
err_lyr['res2c'] = tf.get_variable(name='res2c_lyr_err', shape=res2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res2c'] = tf.add(res2c, err_lyr['res2c'])
res2c_relu = relu(layers_err['res2c'])
res3a_branch1 = conv(res2c_relu, weights_noisy['res3a_branch1'], biases_noisy['res3a_branch1'], 2, 2, biased=False, relu=False)
err_lyr['res3a_branch1'] = tf.get_variable(name='res3a_branch1_lyr_err', shape=res3a_branch1.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3a_branch1'] = tf.add(res3a_branch1, err_lyr['res3a_branch1'])
bn3a_branch1 = batch_normalization(layers_err['res3a_branch1'], scale['bn3a_branch1'], offset['bn3a_branch1'], mean['bn3a_branch1'], variance['bn3a_branch1'])
res3a_branch2a = conv(res2c_relu, weights_noisy['res3a_branch2a'], biases_noisy['res3a_branch2a'], 2, 2, biased=False, relu=False)
err_lyr['res3a_branch2a'] = tf.get_variable(name='res3a_branch2a_lyr_err', shape=res3a_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3a_branch2a'] = tf.add(res3a_branch2a, err_lyr['res3a_branch2a'])
bn3a_branch2a = batch_normalization(layers_err['res3a_branch2a'], scale['bn3a_branch2a'], offset['bn3a_branch2a'], mean['bn3a_branch2a'], variance['bn3a_branch2a'], relu=True)
res3a_branch2b = conv(bn3a_branch2a, weights_noisy['res3a_branch2b'], biases_noisy['res3a_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res3a_branch2b'] = tf.get_variable(name='res3a_branch2b_lyr_err', shape=res3a_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3a_branch2b'] = tf.add(res3a_branch2b, err_lyr['res3a_branch2b'])
bn3a_branch2b = batch_normalization(layers_err['res3a_branch2b'], scale['bn3a_branch2b'], offset['bn3a_branch2b'], mean['bn3a_branch2b'], variance['bn3a_branch2b'], relu=True)
res3a_branch2c = conv(bn3a_branch2b, weights_noisy['res3a_branch2c'], biases_noisy['res3a_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res3a_branch2c'] = tf.get_variable(name='res3a_branch2c_lyr_err', shape=res3a_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3a_branch2c'] = tf.add(res3a_branch2c, err_lyr['res3a_branch2c'])
bn3a_branch2c = batch_normalization(layers_err['res3a_branch2c'], scale['bn3a_branch2c'], offset['bn3a_branch2c'], mean['bn3a_branch2c'], variance['bn3a_branch2c'])
res3a = add([bn3a_branch1, bn3a_branch2c])
err_lyr['res3a'] = tf.get_variable(name='res3a_lyr_err', shape=res3a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3a'] = tf.add(res3a, err_lyr['res3a'])
res3a_relu = relu(layers_err['res3a'])
res3b_branch2a = conv(res3a_relu, weights_noisy['res3b_branch2a'], biases_noisy['res3b_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res3b_branch2a'] = tf.get_variable(name='res3b_branch2a_lyr_err', shape=res3b_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3b_branch2a'] = tf.add(res3b_branch2a, err_lyr['res3b_branch2a'])
bn3b_branch2a = batch_normalization(layers_err['res3b_branch2a'], scale['bn3b_branch2a'], offset['bn3b_branch2a'], mean['bn3b_branch2a'], variance['bn3b_branch2a'], relu=True)
res3b_branch2b = conv(bn3b_branch2a, weights_noisy['res3b_branch2b'], biases_noisy['res3b_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res3b_branch2b'] = tf.get_variable(name='res3b_branch2b_lyr_err', shape=res3b_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3b_branch2b'] = tf.add(res3b_branch2b, err_lyr['res3b_branch2b'])
bn3b_branch2b = batch_normalization(layers_err['res3b_branch2b'], scale['bn3b_branch2b'], offset['bn3b_branch2b'], mean['bn3b_branch2b'], variance['bn3b_branch2b'], relu=True)
res3b_branch2c = conv(bn3b_branch2b, weights_noisy['res3b_branch2c'], biases_noisy['res3b_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res3b_branch2c'] = tf.get_variable(name='res3b_branch2c_lyr_err', shape=res3b_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3b_branch2c'] = tf.add(res3b_branch2c, err_lyr['res3b_branch2c'])
bn3b_branch2c = batch_normalization(layers_err['res3b_branch2c'], scale['bn3b_branch2c'], offset['bn3b_branch2c'], mean['bn3b_branch2c'], variance['bn3b_branch2c'])
res3b = add([res3a_relu, bn3b_branch2c])
err_lyr['res3b'] = tf.get_variable(name='res3b_lyr_err', shape=res3b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3b'] = tf.add(res3b, err_lyr['res3b'])
res3b_relu = relu(layers_err['res3b'])
res3c_branch2a = conv(res3b_relu, weights_noisy['res3c_branch2a'], biases_noisy['res3c_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res3c_branch2a'] = tf.get_variable(name='res3c_branch2a_lyr_err', shape=res3c_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3c_branch2a'] = tf.add(res3c_branch2a, err_lyr['res3c_branch2a'])
bn3c_branch2a = batch_normalization(layers_err['res3c_branch2a'], scale['bn3c_branch2a'], offset['bn3c_branch2a'], mean['bn3c_branch2a'], variance['bn3c_branch2a'], relu=True)
res3c_branch2b = conv(bn3c_branch2a, weights_noisy['res3c_branch2b'], biases_noisy['res3c_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res3c_branch2b'] = tf.get_variable(name='res3c_branch2b_lyr_err', shape=res3c_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3c_branch2b'] = tf.add(res3c_branch2b, err_lyr['res3c_branch2b'])
bn3c_branch2b = batch_normalization(layers_err['res3c_branch2b'], scale['bn3c_branch2b'], offset['bn3c_branch2b'], mean['bn3c_branch2b'], variance['bn3c_branch2b'], relu=True)
res3c_branch2c = conv(bn3c_branch2b, weights_noisy['res3c_branch2c'], biases_noisy['res3c_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res3c_branch2c'] = tf.get_variable(name='res3c_branch2c_lyr_err', shape=res3c_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3c_branch2c'] = tf.add(res3c_branch2c, err_lyr['res3c_branch2c'])
bn3c_branch2c = batch_normalization(layers_err['res3c_branch2c'], scale['bn3c_branch2c'], offset['bn3c_branch2c'], mean['bn3c_branch2c'], variance['bn3c_branch2c'])
res3c = add([res3b_relu, bn3c_branch2c])
err_lyr['res3c'] = tf.get_variable(name='res3c_lyr_err', shape=res3c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3c'] = tf.add(res3c, err_lyr['res3c'])
res3c_relu = relu(layers_err['res3c'])
res3d_branch2a = conv(res3c_relu, weights_noisy['res3d_branch2a'], biases_noisy['res3d_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res3d_branch2a'] = tf.get_variable(name='res3d_branch2a_lyr_err', shape=res3d_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3d_branch2a'] = tf.add(res3d_branch2a, err_lyr['res3d_branch2a'])
bn3d_branch2a = batch_normalization(layers_err['res3d_branch2a'], scale['bn3d_branch2a'], offset['bn3d_branch2a'], mean['bn3d_branch2a'], variance['bn3d_branch2a'], relu=True)
res3d_branch2b = conv(bn3d_branch2a, weights_noisy['res3d_branch2b'], biases_noisy['res3d_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res3d_branch2b'] = tf.get_variable(name='res3d_branch2b_lyr_err', shape=res3d_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3d_branch2b'] = tf.add(res3d_branch2b, err_lyr['res3d_branch2b'])
