def hyperparameter_search(hp_domain, n_iter, n_replicas, save_dir, model):
""" Random hyperparameter search on DNN model """
dataset = utils.load_cifar(USE_CIFAR100)
start_time = time.time()
gpu_devices = utils.get_available_gpus()
threads = []
for rep_id in range(n_replicas):
threads.append(threading.Thread(target=_hp_search_replica, kwargs={'start_time': start_time, 'gpu_devices': gpu_devices, 'hp_domain': hp_domain,
'save_dir': save_dir, 'dataset': dataset, 'rep_id': rep_id, 'n_iter': n_iter, 'model': model}))
threads[-1].start()
for thread in threads:
thread.join()
# Look for best hyperparameter set we found
best_results = {'acc': float('-inf'), 'model_name': None, 'hp': None}
for root, dirs, files in os.walk(save_dir):
for d in dirs:
if re.match('model_([0-9]+)_([0-9]+)', d) is not None:
results = np.load(os.path.join(root, d, 'results.npy')).tolist()
if best_results['acc'] < results['acc']:
best_results = results
best_results['model_name'] = d
print('\n\nHyperparameter search done!\n\tbest_acc=%4f\n\tbest_model_name=%s' % (best_results['acc'], best_results['model_name']) + '\n' + '#' * 100)
return best_results
def __init__(self, in_steps, out_steps, img_shape, devices, filters=[32,1], num_blocks=1, mode="train",
starter_learning_rate=0.001, decay_step=500, decay_rate=1.0, verbose_step=1):
self.in_steps = in_steps
self.out_steps = out_steps
self.img_height, self.img_width = img_shape
self.num_blocks = num_blocks
self.input_shape = [self.img_height, self.img_width, 1]
self.filters = filters
self.starter_learning_rate = starter_learning_rate
self.devices = devices
self.gpus = len(devices)
self.decay_step = decay_step
self.decay_rate = decay_rate
self.global_step = None
self.learning_rate = None
self.keep_rate = None
self.verbose_step = verbose_step
self.mode = mode
self.x = None
self.pw = None
self.y = None
self.y_pw = None
self.y_hat = None
self.is_training = None
self.loss = None
self.loss_1 = None
self.gpus_names = get_available_gpus()[:self.gpus]
self.block_idx_per_gpu = get_block_idx_per_gpu(self.num_blocks, self.gpus)
def arg_parse():
# python train.py --mode train --data_dir sequence_data --gpus 4 --num_blocks 4 --batch_size 1 --train_epochs 20 --lr 0.001 --verbose_step 10
parser = argparse.ArgumentParser()
parser.add_argument("--mode", default="train")
parser.add_argument("--data_dir",
default=gen_data.sequence_data_dir,
help="data dir")
parser.add_argument("--gpus",
default=len(get_available_gpus()),
type=int,
help="number of gpus")
parser.add_argument("--num_blocks",
default=1,
type=int,
help="depth of network")
parser.add_argument("--batch_size", default=1, type=int, help="batch size")
parser.add_argument("--train_epochs",
default=50,
type=int,
help="train epochs")
parser.add_argument("--lr",
default=0.00002,
type=float,
help="starter learning rate")
parser.add_argument("--verbose_step",
default=10,
type=int,
help="verbose step")
return parser.parse_args()
def __init__(self,
stage,
croplen,
nclasses,
optimization=None,
momentum=None,
reuse_variable=False):
self.stage = stage
self.croplen = croplen
self.nclasses = nclasses
self.dataloader = None
self.queue_coord = None
self.queue_threads = None
self._optimization = optimization
self._momentum = momentum
self.summaries = []
self.towers = []
self._train = None
self._reuse = reuse_variable
self._accum = None
self._init = None
self.small_chunk = 1
self.nccl = False
self.replica = False
self.devices = digits.get_available_gpus(
) # it will append cpu further if empty
self.gpus = len(self.devices)
self._nesterov = False
def train(self, xs1, xs2, scores):
global_step = tf.train.get_or_create_global_step()
lr = noam_scheme(self.context.lr, global_step,
self.context.warmup_steps)
optimizer = tf.train.AdamOptimizer(lr)
gpus = get_available_gpus()
if gpus:
num_gpu = len(gpus)
assert self.context.hparams.batch_size % num_gpu == 0
xs1s, xs2s = tf.split(xs1, num_gpu, axis=0), tf.split(xs2,
num_gpu,
axis=0)
scoress = tf.split(scores, num_gpu, axis=0)
tower_grads = []
losses = []
with tf.variable_scope(tf.get_variable_scope()) as scope:
list_predictions = []
for i in range(num_gpu):
with tf.device("/gpu:%d" % i):
with tf.name_scope("tower_%d" % i):
predictions = self._get_prediction(
xs1s[i], xs2s[i])
list_predictions.append(predictions)
# square loss
partial_loss = tf.reduce_sum(tf.squared_difference(
predictions, scoress[i]),
name="loss")
losses.append(partial_loss)
tf.get_variable_scope().reuse_variables()
grad = get_gradients_by_loss_and_optimizer(
partial_loss, optimizer)
tower_grads.append(grad)
predictions = tf.concat(list_predictions, axis=0)
loss = tf.reduce_mean(losses)
grads_and_vars = average_gradients(tower_grads)
else:
predictions = self._get_prediction(xs1, xs2)
loss = tf.reduce_sum(tf.squared_difference(predictions, scores),
name="loss")
grads_and_vars = get_gradients_by_loss_and_optimizer(
loss, optimizer)
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
for g, v in grads_and_vars:
tf.summary.histogram(v.name, v)
tf.summary.histogram(v.name + '_grad', g)
tf.summary.scalar("pred_avg", tf.reduce_mean(predictions))
tf.summary.scalar("label_avg", tf.reduce_mean(scores))
tf.summary.scalar('lr', lr)
tf.summary.scalar("loss", loss)
tf.summary.scalar("global_step", global_step)
summaries = tf.summary.merge_all()
return loss, train_op, global_step, summaries
def train(self, inputs, targets):
global_step = tf.train.get_or_create_global_step()
lr = noam_scheme(self._context.lr, global_step,
self._context.warmup_steps)
optimizer = tf.train.AdamOptimizer(lr)
gpus = get_available_gpus()
loss_func = self._loss_func_dict.get(self._context.loss_func,
self._get_loss)
if gpus:
num_gpu = len(gpus)
assert self._context.hparams.batch_size % num_gpu == 0
partial_inputs = [[] for _ in range(num_gpu)]
for input_tmp in inputs:
input_tmps = tf.split(input_tmp, num_gpu, axis=0)
for i in range(num_gpu):
partial_inputs[i].append(input_tmps[i])
targetses = tf.split(targets, num_gpu, axis=0)
tower_grads = []
losses = []
with tf.variable_scope(tf.get_variable_scope()) as scope:
for i in range(num_gpu):
with tf.device("/gpu:%d" % i):
with tf.name_scope("tower_%d" % i):
partial_loss = loss_func(partial_inputs[i],
targetses[i])
losses.append(partial_loss)
tf.get_variable_scope().reuse_variables()
