# Copyright (c) 2018-2019, Krzysztof Rusek
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# author: Krzysztof Rusek, AGH
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
import tensorflow as tf
from tensorflow import keras
import numpy as np
import argparse
hparams = tf.contrib.training.HParams(
node_count=14,
link_state_dim=4,
path_state_dim=2,
T=3,
readout_units=8,
learning_rate=0.001,
batch_size=32,
dropout_rate=0.5,
l2=0.1,
l2_2=0.01,
learn_embedding=True, # If false, only the readout is trained
readout_layers=2, # number of hidden layers in readout model
)
class RouteNet(tf.keras.Model):
def __init__(self,hparams, output_units=1, final_activation=None):
super(RouteNet, self).__init__()
self.hparams = hparams
self.output_units = output_units
self.final_activation = final_activation
def build(self, input_shape=None):
del input_shape
self.edge_update = tf.keras.layers.GRUCell(self.hparams.link_state_dim, name="edge_update")
self.path_update = tf.keras.layers.GRUCell(self.hparams.path_state_dim, name="path_update")
self.readout = tf.keras.models.Sequential(name='readout')
for i in range(self.hparams.readout_layers):
self.readout.add(tf.keras.layers.Dense(self.hparams.readout_units,
activation=tf.nn.selu,
kernel_regularizer=tf.contrib.layers.l2_regularizer(self.hparams.l2)))
self.readout.add(tf.keras.layers.Dropout(rate=self.hparams.dropout_rate))
self.final = keras.layers.Dense(self.output_units,
kernel_regularizer=tf.contrib.layers.l2_regularizer(self.hparams.l2_2),
activation = self.final_activation )
self.edge_update.build(tf.TensorShape([None,self.hparams.path_state_dim]))
self.path_update.build(tf.TensorShape([None,self.hparams.link_state_dim]))
self.readout.build(input_shape = [None,self.hparams.path_state_dim])
self.final.build(input_shape = [None,self.hparams.path_state_dim + self.hparams.readout_units ])
self.built = True
def call(self, inputs, training=False):
'''
outputs:
Natural parameter
'''
f_ = inputs
shape = tf.stack([f_['n_links'],self.hparams.link_state_dim-1], axis=0)
#link_state = tf.zeros(shape)
link_state = tf.concat([
tf.expand_dims(f_['capacities'],axis=1),
tf.zeros(shape)
], axis=1)
shape = tf.stack([f_['n_paths'],self.hparams.path_state_dim-1], axis=0)
path_state = tf.concat([
tf.expand_dims(f_['traffic'][0:f_["n_paths"]],axis=1),
tf.zeros(shape)
], axis=1)
links = f_['links']
paths = f_['paths']
seqs= f_['sequences']
for _ in range(self.hparams.T):
h_ = tf.gather(link_state,links)
#TODO move this to feature calculation
ids=tf.stack([paths, seqs], axis=1)
max_len = tf.reduce_max(seqs)+1
shape = tf.stack([f_['n_paths'], max_len, self.hparams.link_state_dim])
lens = tf.segment_sum(data=tf.ones_like(paths),
segment_ids=paths)
link_inputs = tf.scatter_nd(ids, h_, shape)
#TODO move to tf.keras.RNN
outputs, path_state = tf.nn.dynamic_rnn(self.path_update,
link_inputs,
sequence_length=lens,
initial_state = path_state,
dtype=tf.float32)
m = tf.gather_nd(outputs,ids)
m = tf.unsorted_segment_sum(m, links ,f_['n_links'])
#Keras cell expects a list
link_state,_ = self.edge_update(m, [link_state])
if self.hparams.learn_embedding:
r = self.readout(path_state,training=training)
o = self.final(tf.concat([r,path_state], axis=1))
else:
r = self.readout(tf.stop_gradient(path_state),training=training)
o = self.final(tf.concat([r, tf.stop_gradient(path_state)], axis=1) )
return o
def delay_model_fn(
features, # This is batch_features from input_fn
labels, # This is batch_labrange
mode, # An instance of tf.estimator.ModeKeys
params): # Additional configuration
model = RouteNet(params, output_units=2)
model.build()
predictions = model(features, training=mode==tf.estimator.ModeKeys.TRAIN)
loc = predictions[...,0]
c = np.log(np.expm1( np.float32(0.098) ))