bn3d_branch2b = batch_normalization(layers_err['res3d_branch2b'], scale['bn3d_branch2b'], offset['bn3d_branch2b'], mean['bn3d_branch2b'], variance['bn3d_branch2b'], relu=True)
res3d_branch2c = conv(bn3d_branch2b, weights_noisy['res3d_branch2c'], biases_noisy['res3d_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res3d_branch2c'] = tf.get_variable(name='res3d_branch2c_lyr_err', shape=res3d_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3d_branch2c'] = tf.add(res3d_branch2c, err_lyr['res3d_branch2c'])
bn3d_branch2c = batch_normalization(layers_err['res3d_branch2c'], scale['bn3d_branch2c'], offset['bn3d_branch2c'], mean['bn3d_branch2c'], variance['bn3d_branch2c'])
res3d = add([res3c_relu, bn3d_branch2c])
err_lyr['res3d'] = tf.get_variable(name='res3d_lyr_err', shape=res3d.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res3d'] = tf.add(res3d, err_lyr['res3d'])
res3d_relu = relu(layers_err['res3d'])
res4a_branch1 = conv(res3d_relu, weights_noisy['res4a_branch1'], biases_noisy['res4a_branch1'], 2, 2, biased=False, relu=False)
err_lyr['res4a_branch1'] = tf.get_variable(name='res4a_branch1_lyr_err', shape=res4a_branch1.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4a_branch1'] = tf.add(res4a_branch1, err_lyr['res4a_branch1'])
bn4a_branch1 = batch_normalization(layers_err['res4a_branch1'], scale['bn4a_branch1'], offset['bn4a_branch1'], mean['bn4a_branch1'], variance['bn4a_branch1'])
res4a_branch2a = conv(res3d_relu, weights_noisy['res4a_branch2a'], biases_noisy['res4a_branch2a'], 2, 2, biased=False, relu=False)
err_lyr['res4a_branch2a'] = tf.get_variable(name='res4a_branch2a_lyr_err', shape=res4a_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4a_branch2a'] = tf.add(res4a_branch2a, err_lyr['res4a_branch2a'])
bn4a_branch2a = batch_normalization(layers_err['res4a_branch2a'], scale['bn4a_branch2a'], offset['bn4a_branch2a'], mean['bn4a_branch2a'], variance['bn4a_branch2a'], relu=True)
res4a_branch2b = conv(bn4a_branch2a, weights_noisy['res4a_branch2b'], biases_noisy['res4a_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res4a_branch2b'] = tf.get_variable(name='res4a_branch2b_lyr_err', shape=res4a_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4a_branch2b'] = tf.add(res4a_branch2b, err_lyr['res4a_branch2b'])
bn4a_branch2b = batch_normalization(layers_err['res4a_branch2b'], scale['bn4a_branch2b'], offset['bn4a_branch2b'], mean['bn4a_branch2b'], variance['bn4a_branch2b'], relu=True)
res4a_branch2c = conv(bn4a_branch2b, weights_noisy['res4a_branch2c'], biases_noisy['res4a_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res4a_branch2c'] = tf.get_variable(name='res4a_branch2c_lyr_err', shape=res4a_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4a_branch2c'] = tf.add(res4a_branch2c, err_lyr['res4a_branch2c'])
bn4a_branch2c = batch_normalization(layers_err['res4a_branch2c'], scale['bn4a_branch2c'], offset['bn4a_branch2c'], mean['bn4a_branch2c'], variance['bn4a_branch2c'])
res4a = add([bn4a_branch1, bn4a_branch2c])
err_lyr['res4a'] = tf.get_variable(name='res4a_lyr_err', shape=res4a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4a'] = tf.add(res4a, err_lyr['res4a'])
res4a_relu = relu(layers_err['res4a'])
res4b_branch2a = conv(res4a_relu, weights_noisy['res4b_branch2a'], biases_noisy['res4b_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res4b_branch2a'] = tf.get_variable(name='res4b_branch2a_lyr_err', shape=res4b_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4b_branch2a'] = tf.add(res4b_branch2a, err_lyr['res4b_branch2a'])
bn4b_branch2a = batch_normalization(layers_err['res4b_branch2a'], scale['bn4b_branch2a'], offset['bn4b_branch2a'], mean['bn4b_branch2a'], variance['bn4b_branch2a'], relu=True)
res4b_branch2b = conv(bn4b_branch2a, weights_noisy['res4b_branch2b'], biases_noisy['res4b_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res4b_branch2b'] = tf.get_variable(name='res4b_branch2b_lyr_err', shape=res4b_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4b_branch2b'] = tf.add(res4b_branch2b, err_lyr['res4b_branch2b'])
bn4b_branch2b = batch_normalization(layers_err['res4b_branch2b'], scale['bn4b_branch2b'], offset['bn4b_branch2b'], mean['bn4b_branch2b'], variance['bn4b_branch2b'], relu=True)
res4b_branch2c = conv(bn4b_branch2b, weights_noisy['res4b_branch2c'], biases_noisy['res4b_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res4b_branch2c'] = tf.get_variable(name='res4b_branch2c_lyr_err', shape=res4b_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4b_branch2c'] = tf.add(res4b_branch2c, err_lyr['res4b_branch2c'])
bn4b_branch2c = batch_normalization(layers_err['res4b_branch2c'], scale['bn4b_branch2c'], offset['bn4b_branch2c'], mean['bn4b_branch2c'], variance['bn4b_branch2c'])
res4b = add([res4a_relu, bn4b_branch2c])
err_lyr['res4b'] = tf.get_variable(name='res4b_lyr_err', shape=res4b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4b'] = tf.add(res4b, err_lyr['res4b'])
res4b_relu = relu(layers_err['res4b'])
res4c_branch2a = conv(res4b_relu, weights_noisy['res4c_branch2a'], biases_noisy['res4c_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res4c_branch2a'] = tf.get_variable(name='res4c_branch2a_lyr_err', shape=res4c_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4c_branch2a'] = tf.add(res4c_branch2a, err_lyr['res4c_branch2a'])
bn4c_branch2a = batch_normalization(layers_err['res4c_branch2a'], scale['bn4c_branch2a'], offset['bn4c_branch2a'], mean['bn4c_branch2a'], variance['bn4c_branch2a'], relu=True)
res4c_branch2b = conv(bn4c_branch2a, weights_noisy['res4c_branch2b'], biases_noisy['res4c_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res4c_branch2b'] = tf.get_variable(name='res4c_branch2b_lyr_err', shape=res4c_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4c_branch2b'] = tf.add(res4c_branch2b, err_lyr['res4c_branch2b'])
bn4c_branch2b = batch_normalization(layers_err['res4c_branch2b'], scale['bn4c_branch2b'], offset['bn4c_branch2b'], mean['bn4c_branch2b'], variance['bn4c_branch2b'], relu=True)
res4c_branch2c = conv(bn4c_branch2b, weights_noisy['res4c_branch2c'], biases_noisy['res4c_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res4c_branch2c'] = tf.get_variable(name='res4c_branch2c_lyr_err', shape=res4c_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4c_branch2c'] = tf.add(res4c_branch2c, err_lyr['res4c_branch2c'])
bn4c_branch2c = batch_normalization(layers_err['res4c_branch2c'], scale['bn4c_branch2c'], offset['bn4c_branch2c'], mean['bn4c_branch2c'], variance['bn4c_branch2c'])
res4c = add([res4b_relu, bn4c_branch2c])
err_lyr['res4c'] = tf.get_variable(name='res4c_lyr_err', shape=res4c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4c'] = tf.add(res4c, err_lyr['res4c'])