grad = get_gradients_by_loss_and_optimizer(
partial_loss, optimizer)
tower_grads.append(grad)
loss = tf.reduce_mean(losses)
grads_and_vars = average_gradients(tower_grads)
else:
loss = tf.reduce_mean(loss_func(inputs, targets))
grads_and_vars = get_gradients_by_loss_and_optimizer(
loss, optimizer)
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
for g, v in grads_and_vars:
if g is None: # 无梯度
continue
tf.summary.histogram(v.name, v)
tf.summary.histogram(v.name + '_grad', g)
tf.summary.scalar("pred_avg", tf.reduce_mean(self._outputs))
tf.summary.scalar("infr_avg", tf.reduce_mean(self._inferences))
tf.summary.scalar("label_avg", tf.reduce_mean(targets))
tf.summary.scalar('lr', lr)
tf.summary.scalar("loss", loss)
tf.summary.scalar("global_step", global_step)
summaries = tf.summary.merge_all()
return loss, train_op, global_step, summaries
def __init__(self, target_bos_id, target_eos_id, params):
self.params = params
self.available_gpus = get_available_gpus()
self.current_gpu_index = 0
self.total_gpu_num = len(self.available_gpus)
self.target_bos_id = target_bos_id
self.target_eos_id = target_eos_id
print("learning_rate:", self.params.learning_rate)
def get_session():
tf.reset_default_graph()
tf_config=tf.ConfigProto(
inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1
)
session=tf.Session(config=tf_config)
print('AVAILABLE GPUS:',utils.get_available_gpus())
return session
def get_model(pretrained):
"""
按照机器配置构建合理模型
:return:
"""
if get_available_gpus() > 1:
model = build_model()
print(pretrained)
if pretrained:
my_model.load_weights(pretrained)
else:
pass
model = multi_gpu_model(model, get_available_gpus())
else:
model = build_model()
if pretrained:
model.load_weights(pretrained)
return model
def _load_model(self):
"""
Load model on GPU if available, else on CPU, and get placeholders for input and outputs
:return: None
"""
self._logger.info('Loading model...')
self._logger.debug('creating TF session')
self._tf_sess = tf.Session(
graph=self._graph,
config=tf.ConfigProto(allow_soft_placement=True))
gpus = get_available_gpus()
if gpus:
self._logger.debug('loading graph on GPU')
with tf.device(gpus[0]):
new_saver = tf.train.import_meta_graph(self._model_file +
'.meta',
clear_devices=True)
new_saver.restore(self._tf_sess, self._model_file)
self._graph = self._tf_sess.graph
else:
self._logger.debug('loading graph on CPU')
new_saver = tf.train.import_meta_graph(self._model_file + '.meta',
clear_devices=True)
new_saver.restore(self._tf_sess, self._model_file)
self._graph = self._tf_sess.graph
self._logger.debug('getting placeholders')
# get graph input placeholder
self._input_placeholder = self._graph.get_tensor_by_name(
'image_tensor:0')
# get output placeholders
self._output_nb_detections = self._graph.get_tensor_by_name(
'num_detections:0')
self._output_classes = self._graph.get_tensor_by_name(
'detection_classes:0')
self._output_boxes = self._graph.get_tensor_by_name(
'detection_boxes:0')
self._output_scores = self._graph.get_tensor_by_name(
'detection_scores:0')
self._logger.info('... model loaded')
def __init__(self,
model_dir,
data_directory,
data_format="NHWC",
lr=0.001,
n_gpus=2,
n_fold=5,
seed=42,
save_best=5,
**kwargs):
"""
High level class to perform multi GPU training with tf.contrib.distribute.MirroredStrategy
and tf.Estimator.
The base models are built using tf.slim.
All the data is processed using tf.data.Dataset and tf.image.
The preprocessing currently runs on the CPU (optimal?)
Additionally built ResNet with NCHW format support for (potentially) faster GPU and MKL optimised CPU operations
Currently NCHW format support is experimental and the speed up is minor (about 10%)
:param model_dir:
:param data_directory:
:param data_format:
:param lr:
:param n_gpus:
:param n_fold:
:param seed:
"""
if data_format in ["NCHW", "NHWC"]:
self.data_format = data_format
else:
raise ValueError(
f"Unknown data format {data_format}. Has to be either NCHW or NHWC"
)
if "weight_decay" in kwargs:
self.weight_decay = kwargs["weight_decay"]
else:
self.weight_decay = WEIGHT_DECAY
if "batch_norm_decay" in kwargs:
self.batch_norm_decay = kwargs["weight_decay"]
else:
self.batch_norm_decay = BATCH_NORM_DECAY
if "batch_norm_epsilon" in kwargs:
self.batch_norm_epsilon = kwargs["batch_norm_epsilon"]
else:
self.batch_norm_epsilon = BATCH_NORM_EPSILON
if "batch_norm_scale" in kwargs:
self.batch_norm_scale = kwargs["batch_norm_scale"]
else:
self.batch_norm_scale = BATCH_NORM_SCALE
if "output_stride" in kwargs:
self.output_stride = kwargs["output_stride"]
else:
self.output_stride = OUTPUT_STRIDE
if "base_depth" in kwargs:
self.base_depth = kwargs["base_depth"]
else:
self.base_depth = BASE_DEPTH
if "input_shape" in kwargs:
self.input_shape = kwargs["input_shape"]
else:
self.input_shape = INPUT_SHAPE
if "n_blocks" in kwargs:
self.n_blocks = kwargs["n_blocks"]
else:
self.n_blocks = (3, 4, 6)
if "block_type" in kwargs:
self.block_type = kwargs["block_type"]
else:
self.block_type = "bottleneck"
# Pathing stuff
self.model_name = model_dir.split('/')[-1]
self.model_dir = model_dir
self.data_dir = data_directory
# Estimator stuff
available_gpus = get_available_gpus()
distribution = tf.contrib.distribute.MirroredStrategy(
devices=available_gpus[:n_gpus])
self.config = tf.estimator.RunConfig(
save_checkpoints_steps=500,
train_distribute=distribution,
save_summary_steps=0 # running summary manually
)
# Additional args to fine tune training at high level
self.data_format = data_format
self.n_gpus = n_gpus
self.n_folds = n_fold
self.seed = seed
self.lr = lr
self.save_best = save_best
_prepare_directory(self.model_dir, self.n_folds)
self.skf = StratifiedKFold(n_splits=self.n_folds,
shuffle=True,
random_state=self.seed)
def __init__(self, config, cc_config, model_config=None):
self.config = config
self.cc_config = cc_config
self.model_dir = config.model_dir
self.cc_config.model_dir = config.model_dir
self.model_config = model_config
if self.model_config:
self.model_config.model_dir = config.model_dir
self.save_model_dir = os.path.join(self.model_dir, 'checkpoints')
if not os.path.exists(self.save_model_dir):
os.mkdir(self.save_model_dir)
self.summary_dir = os.path.join(self.model_dir, 'summaries')
if not os.path.exists(self.summary_dir):
os.mkdir(self.summary_dir)
self.load_path = config.load_path