scale = tf.math.softplus(c + predictions[...,1]) + np.float32(1e-9)
delay_prediction = loc
jitter_prediction = scale**2
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode,
predictions={'delay':delay_prediction, 'jitter':jitter_prediction}
)
with tf.name_scope('heteroscedastic_loss'):
x=features
y=labels
n=x['packets']-y['drops']
_2sigma = np.float32(2.0)*scale**2
nll = n*y['jitter']/_2sigma + n*tf.math.squared_difference(y['delay'], loc)/_2sigma + n*tf.math.log(scale)
loss = tf.reduce_sum(nll)/np.float32(1e6)
regularization_loss = sum(model.losses)
total_loss = loss + regularization_loss
tf.summary.scalar('regularization_loss', regularization_loss)
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode,loss=loss,
eval_metric_ops={
'label/mean/delay':tf.metrics.mean(labels['delay']),
'label/mean/jitter':tf.metrics.mean(labels['jitter']),
'prediction/mean/delay': tf.metrics.mean(delay_prediction),
'prediction/mean/jitter': tf.metrics.mean(jitter_prediction),
'mae/delay':tf.metrics.mean_absolute_error(labels['delay'], delay_prediction),
'mae/jitter':tf.metrics.mean_absolute_error(labels['jitter'], jitter_prediction),
'rho/delay':tf.contrib.metrics.streaming_pearson_correlation(labels=labels['delay'],predictions=delay_prediction),
'rho/jitter':tf.contrib.metrics.streaming_pearson_correlation(labels=labels['jitter'],predictions=jitter_prediction)
}
)
assert mode == tf.estimator.ModeKeys.TRAIN
trainables = model.variables
grads = tf.gradients(total_loss, trainables)
grad_var_pairs = zip(grads, trainables)
summaries = [tf.summary.histogram(var.op.name, var) for var in trainables]
summaries += [tf.summary.histogram(g.op.name, g) for g in grads if g is not None]
decayed_lr = tf.train.exponential_decay(params.learning_rate,
tf.train.get_global_step(), 50000,
0.9, staircase=True)
optimizer=tf.train.AdamOptimizer(decayed_lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.apply_gradients(grad_var_pairs,
global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode,
loss=total_loss,
train_op=train_op,
)
def drop_model_fn(
features, # This is batch_features from input_fn
labels, # This is batch_labrange
mode, # An instance of tf.estimator.ModeKeys
params): # Additional configuration
model = RouteNet(params, output_units=1, final_activation=None)
model.build()
logits = model(features, training=mode==tf.estimator.ModeKeys.TRAIN)
logits = tf.squeeze(logits)
predictions = tf.math.sigmoid(logits)
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode,
predictions={'drops':predictions, 'logits':logits}
)
with tf.name_scope('binomial_loss'):
x=features
y=labels
loss_ratio = y['drops']/x['packets']
# Binomial negative Log-likelihood
loss = tf.reduce_sum(x['packets']*tf.nn.sigmoid_cross_entropy_with_logits(
labels = loss_ratio,
logits = logits
))/np.float32(1e5)
regularization_loss = sum(model.losses)
total_loss = loss + regularization_loss
tf.summary.scalar('regularization_loss', regularization_loss)
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(
mode,loss=loss,
eval_metric_ops={
'label/mean/drops':tf.metrics.mean(loss_ratio),
'prediction/mean/drops': tf.metrics.mean(predictions),
'mae/drops':tf.metrics.mean_absolute_error(loss_ratio, predictions),
'rho/drops':tf.contrib.metrics.streaming_pearson_correlation(labels=loss_ratio,predictions=predictions)
}
)
assert mode == tf.estimator.ModeKeys.TRAIN
trainables = model.trainable_variables
grads = tf.gradients(total_loss, trainables)
grad_var_pairs = zip(grads, trainables)
summaries = [tf.summary.histogram(var.op.name, var) for var in trainables]
summaries += [tf.summary.histogram(g.op.name, g) for g in grads if g is not None]
decayed_lr = tf.train.exponential_decay(params.learning_rate,
tf.train.get_global_step(), 50000,
0.9, staircase=True)