res4c_relu = relu(layers_err['res4c'])
res4d_branch2a = conv(res4c_relu, weights_noisy['res4d_branch2a'], biases_noisy['res4d_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res4d_branch2a'] = tf.get_variable(name='res4d_branch2a_lyr_err', shape=res4d_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4d_branch2a'] = tf.add(res4d_branch2a, err_lyr['res4d_branch2a'])
bn4d_branch2a = batch_normalization(layers_err['res4d_branch2a'], scale['bn4d_branch2a'], offset['bn4d_branch2a'], mean['bn4d_branch2a'], variance['bn4d_branch2a'], relu=True)
res4d_branch2b = conv(bn4d_branch2a, weights_noisy['res4d_branch2b'], biases_noisy['res4d_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res4d_branch2b'] = tf.get_variable(name='res4d_branch2b_lyr_err', shape=res4d_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4d_branch2b'] = tf.add(res4d_branch2b, err_lyr['res4d_branch2b'])
bn4d_branch2b = batch_normalization(layers_err['res4d_branch2b'], scale['bn4d_branch2b'], offset['bn4d_branch2b'], mean['bn4d_branch2b'], variance['bn4d_branch2b'], relu=True)
res4d_branch2c = conv(bn4d_branch2b, weights_noisy['res4d_branch2c'], biases_noisy['res4d_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res4d_branch2c'] = tf.get_variable(name='res4d_branch2c_lyr_err', shape=res4d_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4d_branch2c'] = tf.add(res4d_branch2c, err_lyr['res4d_branch2c'])
bn4d_branch2c = batch_normalization(layers_err['res4d_branch2c'], scale['bn4d_branch2c'], offset['bn4d_branch2c'], mean['bn4d_branch2c'], variance['bn4d_branch2c'])
res4d = add([res4c_relu, bn4d_branch2c])
err_lyr['res4d'] = tf.get_variable(name='res4d_lyr_err', shape=res4d.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4d'] = tf.add(res4d, err_lyr['res4d'])
res4d_relu = relu(layers_err['res4d'])
res4e_branch2a = conv(res4d_relu, weights_noisy['res4e_branch2a'], biases_noisy['res4e_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res4e_branch2a'] = tf.get_variable(name='res4e_branch2a_lyr_err', shape=res4e_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4e_branch2a'] = tf.add(res4e_branch2a, err_lyr['res4e_branch2a'])
bn4e_branch2a = batch_normalization(layers_err['res4e_branch2a'], scale['bn4e_branch2a'], offset['bn4e_branch2a'], mean['bn4e_branch2a'], variance['bn4e_branch2a'], relu=True)
res4e_branch2b = conv(bn4e_branch2a, weights_noisy['res4e_branch2b'], biases_noisy['res4e_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res4e_branch2b'] = tf.get_variable(name='res4e_branch2b_lyr_err', shape=res4e_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4e_branch2b'] = tf.add(res4e_branch2b, err_lyr['res4e_branch2b'])
bn4e_branch2b = batch_normalization(layers_err['res4e_branch2b'], scale['bn4e_branch2b'], offset['bn4e_branch2b'], mean['bn4e_branch2b'], variance['bn4e_branch2b'], relu=True)
res4e_branch2c = conv(bn4e_branch2b, weights_noisy['res4e_branch2c'], biases_noisy['res4e_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res4e_branch2c'] = tf.get_variable(name='res4e_branch2c_lyr_err', shape=res4e_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4e_branch2c'] = tf.add(res4e_branch2c, err_lyr['res4e_branch2c'])
bn4e_branch2c = batch_normalization(layers_err['res4e_branch2c'], scale['bn4e_branch2c'], offset['bn4e_branch2c'], mean['bn4e_branch2c'], variance['bn4e_branch2c'])
res4e = add([res4d_relu, bn4e_branch2c])
err_lyr['res4e'] = tf.get_variable(name='res4e_lyr_err', shape=res4e.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4e'] = tf.add(res4e, err_lyr['res4e'])
res4e_relu = relu(layers_err['res4e'])
res4f_branch2a = conv(res4e_relu, weights_noisy['res4f_branch2a'], biases_noisy['res4f_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res4f_branch2a'] = tf.get_variable(name='res4f_branch2a_lyr_err', shape=res4f_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4f_branch2a'] = tf.add(res4f_branch2a, err_lyr['res4f_branch2a'])
bn4f_branch2a = batch_normalization(layers_err['res4f_branch2a'], scale['bn4f_branch2a'], offset['bn4f_branch2a'], mean['bn4f_branch2a'], variance['bn4f_branch2a'], relu=True)
res4f_branch2b = conv(bn4f_branch2a, weights_noisy['res4f_branch2b'], biases_noisy['res4f_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res4f_branch2b'] = tf.get_variable(name='res4f_branch2b_lyr_err', shape=res4f_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4f_branch2b'] = tf.add(res4f_branch2b, err_lyr['res4f_branch2b'])
bn4f_branch2b = batch_normalization(layers_err['res4f_branch2b'], scale['bn4f_branch2b'], offset['bn4f_branch2b'], mean['bn4f_branch2b'], variance['bn4f_branch2b'], relu=True)
res4f_branch2c = conv(bn4f_branch2b, weights_noisy['res4f_branch2c'], biases_noisy['res4f_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res4f_branch2c'] = tf.get_variable(name='res4f_branch2c_lyr_err', shape=res4f_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4f_branch2c'] = tf.add(res4f_branch2c, err_lyr['res4f_branch2c'])
bn4f_branch2c = batch_normalization(layers_err['res4f_branch2c'], scale['bn4f_branch2c'], offset['bn4f_branch2c'], mean['bn4f_branch2c'], variance['bn4f_branch2c'])
res4f = add([res4e_relu, bn4f_branch2c])
err_lyr['res4f'] = tf.get_variable(name='res4f_lyr_err', shape=res4f.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res4f'] = tf.add(res4f, err_lyr['res4f'])
res4f_relu = relu(layers_err['res4f'])
res5a_branch1 = conv(res4f_relu, weights_noisy['res5a_branch1'], biases_noisy['res5a_branch1'], 2, 2, biased=False, relu=False)
err_lyr['res5a_branch1'] = tf.get_variable(name='res5a_branch1_lyr_err', shape=res5a_branch1.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5a_branch1'] = tf.add(res5a_branch1, err_lyr['res5a_branch1'])
bn5a_branch1 = batch_normalization(layers_err['res5a_branch1'], scale['bn5a_branch1'], offset['bn5a_branch1'], mean['bn5a_branch1'], variance['bn5a_branch1'])
res5a_branch2a = conv(res4f_relu, weights_noisy['res5a_branch2a'], biases_noisy['res5a_branch2a'], 2, 2, biased=False, relu=False)
err_lyr['res5a_branch2a'] = tf.get_variable(name='res5a_branch2a_lyr_err', shape=res5a_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5a_branch2a'] = tf.add(res5a_branch2a, err_lyr['res5a_branch2a'])
bn5a_branch2a = batch_normalization(layers_err['res5a_branch2a'], scale['bn5a_branch2a'], offset['bn5a_branch2a'], mean['bn5a_branch2a'], variance['bn5a_branch2a'], relu=True)
res5a_branch2b = conv(bn5a_branch2a, weights_noisy['res5a_branch2b'], biases_noisy['res5a_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res5a_branch2b'] = tf.get_variable(name='res5a_branch2b_lyr_err', shape=res5a_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5a_branch2b'] = tf.add(res5a_branch2b, err_lyr['res5a_branch2b'])