self.use_gpu = config.use_gpu
#This tensor controls batch_size for all models
#Not expected to change during training, but during testing it can be
#helpful to change it
self.batch_size = tf.placeholder_with_default(self.config.batch_size,
[],
name='batch_size')
loader_batch_size = config.num_devices * config.batch_size
#Always need to build CC
print('setting up CausalController')
cc_batch_size = config.num_devices * self.batch_size #Tensor/placeholder
self.cc = CausalController(cc_batch_size, cc_config)
self.step = self.cc.step
#Data
print('setting up data')
self.data = DataLoader(self.cc.label_names, config)
if self.cc_config.is_pretrain or self.config.build_pretrain:
print('setup pretrain')
#queue system to feed labels quickly. This does not queue images
label_queue = self.data.get_label_queue(loader_batch_size)
self.cc.build_pretrain(label_queue)
#Build Model
if self.model_config:
#Will build both gen and discrim
self.model = self.config.Model(self.batch_size, self.model_config)
#Trainer step is defined as cc.step+model.step
#e.g. 10k iter pretrain and 100k iter image model
#will have image summaries at 100k but trainer model saved at Model-110k
self.step += self.model.step
# This queue holds (image,label) pairs, and is used for training conditional GANs
data_queue = self.data.get_data_queue(loader_batch_size)
self.real_data_by_gpu = distribute_input_data(
data_queue, config.num_gpu)
self.fake_data_by_gpu = distribute_input_data(
self.cc.label_dict, config.num_gpu)
with tf.variable_scope('tower'):
for gpu in get_available_gpus():
print('using device:', gpu)
real_data = self.real_data_by_gpu[gpu]
fake_data = self.fake_data_by_gpu[gpu]
tower = gpu.replace('/', '').replace(':', '_')
with tf.device(gpu), tf.name_scope(tower):
#Build num_gpu copies of graph: inputs->gradient
#Updates self.tower_dict
self.model(real_data, fake_data)
#allow future gpu to use same variables
tf.get_variable_scope().reuse_variables()
if self.model_config.is_train or self.config.build_train:
self.model.build_train_op()
self.model.build_summary_op()
else:
print('Image model not built')
self.saver = tf.train.Saver(keep_checkpoint_every_n_hours=2)
self.summary_writer = tf.summary.FileWriter(self.summary_dir)
print('trainer.model_dir:', self.model_dir)
gpu_options = tf.GPUOptions(allow_growth=True,
per_process_gpu_memory_fraction=0.333)
sess_config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
sv = tf.train.Supervisor(logdir=self.save_model_dir,
is_chief=True,
saver=self.saver,
summary_op=None,
summary_writer=self.summary_writer,
save_model_secs=300,
global_step=self.step,
ready_for_local_init_op=None)
self.sess = sv.prepare_or_wait_for_session(config=sess_config)
if cc_config.pt_load_path:
print('Attempting to load pretrain model:', cc_config.pt_load_path)
self.cc.load(self.sess, cc_config.pt_load_path)
print('Check tvd after restore')
info = crosstab(self, report_tvd=True)
print('tvd after load:', info['tvd'])
#save copy of cc model in new dir
cc_step = self.sess.run(self.cc.step)
self.cc.saver.save(self.sess, self.cc.save_model_name, cc_step)
if config.load_path: #Declare loading point
pnt_str = 'Loaded variables at ccStep:{}'
cc_step = self.sess.run(self.cc.step)
pnt_str = pnt_str.format(cc_step)
print('pntstr', pnt_str)
if self.model_config:
pnt_str += ' imagemodelStep:{}'
model_step = self.sess.run
pnt_str = pnt_str.format(model_step)
print(pnt_str)
#PREPARE training:
#TODO save as Variables so they are restored to same values when load model
fixed_batch_size = 256 #get this many fixed z values
self.fetch_fixed_z = {n.z: n.z for n in self.cc.nodes}
if model_config:
self.fetch_fixed_z[self.model.z_gen] = self.model.z_gen
#feed_dict that ensures constant inputs
#add feed_fixed_z[self.cc.Male.label]=1*ones() to intervene
self.feed_fixed_z = self.sess.run(self.fetch_fixed_z,
{self.batch_size: fixed_batch_size})
parser.add_argument('--print_freq', type=int, default=50)
parser.add_argument('--eval_freq', type=int, default=5)
parser.add_argument('--lr', type=float, default=1e-1)
parser.add_argument('--decay', type=float, default=5e-4)
parser.add_argument('--savedir', type=str, default=None)
parser.add_argument('--mode', type=str, default='train')
parser.add_argument('--n_gpus', type=int, default=None)
args = parser.parse_args()
os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
savedir = ('./results/%d_%d' % (args.depth, args.K)) \
if args.savedir is None else args.savedir
if not os.path.isdir(savedir):
os.makedirs(savedir)
available_gpus = get_available_gpus()
n_gpus = len(available_gpus) if args.n_gpus is None else args.n_gpus
available_gpus = available_gpus[:n_gpus]
print ('GPUs to be used: ' + str(available_gpus))
batch_size = args.batch_size
n_train_batches = NUM_TRAIN / batch_size
n_test_batches = NUM_TEST / args.batch_size
with tf.device('/cpu:0'):
x, y = cifar10_input(batch_size, True)
xs = tf.split(x, n_gpus, axis=0)
ys = tf.split(y, n_gpus, axis=0)
x, y = cifar10_input(batch_size, False)
txs = tf.split(x, n_gpus, axis=0)
tys = tf.split(y, n_gpus, axis=0)
import subprocess
import tensorflow as tf
from utils import get_available_gpus
GPUS = 4
GPUS = min(GPUS, len(get_available_gpus()))
python_cmd = "python"
def run_server(idx):
subprocess.call([python_cmd, "helper_server.py", str(GPUS), str(idx)])
processes = []
for i in range(1, GPUS):
p = Process(target=run_server, args=(i, ))
processes.append(p)
for p in processes:
p.start()
for p in processes:
p.join()
model_checkpoint = ModelCheckpoint(model_names, monitor='val_loss', verbose=1, save_best_only=True)
early_stop = EarlyStopping('val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau('val_loss', factor=0.1, patience=int(patience / 4), verbose=1)
class MyCbk(keras.callbacks.Callback):
def __init__(self, model):
keras.callbacks.Callback.__init__(self)
self.model_to_save = model
def on_epoch_end(self, epoch, logs=None):
fmt = checkpoint_models_path + 'model.%02d-%.4f.hdf5'
self.model_to_save.save(fmt % (epoch, logs['val_loss']))
num_gpu = len(get_available_gpus())
if num_gpu >= 2:
with tf.device("/cpu:0"):
# Load our model, added support for Multi-GPUs
model = build_model()
if pretrained_path is not None:
model.load_weights(pretrained_path)
new_model = multi_gpu_model(model, gpus=num_gpu)