# TODO use decay !
optimizer=tf.train.AdamOptimizer(decayed_lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.apply_gradients(grad_var_pairs,
global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode,
loss=total_loss,
train_op=train_op,
)
def scale_fn(k, val):
'''Scales given feature
Args:
k: key
val: tensor value
'''
if k == 'traffic':
return (val-0.18)/.15
if k == 'capacities':
return val/10.0
return val
def parse(serialized, target=None, normalize=True):
'''
Target is the name of predicted variable-deprecated
'''
with tf.device("/cpu:0"):
with tf.name_scope('parse'):
#TODO add feature spec class
features = tf.io.parse_single_example(
serialized,
features={
'traffic':tf.VarLenFeature(tf.float32),
'delay':tf.VarLenFeature(tf.float32),
'logdelay':tf.VarLenFeature(tf.float32),
'jitter':tf.VarLenFeature(tf.float32),
'drops':tf.VarLenFeature(tf.float32),
'packets':tf.VarLenFeature(tf.float32),
'capacities':tf.VarLenFeature(tf.float32),
'links':tf.VarLenFeature(tf.int64),
'paths':tf.VarLenFeature(tf.int64),
'sequences':tf.VarLenFeature(tf.int64),
'n_links':tf.FixedLenFeature([],tf.int64),
'n_paths':tf.FixedLenFeature([],tf.int64),
'n_total':tf.FixedLenFeature([],tf.int64)
})
for k in ['traffic','delay','logdelay','jitter','drops','packets','capacities','links','paths','sequences']:
features[k] = tf.sparse.to_dense( features[k] )
if normalize:
features[k] = scale_fn(k, features[k])
#return {k:v for k,v in features.items() if k is not target },features[target]
return features
def cummax(alist, extractor):
with tf.name_scope('cummax'):
maxes = [tf.reduce_max( extractor(v) ) + 1 for v in alist ]
cummaxes = [tf.zeros_like(maxes[0])]
for i in range(len(maxes)-1):
cummaxes.append( tf.math.add_n(maxes[0:i+1]))
return cummaxes
def transformation_func(it, batch_size=32):
with tf.name_scope("transformation_func"):
vs = [it.get_next() for _ in range(batch_size)]
links_cummax = cummax(vs,lambda v:v['links'] )
paths_cummax = cummax(vs,lambda v:v['paths'] )
tensors = ({
'traffic':tf.concat([v['traffic'] for v in vs], axis=0),
'capacities': tf.concat([v['capacities'] for v in vs], axis=0),
'sequences':tf.concat([v['sequences'] for v in vs], axis=0),
'packets':tf.concat([v['packets'] for v in vs], axis=0),
'links':tf.concat([v['links'] + m for v,m in zip(vs, links_cummax) ], axis=0),
'paths':tf.concat([v['paths'] + m for v,m in zip(vs, paths_cummax) ], axis=0),
'n_links':tf.math.add_n([v['n_links'] for v in vs]),
'n_paths':tf.math.add_n([v['n_paths'] for v in vs]),
'n_total':tf.math.add_n([v['n_total'] for v in vs])
}, {
'delay' : tf.concat([v['delay'] for v in vs], axis=0),
'logdelay' : tf.concat([v['logdelay'] for v in vs], axis=0),
'drops' : tf.concat([v['drops'] for v in vs], axis=0),
'jitter' : tf.concat([v['jitter'] for v in vs], axis=0),
}
)
return tensors
def tfrecord_input_fn(filenames,hparams,shuffle_buf=1000, target='delay'):
files = tf.data.Dataset.from_tensor_slices(filenames)
files = files.shuffle(len(filenames))
ds = files.apply(tf.data.experimental.parallel_interleave(
tf.data.TFRecordDataset, cycle_length=4))
if shuffle_buf:
ds = ds.apply(tf.data.experimental.shuffle_and_repeat(shuffle_buf))
else :
# sample 10 % for evaluation because it is time consuming
ds = ds.filter(lambda x: tf.random_uniform(shape=())< 0.1)
ds = ds.map(lambda buf:parse(buf,target),
num_parallel_calls=2)
ds=ds.prefetch(10)
it =ds.make_one_shot_iterator()
sample = transformation_func(it,hparams.batch_size)
return sample
def serving_input_receiver_fn():
"""
This is used to define inputs to serve the model.