bn5a_branch2b = batch_normalization(layers_err['res5a_branch2b'], scale['bn5a_branch2b'], offset['bn5a_branch2b'], mean['bn5a_branch2b'], variance['bn5a_branch2b'], relu=True)
res5a_branch2c = conv(bn5a_branch2b, weights_noisy['res5a_branch2c'], biases_noisy['res5a_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res5a_branch2c'] = tf.get_variable(name='res5a_branch2c_lyr_err', shape=res5a_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5a_branch2c'] = tf.add(res5a_branch2c, err_lyr['res5a_branch2c'])
bn5a_branch2c = batch_normalization(layers_err['res5a_branch2c'], scale['bn5a_branch2c'], offset['bn5a_branch2c'], mean['bn5a_branch2c'], variance['bn5a_branch2c'])
res5a = add([bn5a_branch1, bn5a_branch2c])
err_lyr['res5a'] = tf.get_variable(name='res5a_lyr_err', shape=res5a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5a'] = tf.add(res5a, err_lyr['res5a'])
res5a_relu = relu(layers_err['res5a'])
res5b_branch2a = conv(res5a_relu, weights_noisy['res5b_branch2a'], biases_noisy['res5b_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res5b_branch2a'] = tf.get_variable(name='res5b_branch2a_lyr_err', shape=res5b_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5b_branch2a'] = tf.add(res5b_branch2a, err_lyr['res5b_branch2a'])
bn5b_branch2a = batch_normalization(layers_err['res5b_branch2a'], scale['bn5b_branch2a'], offset['bn5b_branch2a'], mean['bn5b_branch2a'], variance['bn5b_branch2a'], relu=True)
res5b_branch2b = conv(bn5b_branch2a, weights_noisy['res5b_branch2b'], biases_noisy['res5b_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res5b_branch2b'] = tf.get_variable(name='res5b_branch2b_lyr_err', shape=res5b_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5b_branch2b'] = tf.add(res5b_branch2b, err_lyr['res5b_branch2b'])
bn5b_branch2b = batch_normalization(layers_err['res5b_branch2b'], scale['bn5b_branch2b'], offset['bn5b_branch2b'], mean['bn5b_branch2b'], variance['bn5b_branch2b'], relu=True)
res5b_branch2c = conv(bn5b_branch2b, weights_noisy['res5b_branch2c'], biases_noisy['res5b_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res5b_branch2c'] = tf.get_variable(name='res5b_branch2c_lyr_err', shape=res5b_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5b_branch2c'] = tf.add(res5b_branch2c, err_lyr['res5b_branch2c'])
bn5b_branch2c = batch_normalization(layers_err['res5b_branch2c'], scale['bn5b_branch2c'], offset['bn5b_branch2c'], mean['bn5b_branch2c'], variance['bn5b_branch2c'])
res5b = add([res5a_relu, bn5b_branch2c])
err_lyr['res5b'] = tf.get_variable(name='res5b_lyr_err', shape=res5b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5b'] = tf.add(res5b, err_lyr['res5b'])
res5b_relu = relu(layers_err['res5b'])
res5c_branch2a = conv(res5b_relu, weights_noisy['res5c_branch2a'], biases_noisy['res5c_branch2a'], 1, 1, biased=False, relu=False)
err_lyr['res5c_branch2a'] = tf.get_variable(name='res5c_branch2a_lyr_err', shape=res5c_branch2a.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5c_branch2a'] = tf.add(res5c_branch2a, err_lyr['res5c_branch2a'])
bn5c_branch2a = batch_normalization(layers_err['res5c_branch2a'], scale['bn5c_branch2a'], offset['bn5c_branch2a'], mean['bn5c_branch2a'], variance['bn5c_branch2a'], relu=True)
res5c_branch2b = conv(bn5c_branch2a, weights_noisy['res5c_branch2b'], biases_noisy['res5c_branch2b'], 1, 1, biased=False, relu=False)
err_lyr['res5c_branch2b'] = tf.get_variable(name='res5c_branch2b_lyr_err', shape=res5c_branch2b.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5c_branch2b'] = tf.add(res5c_branch2b, err_lyr['res5c_branch2b'])
bn5c_branch2b = batch_normalization(layers_err['res5c_branch2b'], scale['bn5c_branch2b'], offset['bn5c_branch2b'], mean['bn5c_branch2b'], variance['bn5c_branch2b'], relu=True)
res5c_branch2c = conv(bn5c_branch2b, weights_noisy['res5c_branch2c'], biases_noisy['res5c_branch2c'], 1, 1, biased=False, relu=False)
err_lyr['res5c_branch2c'] = tf.get_variable(name='res5c_branch2c_lyr_err', shape=res5c_branch2c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5c_branch2c'] = tf.add(res5c_branch2c, err_lyr['res5c_branch2c'])
bn5c_branch2c = batch_normalization(layers_err['res5c_branch2c'], scale['bn5c_branch2c'], offset['bn5c_branch2c'], mean['bn5c_branch2c'], variance['bn5c_branch2c'])
res5c = add([res5b_relu, bn5c_branch2c])
err_lyr['res5c'] = tf.get_variable(name='res5c_lyr_err', shape=res5c.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['res5c'] = tf.add(res5c, err_lyr['res5c'])
res5c_relu = relu(layers_err['res5c'])
pool5 = avg_pool(res5c_relu, 7, 7, 1, 1, padding='VALID')
fc1000 = fc(pool5, weights_noisy['fc1000'], biases_noisy['fc1000'], relu=False)
err_lyr['fc1000'] = tf.get_variable(name='fc1000_lyr_err', shape=fc1000.shape[1:], initializer=tf.random_normal_initializer(mean=err_mean[3], stddev=err_stddev[3]), trainable=train_vars[3])
layers_err['fc1000'] = tf.add(fc1000, err_lyr['fc1000'])
return layers_err['fc1000'], err_w, err_b, err_lyr
def nin_noisy(input_node, netparams, err_mean, err_stddev, train_vars):
weights_noisy, biases_noisy, err_w, err_b = helper.add_noise(
netparams['weights'], netparams['biases'], err_mean, err_stddev,
train_vars)
mean, variance, scale, offset = netparams['mean'], netparams[
'variance'], netparams['scale'], netparams['offset']
err_lyr = {}
layers_err = {}
data_spec = helper.get_data_spec('nin')
err_lyr['input'] = tf.get_variable(
name='input_lyr_err',
shape=(1, data_spec.crop_size, data_spec.crop_size,
data_spec.channels),
initializer=tf.random_normal_initializer(mean=err_mean[0],
stddev=err_stddev[0]),
trainable=train_vars[0])
input_node_noisy = tf.add(input_node, err_lyr['input'])
conv1 = conv(input_node_noisy,
weights_noisy['conv1'],
biases_noisy['conv1'],
4,
4,
padding='VALID')
err_lyr['conv1'] = tf.get_variable(
name='conv1_lyr_err',
shape=conv1.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['conv1'] = tf.add(conv1, err_lyr['conv1'])
cccp1 = conv(layers_err['conv1'], weights_noisy['cccp1'],
biases_noisy['cccp1'], 1, 1)
err_lyr['cccp1'] = tf.get_variable(
name='cccp1_lyr_err',
shape=cccp1.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['cccp1'] = tf.add(cccp1, err_lyr['cccp1'])
cccp2 = conv(layers_err['cccp1'], weights_noisy['cccp2'],
biases_noisy['cccp2'], 1, 1)
err_lyr['cccp2'] = tf.get_variable(
name='cccp2_lyr_err',
shape=cccp2.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['cccp2'] = tf.add(cccp2, err_lyr['cccp2'])
pool1 = max_pool(layers_err['cccp2'], 3, 3, 2, 2)
conv2 = conv(pool1, weights_noisy['conv2'], biases_noisy['conv2'], 1, 1)
err_lyr['conv2'] = tf.get_variable(
name='conv2_lyr_err',
shape=conv2.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['conv2'] = tf.add(conv2, err_lyr['conv2'])
cccp3 = conv(layers_err['conv2'], weights_noisy['cccp3'],
biases_noisy['cccp3'], 1, 1)
err_lyr['cccp3'] = tf.get_variable(