# rewrite the callback: saving through the original model and not the multi-gpu model.
model_checkpoint = MyCbk(model)
else:
new_model = build_model()
if pretrained_path is not None:
new_model.load_weights(pretrained_path)
from keras import backend as K
import tensorflow as tf
import h5py
import cv2
import numpy as np
import matplotlib; matplotlib.use('Agg')
import matplotlib.pyplot as plt
# In[5]:
from keras.backend.tensorflow_backend import set_session
from utils import limited_gpu_memory_session, get_available_gpus
set_session(limited_gpu_memory_session(0.95))
print(get_available_gpus())
# In[6]:
DATA_DIR = '/home/Drive2/rishabh/'
INIT_WEIGHTS = os.path.join(DATA_DIR, 'init_weights_omniglot.hdf5')
CHECKPOINTED_WEIGHTS = os.path.join(DATA_DIR, 'checkpointed_weights_omniglot.hdf5')
# In[7]:
### Load the test and train datasets
import tensorflow as tf
from collections import namedtuple
from utils import get_available_gpus
ALL_DEVICES = get_available_gpus()
NUM_DEVICES = 3
if len(ALL_DEVICES) > NUM_DEVICES:
ALL_DEVICES = ALL_DEVICES[-NUM_DEVICES:]
CHECKPOINT_DIR = './checkpoint/{}_{}'
DICTIONARY_DIR = './data/{}/dictionary.pkl'
TRAIN_PARAMS = {
'batch_size': 128 * max(1, len(ALL_DEVICES)),
'epochs': 1000,
'emb_size': 50,
'learning_rate': 0.01,
'ng_sample': 25,
'buffer_size': 128,
'min_count': 5,
'max_count': 10000,
'decay_steps': -1,
'decay_rate': 0.01,
'lower_gradient': -10,
'upper_gradient': 10,
}
RUN_CONFIG = {
'devices': ALL_DEVICES,
'summary_steps': 50,
def create_model(self, obj_UserModel, stage_scope, batch_x=None):
if batch_x is None:
self.init_dataloader()
batch_x = self.dataloader.batch_x
if self.stage != digits.STAGE_INF:
batch_y = self.dataloader.batch_y
else:
assert self.stage == digits.STAGE_INF
batch_x = batch_x
available_devices = digits.get_available_gpus()
if not available_devices:
available_devices.append('/cpu:0')
# available_devices = ['/gpu:0', '/gpu:1'] # DEVELOPMENT : virtual multi-gpu
# Split the batch over the batch dimension over the number of available gpu's
if len(available_devices) == 1:
batch_x_split = [batch_x]
if self.stage != digits.STAGE_INF: # Has no labels
batch_y_split = [batch_y]
else:
with tf.name_scope('parallelize'):
# Split them up
batch_x_split = tf.split(batch_x,
len(available_devices),
0,
name='split_batch')
if self.stage != digits.STAGE_INF: # Has no labels
batch_y_split = tf.split(batch_y,
len(available_devices),
0,
name='split_batch')
# Get global regularizaion loss collection reference as a list named r_loss_global.
# Now we can edit regularizaion loss collection by operation r_loss_global list
r_loss_global = tf.get_collection_ref(
ops.GraphKeys.REGULARIZATION_LOSSES)
# Note:
# (In training stage)
# r_loss_train_bak = [] (a bak to store all tower's regularizaion loss)
# r_loss_global = (global regularizaion loss)'s reference
# For each Tower:
# empty r_loss_global
# Tower.inference (may add regularizaion loss globally)
# r_loss_tain_bak += r_loss_global
# ...
#
# (restore all tower's reg. loss so validation stage could use it)
# r_loss_global[:] = r_loss_train_bak[:]
#
# (In validation stage)
# r_loss_global = (global regularizaion loss)'s reference
# r_loss_val_bak = list(r_loss_global) <= deep copy
# For each Tower:
# empty r_loss_global
# parse element name start with 'tower_%d' % dev_i in r_loss_val_bak
# ... and save to r_loss_global
#
# Tower.inference (will not add any regularizaion loss cause reuse=True)
# ( Some operations only catch regularizaion losses belong to current tower)
# ...
#
if self.replica:
# Save regularizaion loss of all tower in training stage
if self.stage != digits.STAGE_TRAIN:
r_loss_val_bak = list(r_loss_global)
# Create a list to store regularizaion loss
if self.stage == digits.STAGE_TRAIN:
r_loss_train_bak = list()
# Run the user model through the build_model function that should be filled in
grad_towers = []
for dev_i, dev_name in enumerate(available_devices):
with tf.device(dev_name):
if self.replica:
r_loss_global[:] = []
if self.stage != digits.STAGE_TRAIN:
r_loss_global = [
loss for loss in r_loss_val_bak
if loss.name.startswith('train/tower_%d' % dev_i)
]
with tf.name_scope('tower_%d' % dev_i) as scope_tower:
if self.stage != digits.STAGE_INF:
tower_model = self.add_tower(obj_tower=obj_UserModel,
x=batch_x_split[dev_i],
y=batch_y_split[dev_i])
else:
tower_model = self.add_tower(obj_tower=obj_UserModel,
x=batch_x_split[dev_i],
y=None)
with tf.variable_scope(
'tower_0' if not self.replica else 'tower_%d' %
dev_i,
reuse=(False if self.replica else dev_i > 0)
or self._reuse):
tower_model.inference # touch to initialize
# Reuse the variables in this scope for the next tower/device
tf.get_variable_scope().reuse_variables()
if self.stage == digits.STAGE_INF:
# For inferencing we will only use the inference part of the graph
continue
with tf.name_scope(digits.GraphKeys.LOSS):
for loss in self.get_tower_losses(
tower_model, dev_i):
tf.add_to_collection(digits.GraphKeys.LOSSES,
loss['loss'])
# Assemble all made within this scope so far. The user can add custom
# losses to the digits.GraphKeys.LOSSES collection
losses = tf.get_collection(digits.GraphKeys.LOSSES,
scope=scope_tower)
if (self.replica
) and self.stage == digits.STAGE_TRAIN:
r_loss_train_bak += r_loss_global
losses += ops.get_collection(
ops.GraphKeys.REGULARIZATION_LOSSES,
scope=None)
tower_loss = tf.add_n(losses, name='loss')
self.summaries.append(
tf.summary.scalar(tower_loss.op.name,
tower_loss))
if self.stage == digits.STAGE_TRAIN:
grad_tower_losses = []
for loss in self.get_tower_losses(
tower_model, dev_i):
# use loss + regularization loss instead of loss only
grad_tower_loss = self.optimizer.compute_gradients(
tower_loss, loss['vars'])
grad_tower_loss = tower_model.gradientUpdate(
grad_tower_loss)
grad_tower_losses.append(grad_tower_loss)