returns: ServingInputReceiver
"""
receiver_tensors = {
'capacities': tf.placeholder(tf.float32, [None]),
'traffic': tf.placeholder(tf.float32, [None]),
'links': tf.placeholder(tf.int32, [None]),
'paths': tf.placeholder(tf.int32, [None]),
'sequences': tf.placeholder(tf.int32, [None]),
'n_links': tf.placeholder(tf.int32, []),
'n_paths':tf.placeholder(tf.int32, []),
}
# Convert give inputs to adjust to the model.
features = {k: scale_fn(k,v) for k,v in receiver_tensors.items() }
return tf.estimator.export.ServingInputReceiver(receiver_tensors=receiver_tensors,
features=features)
def train(args):
print(args)
tf.logging.set_verbosity('INFO')
if args.hparams:
hparams.parse(args.hparams)
model_fn = delay_model_fn if args.target =='delay' else drop_model_fn
estimator = tf.estimator.Estimator(
model_fn = model_fn,
model_dir=args.model_dir,
params=hparams,
warm_start_from=args.warm
)
best_exporter = tf.estimator.BestExporter(
serving_input_receiver_fn=serving_input_receiver_fn,
exports_to_keep=2)
latest_exporter = tf.estimator.LatestExporter(
name="latests",
serving_input_receiver_fn=serving_input_receiver_fn,
exports_to_keep=5)
train_spec = tf.estimator.TrainSpec(input_fn=lambda:tfrecord_input_fn(args.train,hparams,shuffle_buf=args.shuffle_buf,target=args.target),
max_steps=args.train_steps)
eval_spec = tf.estimator.EvalSpec(input_fn=lambda:tfrecord_input_fn(args.evaluation,hparams,shuffle_buf=None,target=args.target),
steps=args.eval_steps,
exporters=[best_exporter,latest_exporter],
#throttle_secs=1800)
throttle_secs=600)
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
def main():
parser = argparse.ArgumentParser(description='RouteNet script')
subparsers = parser.add_subparsers(help='sub-command help')
parser_train = subparsers.add_parser('train', help='Train options')
parser_train.add_argument('--hparams', type=str,
help='Comma separated list of "name=value" pairs.')
parser_train.add_argument('--train', help='Train Tfrecords files', type=str ,nargs='+')
parser_train.add_argument('--evaluation', help='Evaluation Tfrecords files', type=str ,nargs='+')
parser_train.add_argument('--model_dir', help='Model directory', type=str )
parser_train.add_argument('--train_steps', help='Training steps', type=int, default=100 )
parser_train.add_argument('--eval_steps', help='Evaluation steps, defaul None= all', type=int, default=None )
parser_train.add_argument('--shuffle_buf',help = "Buffer size for samples shuffling", type=int, default=10000)
parser_train.add_argument('--target',help = "Predicted variable", type=str, default='delay')
parser_train.add_argument('--warm',help = "Warm start from", type=str, default=None)
parser_train.set_defaults(func=train)
args = parser.parse_args()
return args.func(args)
if __name__ == '__main__':
main()
最新发布
本文讨论了在制作TFRecords数据集时常见的错误，即未充分混合数据导致训练效果不佳的问题。以Cifar-10数据集为例，详细说明了如何正确地将图片数据打乱并写入TFRecords，确保网络能够接收到多样化的输入，从而提升训练效果。
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