name='cccp3_lyr_err',
shape=cccp3.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['cccp3'] = tf.add(cccp3, err_lyr['cccp3'])
cccp4 = conv(layers_err['cccp3'], weights_noisy['cccp4'],
biases_noisy['cccp4'], 1, 1)
err_lyr['cccp4'] = tf.get_variable(
name='cccp4_lyr_err',
shape=cccp4.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['cccp4'] = tf.add(cccp4, err_lyr['cccp4'])
pool2 = max_pool(layers_err['cccp4'], 3, 3, 2, 2, padding='VALID')
conv3 = conv(pool2, weights_noisy['conv3'], biases_noisy['conv3'], 1, 1)
err_lyr['conv3'] = tf.get_variable(
name='conv3_lyr_err',
shape=conv3.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['conv3'] = tf.add(conv3, err_lyr['conv3'])
cccp5 = conv(layers_err['conv3'], weights_noisy['cccp5'],
biases_noisy['cccp5'], 1, 1)
err_lyr['cccp5'] = tf.get_variable(
name='cccp5_lyr_err',
shape=cccp5.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['cccp5'] = tf.add(cccp5, err_lyr['cccp5'])
cccp6 = conv(layers_err['cccp5'], weights_noisy['cccp6'],
biases_noisy['cccp6'], 1, 1)
err_lyr['cccp6'] = tf.get_variable(
name='cccp6_lyr_err',
shape=cccp6.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['cccp6'] = tf.add(cccp6, err_lyr['cccp6'])
pool3 = max_pool(layers_err['cccp6'], 3, 3, 2, 2, padding='VALID')
conv4_1024 = conv(pool3, weights_noisy['conv4_1024'],
biases_noisy['conv4_1024'], 1, 1)
err_lyr['conv4_1024'] = tf.get_variable(
name='conv4_1024_lyr_err',
shape=conv4_1024.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['conv4_1024'] = tf.add(conv4_1024, err_lyr['conv4_1024'])
cccp7_1024 = conv(layers_err['conv4_1024'], weights_noisy['cccp7_1024'],
biases_noisy['cccp7_1024'], 1, 1)
err_lyr['cccp7_1024'] = tf.get_variable(
name='cccp7_1024_lyr_err',
shape=cccp7_1024.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['cccp7_1024'] = tf.add(cccp7_1024, err_lyr['cccp7_1024'])
cccp8_1024 = conv(layers_err['cccp7_1024'], weights_noisy['cccp8_1024'],
biases_noisy['cccp8_1024'], 1, 1)
err_lyr['cccp8_1024'] = tf.get_variable(
name='cccp8_1024_lyr_err',
shape=cccp8_1024.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['cccp8_1024'] = tf.add(cccp8_1024, err_lyr['cccp8_1024'])
pool4 = avg_pool(layers_err['cccp8_1024'], 6, 6, 1, 1, padding='VALID')
return pool4, err_w, err_b, err_lyr
def googlenet_noisy(input_node, netparams, err_mean, err_stddev, train_vars):
weights_noisy, biases_noisy, err_w, err_b = helper.add_noise(
netparams['weights'], netparams['biases'], err_mean, err_stddev,
train_vars)
mean, variance, scale, offset = netparams['mean'], netparams[
'variance'], netparams['scale'], netparams['offset']
err_lyr = {}
layers_err = {}
data_spec = helper.get_data_spec('googlenet')
err_lyr['input'] = tf.get_variable(
name='input_lyr_err',
shape=(1, data_spec.crop_size, data_spec.crop_size,
data_spec.channels),
initializer=tf.random_normal_initializer(mean=err_mean[0],
stddev=err_stddev[0]),
trainable=train_vars[0])
input_node_noisy = tf.add(input_node, err_lyr['input'])
conv1_7x7_s2 = conv(input_node_noisy, weights_noisy['conv1_7x7_s2'],
biases_noisy['conv1_7x7_s2'], 2, 2)
err_lyr['conv1_7x7_s2'] = tf.get_variable(
name='conv1_7x7_s2_lyr_err',
shape=conv1_7x7_s2.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['conv1_7x7_s2'] = tf.add(conv1_7x7_s2, err_lyr['conv1_7x7_s2'])
pool1_3x3_s2 = max_pool(layers_err['conv1_7x7_s2'], 3, 3, 2, 2)
pool1_norm1 = lrn(pool1_3x3_s2, 2, 1.99999994948e-05, 0.75)
conv2_3x3_reduce = conv(pool1_norm1, weights_noisy['conv2_3x3_reduce'],
biases_noisy['conv2_3x3_reduce'], 1, 1)
err_lyr['conv2_3x3_reduce'] = tf.get_variable(
name='conv2_3x3_reduce_lyr_err',
shape=conv2_3x3_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['conv2_3x3_reduce'] = tf.add(conv2_3x3_reduce,
err_lyr['conv2_3x3_reduce'])
conv2_3x3 = conv(layers_err['conv2_3x3_reduce'],
weights_noisy['conv2_3x3'], biases_noisy['conv2_3x3'], 1,
1)
err_lyr['conv2_3x3'] = tf.get_variable(
name='conv2_3x3_lyr_err',
shape=conv2_3x3.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['conv2_3x3'] = tf.add(conv2_3x3, err_lyr['conv2_3x3'])
conv2_norm2 = lrn(layers_err['conv2_3x3'], 2, 1.99999994948e-05, 0.75)
pool2_3x3_s2 = max_pool(conv2_norm2, 3, 3, 2, 2)
inception_3a_1x1 = conv(pool2_3x3_s2, weights_noisy['inception_3a_1x1'],
biases_noisy['inception_3a_1x1'], 1, 1)
err_lyr['inception_3a_1x1'] = tf.get_variable(
name='inception_3a_1x1_lyr_err',
shape=inception_3a_1x1.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_3a_1x1'] = tf.add(inception_3a_1x1,
err_lyr['inception_3a_1x1'])
inception_3a_3x3_reduce = conv(pool2_3x3_s2,
weights_noisy['inception_3a_3x3_reduce'],
biases_noisy['inception_3a_3x3_reduce'], 1,
1)
err_lyr['inception_3a_3x3_reduce'] = tf.get_variable(
name='inception_3a_3x3_reduce_lyr_err',
shape=inception_3a_3x3_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_3a_3x3_reduce'] = tf.add(
inception_3a_3x3_reduce, err_lyr['inception_3a_3x3_reduce'])
inception_3a_3x3 = conv(layers_err['inception_3a_3x3_reduce'],
weights_noisy['inception_3a_3x3'],
biases_noisy['inception_3a_3x3'], 1, 1)
err_lyr['inception_3a_3x3'] = tf.get_variable(
name='inception_3a_3x3_lyr_err',
shape=inception_3a_3x3.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_3a_3x3'] = tf.add(inception_3a_3x3,
err_lyr['inception_3a_3x3'])
inception_3a_5x5_reduce = conv(pool2_3x3_s2,
weights_noisy['inception_3a_5x5_reduce'],
biases_noisy['inception_3a_5x5_reduce'], 1,
1)
err_lyr['inception_3a_5x5_reduce'] = tf.get_variable(
name='inception_3a_5x5_reduce_lyr_err',
shape=inception_3a_5x5_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_3a_5x5_reduce'] = tf.add(
inception_3a_5x5_reduce, err_lyr['inception_3a_5x5_reduce'])
inception_3a_5x5 = conv(layers_err['inception_3a_5x5_reduce'],
weights_noisy['inception_3a_5x5'],
biases_noisy['inception_3a_5x5'], 1, 1)
err_lyr['inception_3a_5x5'] = tf.get_variable(
name='inception_3a_5x5_lyr_err',
shape=inception_3a_5x5.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_3a_5x5'] = tf.add(inception_3a_5x5,
err_lyr['inception_3a_5x5'])
inception_3a_pool = max_pool(pool2_3x3_s2, 3, 3, 1, 1)
inception_3a_pool_proj = conv(inception_3a_pool,
weights_noisy['inception_3a_pool_proj'],
biases_noisy['inception_3a_pool_proj'], 1, 1)
err_lyr['inception_3a_pool_proj'] = tf.get_variable(
name='inception_3a_pool_proj_lyr_err',
shape=inception_3a_pool_proj.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_3a_pool_proj'] = tf.add(
inception_3a_pool_proj, err_lyr['inception_3a_pool_proj'])
inception_3a_output = concat([
layers_err['inception_3a_1x1'], layers_err['inception_3a_3x3'],
layers_err['inception_3a_5x5'], layers_err['inception_3a_pool_proj']
], 3)
inception_3b_1x1 = conv(inception_3a_output,
weights_noisy['inception_3b_1x1'],
biases_noisy['inception_3b_1x1'], 1, 1)
err_lyr['inception_3b_1x1'] = tf.get_variable(