grad_towers.append(grad_tower_losses)
# Assemble and average the gradients from all towers
if self.stage == digits.STAGE_TRAIN:
if self.replica:
r_loss_global[:] = r_loss_train_bak[:]
grad_accum = []
grad_averages = []
n_gpus = len(available_devices)
if n_gpus == 1:
n_losses = len(grad_towers[0])
for loss in xrange(n_losses):
if (self.replica):
grad_averages.append([grad_towers[0][loss]])
else:
grad_averages.append(grad_towers[0][loss])
for g, _ in grad_towers[0][loss]:
grad_accum.append(g)
else:
n_losses = len(grad_towers[0])
for loss in xrange(n_losses):
if not self.nccl:
if (self.replica):
grad_averages.append(
average_grads([
grad_towers[gpu][loss]
for gpu in xrange(n_gpus)
]))
else:
grad_averages.append(
average_gradients([
grad_towers[gpu][loss]
for gpu in xrange(n_gpus)
], 0))
else:
if (self.replica):
grad_averages.append(
allreduce_gradients_bak([
grad_towers[gpu][loss]
for gpu in xrange(n_gpus)
]))
else:
grad_averages.append(
allreduce_gradients([
grad_towers[gpu][loss]
for gpu in xrange(n_gpus)
], 0))
for gpu in xrange(n_gpus):
for g, _ in grad_towers[gpu][loss]:
grad_accum.append(g)
apply_gradient_ops = []
for grad_avg in grad_averages:
if (self.replica):
tmp = []
for grad_and_vars in grad_avg:
for (g, v) in grad_and_vars:
tmp.append((g, v))
else:
tmp = grad_avg
apply_gradient_ops.append(
self.optimizer.apply_gradients(
tmp, global_step=self.global_step))
self._train = apply_gradient_ops
self._accum = tf.group(*grad_accum)
if (self.replica):
self._init = self.get_post_init_ops()
else:
self._init = []
def create_model(self, obj_UserModel, stage_scope, batch_x=None):
if batch_x is None:
self.init_dataloader()
batch_x = self.dataloader.batch_x
if self.stage != digits.STAGE_INF:
batch_y = self.dataloader.batch_y
else:
assert self.stage == digits.STAGE_INF
batch_x = batch_x
available_devices = digits.get_available_gpus()
if not available_devices:
available_devices.append('/cpu:0')
# available_devices = ['/gpu:0', '/gpu:1'] # DEVELOPMENT : virtual multi-gpu
# Split the batch over the batch dimension over the number of available gpu's
if len(available_devices) == 1:
batch_x_split = [batch_x]
if self.stage != digits.STAGE_INF: # Has no labels
batch_y_split = [batch_y]
else:
with tf.name_scope('parallelize'):
# Split them up
batch_x_split = tf.split(0, len(available_devices), batch_x, name='split_batch')
if self.stage != digits.STAGE_INF: # Has no labels
batch_y_split = tf.split(0, len(available_devices), batch_y, name='split_batch')
# Run the user model through the build_model function that should be filled in
grad_towers = []
for dev_i, dev_name in enumerate(available_devices):
with tf.device(dev_name):
current_scope = stage_scope if len(available_devices) == 1 else ('tower_%d' % dev_i)
with tf.name_scope(current_scope) as scope_tower:
if self.stage != digits.STAGE_INF:
tower_model = self.add_tower(obj_tower=obj_UserModel,
x=batch_x_split[dev_i],
y=batch_y_split[dev_i])
else:
tower_model = self.add_tower(obj_tower=obj_UserModel,
x=batch_x_split[dev_i],
y=None)
with tf.variable_scope(digits.GraphKeys.MODEL, reuse=dev_i > 0):
tower_model.inference # touch to initialize
if self.stage == digits.STAGE_INF:
# For inferencing we will only use the inference part of the graph
continue
with tf.name_scope(digits.GraphKeys.LOSS):
for loss in self.get_tower_losses(tower_model):
tf.add_to_collection(digits.GraphKeys.LOSSES, loss['loss'])
# Assemble all made within this scope so far. The user can add custom
# losses to the digits.GraphKeys.LOSSES collection
losses = tf.get_collection(digits.GraphKeys.LOSSES, scope=scope_tower)
losses += ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES, scope=None)
tower_loss = tf.add_n(losses, name='loss')
self.summaries.append(tf.scalar_summary(tower_loss.op.name, tower_loss))
# Reuse the variables in this scope for the next tower/device
tf.get_variable_scope().reuse_variables()
if self.stage == digits.STAGE_TRAIN:
grad_tower_losses = []
for loss in self.get_tower_losses(tower_model):
grad_tower_loss = self.optimizer.compute_gradients(loss['loss'], loss['vars'])
grad_tower_loss = tower_model.gradientUpdate(grad_tower_loss)
grad_tower_losses.append(grad_tower_loss)
grad_towers.append(grad_tower_losses)
# Assemble and average the gradients from all towers
if self.stage == digits.STAGE_TRAIN:
n_gpus = len(available_devices)
if n_gpus == 1:
grad_averages = grad_towers[0]
else:
with tf.device(available_devices[0]):
n_losses = len(grad_towers[0])
grad_averages = []
for loss in xrange(n_losses):
grad_averages.append(average_gradients([grad_towers[gpu][loss] for gpu in xrange(n_gpus)]))
apply_gradient_ops = []
for grad_avg in grad_averages:
apply_gradient_ops.append(self.optimizer.apply_gradients(grad_avg, global_step=self.global_step))
self._train = apply_gradient_ops
def create_model(self, obj_UserModel, stage_scope, batch_x=None):
logging.debug('Stage: {}'.format(stage_scope))
# get batch data
if batch_x is None:
self.init_dataloader()
batch_x = self.dataloader.batch_x
if self.stage != utils.STAGE_INF:
batch_y = self.dataloader.batch_y
else:
assert self.stage == utils.STAGE_INF
batch_x = batch_x
logging.debug('batch_x shape={}'.format(batch_x.get_shape().as_list()))
if self.stage != utils.STAGE_INF:
logging.debug('batch_y shape={}'.format(
batch_y.get_shape().as_list()))
# get avilable gpu list
available_devices = utils.get_available_gpus()
logging.debug('GPUs {}'.format(available_devices))
if not available_devices:
available_devices.append('/cpu:0')
# available_devices = ['/gpu:0', '/gpu:1'] # DEVELOPMENT: virtual multi-gpu
# Split the batch over the batch dimension over the number of available gpu's
if len(available_devices) == 1:
batch_x_split = [batch_x]
if self.stage != utils.STAGE_INF: # Has no labels
batch_y_split = [batch_y]
else:
with tf.name_scope('parallelize'):
# Split them up
batch_x_split = tf.split(batch_x,
len(available_devices),
0,
name='split_batch_x')
if self.stage != utils.STAGE_INF: # Has no labels
batch_y_split = tf.split(batch_y,
len(available_devices),
0,
name='split_batch_y')
# Run the user model through the build_model function that should be filled in
# collect all type of lossess and all gpus
grad_towers = []
#with tf.variable_scope(tf.get_variable_scope()):
for dev_i, dev_name in enumerate(available_devices):
with tf.device(dev_name):
current_scope = stage_scope if len(
available_devices) == 1 else ('tower_{}'.format(dev_i))
with tf.name_scope(current_scope) as scope_tower:
with tf.variable_scope(utils.GraphKeys.MODEL,
reuse=dev_i > 0 or self._reuse):
if self.stage != utils.STAGE_INF:
tower_model = self.add_tower(
obj_tower=obj_UserModel,
x=batch_x_split[dev_i],
y=batch_y_split[dev_i])
else:
tower_model = self.add_tower(
obj_tower=obj_UserModel,
x=batch_x_split[dev_i],
y=None)
tower_model.inference # touch to initialize
# Reuse the variables int this scope for the next tower/device
tf.get_variable_scope().reuse_variables()
if self.stage == utils.STAGE_INF:
# For inferencing we will only use the inference part of the graph
continue
with tf.name_scope(utils.GraphKeys.LOSS):
for loss in self.get_tower_losses(tower_model):
tf.add_to_collection(utils.GraphKeys.LOSSES,
loss['loss'])
# Assemble all made within this scope so far. The user can add custom
# losses to the utils.GraphKeys.LOSSES collection
losses = tf.get_collection(utils.GraphKeys.LOSSES,
scope=scope_tower)
#logging.debug('get_collection: graykeys.LOSSES : {}'.format(losses))
#logging.debug('get_collection: graykeys.REGULARIZATION_LOSSES : {}'.format(ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES, scope=None)))
losses += ops.get_collection(
ops.GraphKeys.REGULARIZATION_LOSSES,
scope=None)
tower_loss = tf.add_n(losses, name='loss')
self.summaries.append(
scalar_summary(tower_loss.op.name, tower_loss))
if self.stage == utils.STAGE_TRAIN:
# collect all type of losses on the gpu
grad_tower_losses = []
# for each type loss
for loss in self.get_tower_losses(tower_model):
# compute gradients of this gpu
grad_tower_loss = self.optimizer.compute_gradients(
loss['loss'], loss['vars'])
grad_tower_loss = tower_model.gradientUpdate(
grad_tower_loss)
grad_tower_losses.append(grad_tower_loss)
grad_towers.append(grad_tower_losses)
# Assemble and average the gradients from all towers
if self.stage == utils.STAGE_TRAIN:
n_gpus = len(available_devices)
if n_gpus == 1:
grad_averages = grad_towers[0]
else:
with tf.device(available_devices[0]):
n_losses = len(grad_towers[0])
grad_averages = []
# for each loss, averages loss on all gpus
for loss in xrange(n_losses):
grad_averages.append(
average_gradients([
grad_towers[gpu][loss]
for gpu in xrange(n_gpus)
]))
apply_gradient_ops = []
for grad_avg in grad_averages:
# apply average gradients
apply_gradient_ops.append(
self.optimizer.apply_gradients(
grad_avg, global_step=self.global_step))
# train op, list
self._train = apply_gradient_ops
def train(log_dir, args, hp):
# create dir
os.makedirs(log_dir, exist_ok=True)
checkpoint_dir = os.path.join(log_dir, 'checkpoints')
event_dir = os.path.join(log_dir, 'events')
os.makedirs(event_dir, exist_ok=True)
os.makedirs(checkpoint_dir, exist_ok=True)
checkpoint_path = os.path.join(checkpoint_dir, 'model_ckpt')
audio_dir = os.path.join(log_dir, 'train_stats', 'wavs')
plot_dir = os.path.join(log_dir, 'train_stats', 'plots')
eval_audio_dir = os.path.join(log_dir, 'eval_stats', 'wavs')
eval_plot_dir = os.path.join(log_dir, 'eval_stats', 'plots')
os.makedirs(audio_dir, exist_ok=True)
os.makedirs(plot_dir, exist_ok=True)
os.makedirs(eval_audio_dir, exist_ok=True)
os.makedirs(eval_plot_dir, exist_ok=True)
# sess config
config = tf.ConfigProto(
gpu_options=tf.GPUOptions(force_gpu_compatible=True,
allow_growth=True),
allow_soft_placement=True,
log_device_placement=False,
)
# how many gpus will be used
num_gpus = len(utils.get_available_gpus(config))
controller = "/gpu:0" if num_gpus == 1 else "/cpu:0"
# create dataset and iterator
train_dataset = get_dataset(args.train_file,
True,
hp,
batch_size=hp.batch_size * num_gpus)
val_dataset = get_dataset(args.val_file,
False,
hp,
batch_size=hp.batch_size * num_gpus)
iterator = tf.data.Iterator.from_structure(train_dataset.output_types,
train_dataset.output_shapes)
# feeder: inputs, targets, input_lengths, local_condition, global_condition
next_inputs = iterator.get_next()
# To Do: multi gpu training
feeder = get_inputs(next_inputs, 1)
train_init = iterator.make_initializer(train_dataset)
val_init = iterator.make_initializer(val_dataset)
# global step
global_step = tf.Variable(name='global_step',
initial_value=-1,
trainable=False,
dtype=tf.int64)
global_val_step = tf.Variable(name='global_val_step',
initial_value=-1,
trainable=False,
dtype=tf.int64)
global_val_step_op = tf.assign_add(global_val_step,
1,
name='global_val_step_add')
# apply ema to variable
ema = tf.train.ExponentialMovingAverage(decay=hp.ema_decay)
# create model
# use multi gpu to train
train_model = create_train_model(feeder, ema, hp, global_step)
eval_model = create_eval_model(feeder, hp)
# save info
saver = tf.train.Saver(max_to_keep=5)
train_stats = add_stats(train_model)
train_loss_window = ValueWindow(100)
val_loss_window = ValueWindow(100)
with tf.Session(config=config) as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter(event_dir, sess.graph)
# restore from checkpoint
if args.restore_step is not None:
restore_path = '{}-{}'.format(checkpoint_path, args.restore_step)
# we don't load the ema to continue training, that is just for evaluating
saver.restore(sess, restore_path)
print('Resuming from checkpoint: {}...'.format(args.restore_step))
else:
print('Start new training....')