name='inception_3b_1x1_lyr_err',
shape=inception_3b_1x1.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_3b_1x1'] = tf.add(inception_3b_1x1,
err_lyr['inception_3b_1x1'])
inception_3b_3x3_reduce = conv(inception_3a_output,
weights_noisy['inception_3b_3x3_reduce'],
biases_noisy['inception_3b_3x3_reduce'], 1,
1)
err_lyr['inception_3b_3x3_reduce'] = tf.get_variable(
name='inception_3b_3x3_reduce_lyr_err',
shape=inception_3b_3x3_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_3b_3x3_reduce'] = tf.add(
inception_3b_3x3_reduce, err_lyr['inception_3b_3x3_reduce'])
inception_3b_3x3 = conv(layers_err['inception_3b_3x3_reduce'],
weights_noisy['inception_3b_3x3'],
biases_noisy['inception_3b_3x3'], 1, 1)
err_lyr['inception_3b_3x3'] = tf.get_variable(
name='inception_3b_3x3_lyr_err',
shape=inception_3b_3x3.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_3b_3x3'] = tf.add(inception_3b_3x3,
err_lyr['inception_3b_3x3'])
inception_3b_5x5_reduce = conv(inception_3a_output,
weights_noisy['inception_3b_5x5_reduce'],
biases_noisy['inception_3b_5x5_reduce'], 1,
1)
err_lyr['inception_3b_5x5_reduce'] = tf.get_variable(
name='inception_3b_5x5_reduce_lyr_err',
shape=inception_3b_5x5_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_3b_5x5_reduce'] = tf.add(
inception_3b_5x5_reduce, err_lyr['inception_3b_5x5_reduce'])
inception_3b_5x5 = conv(layers_err['inception_3b_5x5_reduce'],
weights_noisy['inception_3b_5x5'],
biases_noisy['inception_3b_5x5'], 1, 1)
err_lyr['inception_3b_5x5'] = tf.get_variable(
name='inception_3b_5x5_lyr_err',
shape=inception_3b_5x5.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_3b_5x5'] = tf.add(inception_3b_5x5,
err_lyr['inception_3b_5x5'])
inception_3b_pool = max_pool(inception_3a_output, 3, 3, 1, 1)
inception_3b_pool_proj = conv(inception_3b_pool,
weights_noisy['inception_3b_pool_proj'],
biases_noisy['inception_3b_pool_proj'], 1, 1)
err_lyr['inception_3b_pool_proj'] = tf.get_variable(
name='inception_3b_pool_proj_lyr_err',
shape=inception_3b_pool_proj.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_3b_pool_proj'] = tf.add(
inception_3b_pool_proj, err_lyr['inception_3b_pool_proj'])
inception_3b_output = concat([
layers_err['inception_3b_1x1'], layers_err['inception_3b_3x3'],
layers_err['inception_3b_5x5'], layers_err['inception_3b_pool_proj']
], 3)
pool3_3x3_s2 = max_pool(inception_3b_output, 3, 3, 2, 2)
inception_4a_1x1 = conv(pool3_3x3_s2, weights_noisy['inception_4a_1x1'],
biases_noisy['inception_4a_1x1'], 1, 1)
err_lyr['inception_4a_1x1'] = tf.get_variable(
name='inception_4a_1x1_lyr_err',
shape=inception_4a_1x1.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4a_1x1'] = tf.add(inception_4a_1x1,
err_lyr['inception_4a_1x1'])
inception_4a_3x3_reduce = conv(pool3_3x3_s2,
weights_noisy['inception_4a_3x3_reduce'],
biases_noisy['inception_4a_3x3_reduce'], 1,
1)
err_lyr['inception_4a_3x3_reduce'] = tf.get_variable(
name='inception_4a_3x3_reduce_lyr_err',
shape=inception_4a_3x3_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4a_3x3_reduce'] = tf.add(
inception_4a_3x3_reduce, err_lyr['inception_4a_3x3_reduce'])
inception_4a_3x3 = conv(layers_err['inception_4a_3x3_reduce'],
weights_noisy['inception_4a_3x3'],
biases_noisy['inception_4a_3x3'], 1, 1)
err_lyr['inception_4a_3x3'] = tf.get_variable(
name='inception_4a_3x3_lyr_err',
shape=inception_4a_3x3.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4a_3x3'] = tf.add(inception_4a_3x3,
err_lyr['inception_4a_3x3'])
inception_4a_5x5_reduce = conv(pool3_3x3_s2,
weights_noisy['inception_4a_5x5_reduce'],
biases_noisy['inception_4a_5x5_reduce'], 1,
1)
err_lyr['inception_4a_5x5_reduce'] = tf.get_variable(
name='inception_4a_5x5_reduce_lyr_err',
shape=inception_4a_5x5_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4a_5x5_reduce'] = tf.add(
inception_4a_5x5_reduce, err_lyr['inception_4a_5x5_reduce'])
inception_4a_5x5 = conv(layers_err['inception_4a_5x5_reduce'],
weights_noisy['inception_4a_5x5'],
biases_noisy['inception_4a_5x5'], 1, 1)
err_lyr['inception_4a_5x5'] = tf.get_variable(
name='inception_4a_5x5_lyr_err',
shape=inception_4a_5x5.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4a_5x5'] = tf.add(inception_4a_5x5,
err_lyr['inception_4a_5x5'])
inception_4a_pool = max_pool(pool3_3x3_s2, 3, 3, 1, 1)
inception_4a_pool_proj = conv(inception_4a_pool,
weights_noisy['inception_4a_pool_proj'],
biases_noisy['inception_4a_pool_proj'], 1, 1)
err_lyr['inception_4a_pool_proj'] = tf.get_variable(
name='inception_4a_pool_proj_lyr_err',
shape=inception_4a_pool_proj.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4a_pool_proj'] = tf.add(
inception_4a_pool_proj, err_lyr['inception_4a_pool_proj'])
inception_4a_output = concat([
layers_err['inception_4a_1x1'], layers_err['inception_4a_3x3'],
layers_err['inception_4a_5x5'], layers_err['inception_4a_pool_proj']
], 3)
inception_4b_1x1 = conv(inception_4a_output,
weights_noisy['inception_4b_1x1'],
biases_noisy['inception_4b_1x1'], 1, 1)
err_lyr['inception_4b_1x1'] = tf.get_variable(
name='inception_4b_1x1_lyr_err',
shape=inception_4b_1x1.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4b_1x1'] = tf.add(inception_4b_1x1,
err_lyr['inception_4b_1x1'])
inception_4b_3x3_reduce = conv(inception_4a_output,
weights_noisy['inception_4b_3x3_reduce'],
biases_noisy['inception_4b_3x3_reduce'], 1,
1)
err_lyr['inception_4b_3x3_reduce'] = tf.get_variable(
name='inception_4b_3x3_reduce_lyr_err',
shape=inception_4b_3x3_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4b_3x3_reduce'] = tf.add(
inception_4b_3x3_reduce, err_lyr['inception_4b_3x3_reduce'])
inception_4b_3x3 = conv(layers_err['inception_4b_3x3_reduce'],
weights_noisy['inception_4b_3x3'],
biases_noisy['inception_4b_3x3'], 1, 1)
err_lyr['inception_4b_3x3'] = tf.get_variable(
name='inception_4b_3x3_lyr_err',
shape=inception_4b_3x3.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4b_3x3'] = tf.add(inception_4b_3x3,
err_lyr['inception_4b_3x3'])
inception_4b_5x5_reduce = conv(inception_4a_output,
weights_noisy['inception_4b_5x5_reduce'],
biases_noisy['inception_4b_5x5_reduce'], 1,
1)
err_lyr['inception_4b_5x5_reduce'] = tf.get_variable(
name='inception_4b_5x5_reduce_lyr_err',
shape=inception_4b_5x5_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4b_5x5_reduce'] = tf.add(
inception_4b_5x5_reduce, err_lyr['inception_4b_5x5_reduce'])
inception_4b_5x5 = conv(layers_err['inception_4b_5x5_reduce'],
weights_noisy['inception_4b_5x5'],
biases_noisy['inception_4b_5x5'], 1, 1)
err_lyr['inception_4b_5x5'] = tf.get_variable(
name='inception_4b_5x5_lyr_err',
shape=inception_4b_5x5.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4b_5x5'] = tf.add(inception_4b_5x5,
err_lyr['inception_4b_5x5'])
inception_4b_pool = max_pool(inception_4a_output, 3, 3, 1, 1)
inception_4b_pool_proj = conv(inception_4b_pool,