for epoch in range(args.epochs):
sess.run(train_init)
while True:
try:
start_time = time.time()
step, loss, _, = sess.run(
[global_step, train_model.loss, train_model.optimize])
train_loss_window.append(loss)
if step % 10 == 0:
message = 'Epoch {:4d} Train Step {:7d} [{:.3f} sec/step step_loss={:.5f} avg_loss={:.5f}]'.format(
epoch, step,
time.time() - start_time, loss,
train_loss_window.average)
print(message)
if step % args.checkpoint_interval == 0:
saver.save(sess, checkpoint_path, step)
save_log(sess, step, train_model, plot_dir, audio_dir,
hp)
if step % args.summary_interval == 0:
print('Writing summary at step {}'.format(step))
summary_writer.add_summary(sess.run(train_stats), step)
sys.stdout.flush()
except tf.errors.OutOfRangeError:
break
sess.run(val_init)
while True:
try:
start_time = time.time()
loss = sess.run(train_model.loss)
val_loss_window.append(loss)
step = sess.run(global_val_step_op)
message = 'Epoch {:4d} Val Step {:7d} [{:.3f} sec/step step_loss={:.5f} avg_loss={:.5f}]'.format(
epoch, step,
time.time() - start_time, loss,
val_loss_window.average)
print(message)
if step % args.eval_interval == 0:
eval_step(eval_model, sess, step, eval_plot_dir,
eval_audio_dir)
if step % args.summary_val_interval == 0:
add_test_stats(summary_writer, step, loss)
sys.stdout.flush()
except tf.errors.OutOfRangeError:
break
def get_model_fn(features, labels, mode, params):
"""Returns a function that will build the TargetedLearning framework."""
"""Model body.
Args:
features: a list of tensors
labels: a list of tensors
mode: ModeKeys.TRAIN or EVAL
params: Hyperparameters suitable for tuning
Returns:
A EstimatorSpec object.
"""
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
weight_decay = params.weight_decay
out_lr = 0.1 #params.learning_rate
train_features = features[0]
train_labels = labels[0]
if is_training:
val_features = features[1]
val_labels = labels[1]
else:
val_features = features[0]
val_labels = labels[0]
# channels first (NCHW) is normally optimal on GPU and channels last (NHWC)
# on CPU. The exception is Intel MKL on CPU which is optimal with
# channels_last.
num_gpus = len(utils.get_available_gpus())
data_format = params.data_format
if not data_format:
if num_gpus == 0:
data_format = 'channels_last'
else:
data_format = 'channels_first'
train_op = []
# Building the base model
with tf.compat.v1.variable_scope('base_model') as var_scope:
if params.dataset == 'mnist':
base_model = model.BilevelLenet(num_class=params.num_class)
else:
base_model = model.BilevelResNet(resnet_size=params.num_layers,
num_classes=params.num_class,
resnet_version=params.version)
base_model_logits = base_model(train_features, is_training)
update_ops = tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.UPDATE_OPS, var_scope.name)
extra_update_ops = base_model.get_updates_for(train_features)
update_ops.extend(extra_update_ops)
# Get the params of the model
base_model_params = tf.compat.v1.trainable_variables(
scope=var_scope.name)
# Set initial weights
class_init = np.array([[1.0 / params.num_class]
for _ in range(params.num_class)
]).astype(np.float32)
class_weights = tf.compat.v1.get_variable('class_weight',
initializer=class_init)
weight = tf.matmul(
tf.cast(
tf.one_hot(train_labels,
len(class_init),
on_value=1,
off_value=0), tf.float32), class_weights)
# Get the loss of the main model
base_model_loss, base_model_preds = _loss_fn(
base_model_logits,
tf.one_hot(train_labels, params.num_class, on_value=1,
off_value=0))
base_model_loss_reduced = tf.reduce_mean(
tf.squeeze(weight) * base_model_loss) + weight_decay * tf.add_n(
[tf.nn.l2_loss(v) for v in base_model_params])
# Define the outer model's logits, which is the bilevel model
with tf.compat.v1.variable_scope(
'bilevel_model', reuse=tf.compat.v1.AUTO_REUSE) as var_scope1:
base_model.perturb_model_weights(base_model_loss_reduced,
params.learning_rate, var_scope.name)
target_logits = base_model(val_features, False)
target_params = tf.compat.v1.trainable_variables(scope=var_scope1.name)
target_loss, target_preds = _loss_fn(
target_logits,
tf.one_hot(val_labels, params.num_class, on_value=1, off_value=0))
target_loss = tf.reduce_mean(target_loss) + weight_decay * tf.add_n(
[tf.nn.l2_loss(v) for v in target_params])
# Calculate the gradients with respect to the class weights and normalize it
class_weight_gradient = tf.gradients(target_loss, class_weights)
update_class_weights = tf.clip_by_value(class_weights -
out_lr * class_weight_gradient[0],
clip_value_min=0.0,
clip_value_max=100.0)
sum_class_weights = tf.reduce_sum(update_class_weights) + 2e-12
update_class_weights /= sum_class_weights
# Update the weight every n steps.
weight_update_hook = utils.WeightUpdateHook1(
class_weights,
update_class_weights,
every_n_steps=10,
log_every_n_step=params.log_freq)
# Calculate the base model grads
base_model_grads = tf.gradients(base_model_loss_reduced, base_model_params)
base_model_gradvars = zip(base_model_grads, base_model_params)
boundaries = [
params.num_batches_per_epoch * x
for x in np.array([91, 136, 182], dtype=np.int64)
]
staged_lr = [params.learning_rate * x for x in [1, 0.1, 0.01, 0.001]]
learning_rate = tf.compat.v1.train.piecewise_constant(
tf.compat.v1.train.get_global_step(), boundaries, staged_lr)
# Define optimizer
optimizer = tf.compat.v1.train.MomentumOptimizer(
learning_rate=learning_rate, momentum=params.momentum)
# optimizer = tf.train.AdamOptimizer(learning_rate=params.learning_rate)
train_op.append(
optimizer.apply_gradients(
base_model_gradvars,
global_step=tf.compat.v1.train.get_global_step()))
# Calculate metrics
target_accuracy = tf.compat.v1.metrics.accuracy(val_labels,
target_preds['classes'])
accuracy = tf.compat.v1.metrics.accuracy(train_labels,
base_model_preds['classes'])