weights_noisy['inception_4b_pool_proj'],
biases_noisy['inception_4b_pool_proj'], 1, 1)
err_lyr['inception_4b_pool_proj'] = tf.get_variable(
name='inception_4b_pool_proj_lyr_err',
shape=inception_4b_pool_proj.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4b_pool_proj'] = tf.add(
inception_4b_pool_proj, err_lyr['inception_4b_pool_proj'])
inception_4b_output = concat([
layers_err['inception_4b_1x1'], layers_err['inception_4b_3x3'],
layers_err['inception_4b_5x5'], layers_err['inception_4b_pool_proj']
], 3)
inception_4c_1x1 = conv(inception_4b_output,
weights_noisy['inception_4c_1x1'],
biases_noisy['inception_4c_1x1'], 1, 1)
err_lyr['inception_4c_1x1'] = tf.get_variable(
name='inception_4c_1x1_lyr_err',
shape=inception_4c_1x1.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4c_1x1'] = tf.add(inception_4c_1x1,
err_lyr['inception_4c_1x1'])
inception_4c_3x3_reduce = conv(inception_4b_output,
weights_noisy['inception_4c_3x3_reduce'],
biases_noisy['inception_4c_3x3_reduce'], 1,
1)
err_lyr['inception_4c_3x3_reduce'] = tf.get_variable(
name='inception_4c_3x3_reduce_lyr_err',
shape=inception_4c_3x3_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4c_3x3_reduce'] = tf.add(
inception_4c_3x3_reduce, err_lyr['inception_4c_3x3_reduce'])
inception_4c_3x3 = conv(layers_err['inception_4c_3x3_reduce'],
weights_noisy['inception_4c_3x3'],
biases_noisy['inception_4c_3x3'], 1, 1)
err_lyr['inception_4c_3x3'] = tf.get_variable(
name='inception_4c_3x3_lyr_err',
shape=inception_4c_3x3.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4c_3x3'] = tf.add(inception_4c_3x3,
err_lyr['inception_4c_3x3'])
inception_4c_5x5_reduce = conv(inception_4b_output,
weights_noisy['inception_4c_5x5_reduce'],
biases_noisy['inception_4c_5x5_reduce'], 1,
1)
err_lyr['inception_4c_5x5_reduce'] = tf.get_variable(
name='inception_4c_5x5_reduce_lyr_err',
shape=inception_4c_5x5_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4c_5x5_reduce'] = tf.add(
inception_4c_5x5_reduce, err_lyr['inception_4c_5x5_reduce'])
inception_4c_5x5 = conv(layers_err['inception_4c_5x5_reduce'],
weights_noisy['inception_4c_5x5'],
biases_noisy['inception_4c_5x5'], 1, 1)
err_lyr['inception_4c_5x5'] = tf.get_variable(
name='inception_4c_5x5_lyr_err',
shape=inception_4c_5x5.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4c_5x5'] = tf.add(inception_4c_5x5,
err_lyr['inception_4c_5x5'])
inception_4c_pool = max_pool(inception_4b_output, 3, 3, 1, 1)
inception_4c_pool_proj = conv(inception_4c_pool,
weights_noisy['inception_4c_pool_proj'],
biases_noisy['inception_4c_pool_proj'], 1, 1)
err_lyr['inception_4c_pool_proj'] = tf.get_variable(
name='inception_4c_pool_proj_lyr_err',
shape=inception_4c_pool_proj.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4c_pool_proj'] = tf.add(
inception_4c_pool_proj, err_lyr['inception_4c_pool_proj'])
inception_4c_output = concat([
layers_err['inception_4c_1x1'], layers_err['inception_4c_3x3'],
layers_err['inception_4c_5x5'], layers_err['inception_4c_pool_proj']
], 3)
inception_4d_1x1 = conv(inception_4c_output,
weights_noisy['inception_4d_1x1'],
biases_noisy['inception_4d_1x1'], 1, 1)
err_lyr['inception_4d_1x1'] = tf.get_variable(
name='inception_4d_1x1_lyr_err',
shape=inception_4d_1x1.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4d_1x1'] = tf.add(inception_4d_1x1,
err_lyr['inception_4d_1x1'])
inception_4d_3x3_reduce = conv(inception_4c_output,
weights_noisy['inception_4d_3x3_reduce'],
biases_noisy['inception_4d_3x3_reduce'], 1,
1)
err_lyr['inception_4d_3x3_reduce'] = tf.get_variable(
name='inception_4d_3x3_reduce_lyr_err',
shape=inception_4d_3x3_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4d_3x3_reduce'] = tf.add(
inception_4d_3x3_reduce, err_lyr['inception_4d_3x3_reduce'])
inception_4d_3x3 = conv(layers_err['inception_4d_3x3_reduce'],
weights_noisy['inception_4d_3x3'],
biases_noisy['inception_4d_3x3'], 1, 1)
err_lyr['inception_4d_3x3'] = tf.get_variable(
name='inception_4d_3x3_lyr_err',
shape=inception_4d_3x3.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4d_3x3'] = tf.add(inception_4d_3x3,
err_lyr['inception_4d_3x3'])
inception_4d_5x5_reduce = conv(inception_4c_output,
weights_noisy['inception_4d_5x5_reduce'],
biases_noisy['inception_4d_5x5_reduce'], 1,
1)
err_lyr['inception_4d_5x5_reduce'] = tf.get_variable(
name='inception_4d_5x5_reduce_lyr_err',
shape=inception_4d_5x5_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4d_5x5_reduce'] = tf.add(
inception_4d_5x5_reduce, err_lyr['inception_4d_5x5_reduce'])
inception_4d_5x5 = conv(layers_err['inception_4d_5x5_reduce'],
weights_noisy['inception_4d_5x5'],
biases_noisy['inception_4d_5x5'], 1, 1)
err_lyr['inception_4d_5x5'] = tf.get_variable(
name='inception_4d_5x5_lyr_err',
shape=inception_4d_5x5.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4d_5x5'] = tf.add(inception_4d_5x5,
err_lyr['inception_4d_5x5'])
inception_4d_pool = max_pool(inception_4c_output, 3, 3, 1, 1)
inception_4d_pool_proj = conv(inception_4d_pool,
weights_noisy['inception_4d_pool_proj'],
biases_noisy['inception_4d_pool_proj'], 1, 1)
err_lyr['inception_4d_pool_proj'] = tf.get_variable(
name='inception_4d_pool_proj_lyr_err',
shape=inception_4d_pool_proj.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4d_pool_proj'] = tf.add(
inception_4d_pool_proj, err_lyr['inception_4d_pool_proj'])
inception_4d_output = concat([
layers_err['inception_4d_1x1'], layers_err['inception_4d_3x3'],
layers_err['inception_4d_5x5'], layers_err['inception_4d_pool_proj']
], 3)
inception_4e_1x1 = conv(inception_4d_output,
weights_noisy['inception_4e_1x1'],
biases_noisy['inception_4e_1x1'], 1, 1)
err_lyr['inception_4e_1x1'] = tf.get_variable(
name='inception_4e_1x1_lyr_err',
shape=inception_4e_1x1.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4e_1x1'] = tf.add(inception_4e_1x1,
err_lyr['inception_4e_1x1'])
inception_4e_3x3_reduce = conv(inception_4d_output,
weights_noisy['inception_4e_3x3_reduce'],
biases_noisy['inception_4e_3x3_reduce'], 1,
1)
err_lyr['inception_4e_3x3_reduce'] = tf.get_variable(
name='inception_4e_3x3_reduce_lyr_err',
shape=inception_4e_3x3_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4e_3x3_reduce'] = tf.add(
inception_4e_3x3_reduce, err_lyr['inception_4e_3x3_reduce'])
inception_4e_3x3 = conv(layers_err['inception_4e_3x3_reduce'],
weights_noisy['inception_4e_3x3'],
biases_noisy['inception_4e_3x3'], 1, 1)
err_lyr['inception_4e_3x3'] = tf.get_variable(
name='inception_4e_3x3_lyr_err',
shape=inception_4e_3x3.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4e_3x3'] = tf.add(inception_4e_3x3,
err_lyr['inception_4e_3x3'])
inception_4e_5x5_reduce = conv(inception_4d_output,
weights_noisy['inception_4e_5x5_reduce'],
biases_noisy['inception_4e_5x5_reduce'], 1,
1)
err_lyr['inception_4e_5x5_reduce'] = tf.get_variable(