# The following metrics are for the binary classification scenario.
# They should be adopted for multiclass classification tasks.
if params.num_class == 2:
train_labels_mask = tf.cast(train_labels, tf.bool)
inverse_train_labels_mask = tf.cast(
tf.math.logical_not(train_labels_mask), tf.float32)
inverse_prediction_mask = tf.cast(
tf.math.logical_not(tf.cast(base_model_preds['classes'], tf.bool)),
tf.float32)
recall_minor = tf.compat.v1.metrics.recall(inverse_train_labels_mask,
inverse_prediction_mask)
recall_major = tf.compat.v1.metrics.recall(train_labels,
base_model_preds['classes'])
precision_minor = tf.compat.v1.metrics.precision(
inverse_train_labels_mask, inverse_prediction_mask)
metrics = {
'obj/accuracy': accuracy,
'metrics/recall_minor': recall_minor,
'metrics/recall_major': recall_major,
'metrics/precision_minor': precision_minor
}
else:
metrics = {'obj/accuracy': accuracy}
examples_sec_hook = utils.ExamplesPerSecondHook(
params.train_batch_size, every_n_steps=params.log_freq)
tensors_to_log = {
'Target loss': target_loss,
'Main loss': base_model_loss_reduced,
'Target accuracy': target_accuracy[1],
'Main accuracy': accuracy[1],
'learning_rates': learning_rate,
'step': tf.compat.v1.train.get_global_step()
}
logging_hook = tf.estimator.LoggingTensorHook(tensors=tensors_to_log,
every_n_iter=params.log_freq)
train_hooks = [weight_update_hook, logging_hook, examples_sec_hook]
train_op.extend(update_ops)
train_op = tf.group(*train_op)
return tf.estimator.EstimatorSpec(mode=mode,
predictions=target_preds,
loss=base_model_loss_reduced,
train_op=train_op,
training_hooks=train_hooks,
eval_metric_ops=metrics)
'eurosat': ['resnet56']
}
attack_names = ['fgsm', 'pgd', 'deepfool', 'bim', 'apgd','cw']
### add combinations into queues
manager = multiprocessing.Manager()
q = manager.Queue()
for attack_name in attack_names:
for dataset in datasets:
for model_name in model_dict[dataset]:
q.put((attack_name, dataset, model_name))
p_list = []
for i in range(len(get_available_gpus())):
gpu_id = i
p = multiprocessing.Process(target=train, args=(str(gpu_id), ))
p_list.append(p)
p.start()
for i in p_list:
i.join()
print("All processed finished.")
patience=int(patience / 4),
verbose=1)
class MyCbk(keras.callbacks.Callback):
def __init__(self, model):
keras.callbacks.Callback.__init__(self)
self.model_to_save = model
def on_epoch_end(self, epoch, logs=None):
fmt = checkpoint_models_path + 'model.%02d-%.4f.hdf5'
highest_acc = get_highest_acc()
if float(logs['val_acc']) > highest_acc:
self.model_to_save.save(fmt % (epoch, logs['val_acc']))
# Load our model, added support for Multi-GPUs
num_gpu = len(get_available_gpus())
if num_gpu >= 2:
with tf.device("/cpu:0"):
model = build_model()
if pretrained_path is not None:
model.load_weights(pretrained_path)
new_model = multi_gpu_model(model, gpus=num_gpu)
# rewrite the callback: saving through the original model and not the multi-gpu model.
model_checkpoint = MyCbk(model)
else:
new_model = build_model()
if pretrained_path is not None:
new_model.load_weights(pretrained_path)
adam = keras.optimizers.Adam(lr=1e-4,
def train(data_dict, emebedding_path, language):
# pre-process train data
# data_dict = to_normal(data_dict)
MAX_NB_WORDS = 100000
max_seq_len = 1000
# load train data
raw_docs_train = [data['content'] for data in data_dict]
labels = [data['steam_weight'] for data in data_dict]
word_seq, word_index = kt_tokenizer(raw_docs_train, language, MAX_NB_WORDS,
max_seq_len)
print('{0} reviews in {1}'.format(len(word_seq), language))
print('loading word embeddings...')
emebedding_path = EMBEDDING_DIR + embedding_model_path(language) # TODO
embeddings_index = {}
f = codecs.open(emebedding_path, encoding='utf-8')
for line in f:
values = line.rstrip().rsplit(' ')
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
f.close()
print('found %s word vectors' % len(embeddings_index))
y_all = np.array(labels)
# training params
batch_size = 256
num_epochs = 100
num_gpus = get_available_gpus()
# model parameters
num_filters = 32
embed_dim = 300
weight_decay = 1e-4
learning_rate = 0.001
# output parameters
num_classes = 4
# split data
split_persentage = 0.8
split_index = int(len(word_seq) * split_persentage)
word_seq_train = word_seq[:split_index]
word_seq_test = word_seq[split_index:]
y_train = y_all[:split_index]
y_test = y_all[split_index:]
# embedding matrix
print('preparing embedding matrix...')
words_not_found = []
nb_words = min(MAX_NB_WORDS, len(word_index))
embedding_matrix = np.zeros((nb_words, embed_dim))
for word, i in word_index.items():
if i >= nb_words:
continue
embedding_vector = embeddings_index.get(word)
if (embedding_vector is not None) and len(embedding_vector) > 0:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
else:
words_not_found.append(word)
print('number of null word embeddings: %d' %
np.sum(np.sum(embedding_matrix, axis=1) == 0))
print("sample words not found: ", np.random.choice(words_not_found, 20))
# CNN architecture
print("Defining CNN ...")
model = Sequential()
model.add(
Embedding(nb_words,
embed_dim,
weights=[embedding_matrix],
input_length=max_seq_len,
trainable=False))
model.add(Conv1D(num_filters, 7, activation='relu', padding='same'))
model.add(MaxPooling1D(2))
model.add(Conv1D(num_filters, 7, activation='relu', padding='same'))
model.add(GlobalMaxPooling1D())
# model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))
# model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(weight_decay)))
# model.add(Dropout(0.5))
model.add(
Dense(16,
activation='relu',
kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
adam = optimizers.Adam(lr=learning_rate,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
decay=0.0)
try:
model = multi_gpu_model(model, gpus=num_gpus)
print("Training using {0} GPUs..".format(num_gpus))
except:
print("Training using single GPU or CPU..")
model.compile(loss='binary_crossentropy',
optimizer=adam,
metrics=['accuracy'])
print(model.summary())
# save model
filepath = 'models/' + language + '.' + 'weights.ep{epoch:03d}.loss{loss:.3f}.val_loss{val_loss:.3f}.h5'
checkpoint = ModelCheckpoint(filepath,
monitor='val_loss',
verbose=0,
save_best_only=True,
save_weights_only=False,
mode='auto',
period=1)
callbacks_list = [checkpoint]
# model training
hist = model.fit(word_seq_train,
y_train,
batch_size=batch_size,
epochs=num_epochs,
callbacks=callbacks_list,
validation_split=0.1,
shuffle=True,
verbose=2)
# plot loss
train_history = hist
loss = train_history.history['loss']
val_loss = train_history.history['val_loss']
plt.title(language + ' ' + 'model')
plt.plot(loss)
plt.plot(val_loss)
plt.legend(['loss', 'val_loss'])
plt.show()
# plot y_predict
plt.title(language + ' ' + 'predict')
plt.plot(y_test)
plt.plot(model.predict(word_seq_test))
plt.legend(['y_text', 'y_predict'])
plt.show()