name='inception_4e_5x5_reduce_lyr_err',
shape=inception_4e_5x5_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4e_5x5_reduce'] = tf.add(
inception_4e_5x5_reduce, err_lyr['inception_4e_5x5_reduce'])
inception_4e_5x5 = conv(layers_err['inception_4e_5x5_reduce'],
weights_noisy['inception_4e_5x5'],
biases_noisy['inception_4e_5x5'], 1, 1)
err_lyr['inception_4e_5x5'] = tf.get_variable(
name='inception_4e_5x5_lyr_err',
shape=inception_4e_5x5.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4e_5x5'] = tf.add(inception_4e_5x5,
err_lyr['inception_4e_5x5'])
inception_4e_pool = max_pool(inception_4d_output, 3, 3, 1, 1)
inception_4e_pool_proj = conv(inception_4e_pool,
weights_noisy['inception_4e_pool_proj'],
biases_noisy['inception_4e_pool_proj'], 1, 1)
err_lyr['inception_4e_pool_proj'] = tf.get_variable(
name='inception_4e_pool_proj_lyr_err',
shape=inception_4e_pool_proj.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_4e_pool_proj'] = tf.add(
inception_4e_pool_proj, err_lyr['inception_4e_pool_proj'])
inception_4e_output = concat([
layers_err['inception_4e_1x1'], layers_err['inception_4e_3x3'],
layers_err['inception_4e_5x5'], layers_err['inception_4e_pool_proj']
], 3)
pool4_3x3_s2 = max_pool(inception_4e_output, 3, 3, 2, 2)
inception_5a_1x1 = conv(pool4_3x3_s2, weights_noisy['inception_5a_1x1'],
biases_noisy['inception_5a_1x1'], 1, 1)
err_lyr['inception_5a_1x1'] = tf.get_variable(
name='inception_5a_1x1_lyr_err',
shape=inception_5a_1x1.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_5a_1x1'] = tf.add(inception_5a_1x1,
err_lyr['inception_5a_1x1'])
inception_5a_3x3_reduce = conv(pool4_3x3_s2,
weights_noisy['inception_5a_3x3_reduce'],
biases_noisy['inception_5a_3x3_reduce'], 1,
1)
err_lyr['inception_5a_3x3_reduce'] = tf.get_variable(
name='inception_5a_3x3_reduce_lyr_err',
shape=inception_5a_3x3_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_5a_3x3_reduce'] = tf.add(
inception_5a_3x3_reduce, err_lyr['inception_5a_3x3_reduce'])
inception_5a_3x3 = conv(layers_err['inception_5a_3x3_reduce'],
weights_noisy['inception_5a_3x3'],
biases_noisy['inception_5a_3x3'], 1, 1)
err_lyr['inception_5a_3x3'] = tf.get_variable(
name='inception_5a_3x3_lyr_err',
shape=inception_5a_3x3.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_5a_3x3'] = tf.add(inception_5a_3x3,
err_lyr['inception_5a_3x3'])
inception_5a_5x5_reduce = conv(pool4_3x3_s2,
weights_noisy['inception_5a_5x5_reduce'],
biases_noisy['inception_5a_5x5_reduce'], 1,
1)
err_lyr['inception_5a_5x5_reduce'] = tf.get_variable(
name='inception_5a_5x5_reduce_lyr_err',
shape=inception_5a_5x5_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_5a_5x5_reduce'] = tf.add(
inception_5a_5x5_reduce, err_lyr['inception_5a_5x5_reduce'])
inception_5a_5x5 = conv(layers_err['inception_5a_5x5_reduce'],
weights_noisy['inception_5a_5x5'],
biases_noisy['inception_5a_5x5'], 1, 1)
err_lyr['inception_5a_5x5'] = tf.get_variable(
name='inception_5a_5x5_lyr_err',
shape=inception_5a_5x5.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_5a_5x5'] = tf.add(inception_5a_5x5,
err_lyr['inception_5a_5x5'])
inception_5a_pool = max_pool(pool4_3x3_s2, 3, 3, 1, 1)
inception_5a_pool_proj = conv(inception_5a_pool,
weights_noisy['inception_5a_pool_proj'],
biases_noisy['inception_5a_pool_proj'], 1, 1)
err_lyr['inception_5a_pool_proj'] = tf.get_variable(
name='inception_5a_pool_proj_lyr_err',
shape=inception_5a_pool_proj.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_5a_pool_proj'] = tf.add(
inception_5a_pool_proj, err_lyr['inception_5a_pool_proj'])
inception_5a_output = concat([
layers_err['inception_5a_1x1'], layers_err['inception_5a_3x3'],
layers_err['inception_5a_5x5'], layers_err['inception_5a_pool_proj']
], 3)
inception_5b_1x1 = conv(inception_5a_output,
weights_noisy['inception_5b_1x1'],
biases_noisy['inception_5b_1x1'], 1, 1)
err_lyr['inception_5b_1x1'] = tf.get_variable(
name='inception_5b_1x1_lyr_err',
shape=inception_5b_1x1.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_5b_1x1'] = tf.add(inception_5b_1x1,
err_lyr['inception_5b_1x1'])
inception_5b_3x3_reduce = conv(inception_5a_output,
weights_noisy['inception_5b_3x3_reduce'],
biases_noisy['inception_5b_3x3_reduce'], 1,
1)
err_lyr['inception_5b_3x3_reduce'] = tf.get_variable(
name='inception_5b_3x3_reduce_lyr_err',
shape=inception_5b_3x3_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_5b_3x3_reduce'] = tf.add(
inception_5b_3x3_reduce, err_lyr['inception_5b_3x3_reduce'])
inception_5b_3x3 = conv(layers_err['inception_5b_3x3_reduce'],
weights_noisy['inception_5b_3x3'],
biases_noisy['inception_5b_3x3'], 1, 1)
err_lyr['inception_5b_3x3'] = tf.get_variable(
name='inception_5b_3x3_lyr_err',
shape=inception_5b_3x3.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_5b_3x3'] = tf.add(inception_5b_3x3,
err_lyr['inception_5b_3x3'])
inception_5b_5x5_reduce = conv(inception_5a_output,
weights_noisy['inception_5b_5x5_reduce'],
biases_noisy['inception_5b_5x5_reduce'], 1,
1)
err_lyr['inception_5b_5x5_reduce'] = tf.get_variable(
name='inception_5b_5x5_reduce_lyr_err',
shape=inception_5b_5x5_reduce.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_5b_5x5_reduce'] = tf.add(
inception_5b_5x5_reduce, err_lyr['inception_5b_5x5_reduce'])
inception_5b_5x5 = conv(layers_err['inception_5b_5x5_reduce'],
weights_noisy['inception_5b_5x5'],
biases_noisy['inception_5b_5x5'], 1, 1)
err_lyr['inception_5b_5x5'] = tf.get_variable(
name='inception_5b_5x5_lyr_err',
shape=inception_5b_5x5.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_5b_5x5'] = tf.add(inception_5b_5x5,
err_lyr['inception_5b_5x5'])
inception_5b_pool = max_pool(inception_5a_output, 3, 3, 1, 1)
inception_5b_pool_proj = conv(inception_5b_pool,
weights_noisy['inception_5b_pool_proj'],
biases_noisy['inception_5b_pool_proj'], 1, 1)
err_lyr['inception_5b_pool_proj'] = tf.get_variable(
name='inception_5b_pool_proj_lyr_err',
shape=inception_5b_pool_proj.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['inception_5b_pool_proj'] = tf.add(
inception_5b_pool_proj, err_lyr['inception_5b_pool_proj'])
inception_5b_output = concat([
layers_err['inception_5b_1x1'], layers_err['inception_5b_3x3'],
layers_err['inception_5b_5x5'], layers_err['inception_5b_pool_proj']
], 3)
pool5_7x7_s1 = avg_pool(inception_5b_output, 7, 7, 1, 1, padding='VALID')
loss3_classifier = fc(pool5_7x7_s1,
weights_noisy['loss3_classifier'],
biases_noisy['loss3_classifier'],
relu=False)
err_lyr['loss3_classifier'] = tf.get_variable(
name='loss3_classifier_lyr_err',
shape=loss3_classifier.shape[1:],
initializer=tf.random_normal_initializer(mean=err_mean[3],
stddev=err_stddev[3]),
trainable=train_vars[3])
layers_err['loss3_classifier'] = tf.add(loss3_classifier,
err_lyr['loss3_classifier'])
return layers_err['loss3_classifier'], err_w, err_b, err_lyr