def main(argv):
"""Implement a simple demo for computing error CDFs."""
# Input/output flags.
gflags.DEFINE_string('input_file', None, 'Full path to wing HDF5 log file.')
gflags.MarkFlagAsRequired('input_file')
gflags.DEFINE_string('output_file', None, 'Full path to output MAT file.')
gflags.MarkFlagAsRequired('output_file')
# Segment processing flags.
gflags.DEFINE_integer('increment', 100,
'Integer number of messages between segments.')
gflags.DEFINE_integer('seg_length', 1000,
'Integer number of messages in each segment.')
# Evaluate segments over a specific time interval.
gflags.DEFINE_float('start_time', -float('inf'),
'Start time to evaluate segment errors.')
gflags.DEFINE_float('end_time', float('inf'),
'End time to evaluate segment errors.')
# Override default parameters.
gflags.DEFINE_list('params', [],
'A comma-separated list of param=value tokens, where '
'each param describes the dot path to a parameter in '
'EstimatorParams.')
gflags.RegisterValidator('params',
lambda l: all(len(s.split('=')) == 2 for s in l),
message='Invalid key=value parameter syntax.')
# Scenarios to process.
gflags.DEFINE_bool('scenario_pure_inertial', False,
'Process pure inertial scenario.')
gflags.DEFINE_bool('scenario_gps_dropout', False,
'Process GPS dropout scenario.')
# Common faults to introduce.
gflags.DEFINE_bool('fault_weather', False,
'Fault weather subsystems to avoid an assert when '
'reprocessing historical data.')
gflags.DEFINE_bool('fault_glas', False, 'Fault GLAS subsystems.')
# Specify flight for special handling.
gflags.DEFINE_string('flight', None,
'Fix known issues associated with the given flight.')
try:
argv = gflags.FLAGS(argv)
except gflags.FlagsError, e:
print '{}\nUsage: {} ARGS\n{}'.format(e, sys.argv[0], gflags.FLAGS)
sys.exit(1)
def flags():
# Debug settings
gflags.DEFINE_string("branch", None, "")
gflags.DEFINE_string("sha", None, "")
gflags.DEFINE_boolean("debug", False, "")
# Performance settings
gflags.DEFINE_boolean("cuda", False, "")
# Display settings
gflags.DEFINE_string("env", "main", "")
gflags.DEFINE_string("experiment_name", None, "")
# Data settings
gflags.DEFINE_string("descr_train", "./utils/descriptions.csv", "")
gflags.DEFINE_string("descr_dev", "./utils/descriptions.csv", "")
gflags.DEFINE_string("train_data", "./utils/imgs/train", "")
gflags.DEFINE_string("dev_data", "./utils/imgs/dev", "")
gflags.DEFINE_integer("word_embedding_dim", 100, "")
gflags.DEFINE_string("word_embedding_path",
"~/data/glove/glove.6B.100d.txt", "")
# Optimization settings
gflags.DEFINE_enum("optim_type", "RMSprop", ["Adam", "SGD", "RMSprop"], "")
gflags.DEFINE_integer("batch_size", 32, "Minibatch size for train set.")
gflags.DEFINE_integer("batch_size_dev", 50, "Minibatch size for dev set.")
gflags.DEFINE_float("learning_rate", 1e-4, "Used in optimizer.")
gflags.DEFINE_integer("max_epoch", 500, "")
def args():
default_experiment_name = "exp-{}".format(int(time.time()))
gflags.DEFINE_enum("preset", None, ["demo"], "Easily set configuration.")
gflags.DEFINE_string("data_path", os.path.expanduser("~/data/multinli_0.9/multinli_0.9_dev_matched.jsonl"), "Path to NLI data.")
gflags.DEFINE_string("eval_data_path", os.path.expanduser("~/data/multinli_0.9/multinli_0.9_dev_matched.jsonl"), "Path to NLI data.")
gflags.DEFINE_string("embedding_path", os.path.expanduser("~/data/glove.840B.300d.txt"), "Path to GloVe vectors.")
gflags.DEFINE_integer("batch_size", 100, "Batch size.")
gflags.DEFINE_integer("input_dim", 300, "Word embedding dimension.")
gflags.DEFINE_integer("hidden_dim", 300, "Hidden representation dimension.")
gflags.DEFINE_string("save_path", ".", "Path to logs and checkpoints.")
gflags.DEFINE_string("load_path", None, "Path to load checkpoint.")
gflags.DEFINE_string("log_path", None, "Path to log.")
gflags.DEFINE_string("experiment_name", default_experiment_name, "Experiment name.")
gflags.DEFINE_float("l2", None, "Use l2 regularization.")
gflags.DEFINE_boolean("extract", False, "Use pretrained model to calculate query and target vectors for input data.")
gflags.DEFINE_integer("seed", 11, "Random seed.")
FLAGS(sys.argv)
presets()
if not FLAGS.load_path:
FLAGS.load_path = FLAGS.save_path # this way we use logs/ckpt for an experiment_name if it exists.
if not FLAGS.log_path:
FLAGS.log_path = os.path.join('.', FLAGS.experiment_name + '.log')
logger = MainLogger().init(path=FLAGS.log_path)
logger.Log(json.dumps(FLAGS.FlagValuesDict(), indent=4, sort_keys=True))
def get_flags():
# Debug settings.
gflags.DEFINE_string("data_dir", "cmu",
"dir containing train.txt, test.txt, valid.txt")
gflags.DEFINE_string("log_path", "logs", "")
gflags.DEFINE_string("data_type", "discriminator",
"figure out how to use this")
gflags.DEFINE_enum("model_type", "LSTM", ["LSTM", "BiLSTM", "DEEP"],
"options: LSTM, BiLSTM, DEEP, ...")
gflags.DEFINE_string("ckpt_path", "checkpoints", "")
gflags.DEFINE_boolean("gpu", False, "set to false on local")
gflags.DEFINE_string("experiment_name", "", "")
gflags.DEFINE_boolean("evaluate_only", False, "")
#sizes
gflags.DEFINE_integer("embedding_size", 29, "hardcoded for simplicity")
gflags.DEFINE_integer("reduction_size", 40, "hardcoded for simplicity")
gflags.DEFINE_integer("crop_pad_length", 30, "")
#chunks
gflags.DEFINE_integer("stages_per_epoch", 40,
"how many eval/stats steps per epoch?")
gflags.DEFINE_integer("prints_per_stage", 1,
"how often to print stats to stdout during epoch")
gflags.DEFINE_integer("convergence_threshold", 50,
"how many eval steps before early stop")
gflags.DEFINE_integer(
"max_epochs", 100,
"number of epochs before stop, essentially unreachable")
gflags.DEFINE_integer("batch_size", 64, "")
#tunable parameters
gflags.DEFINE_integer("hidden_size", 1024, "")
gflags.DEFINE_integer("num_layers", 1, "")
gflags.DEFINE_float("learning_rate", .002, "")
def _set_flags():
gflags.DEFINE_boolean("predict_hyp", False, "train to predict hypotheses")
gflags.DEFINE_boolean("infer_hyp", False, "use hypotheses at test time")
gflags.DEFINE_boolean("infer_by_likelihood", False, "use likelihood (rather than accuracy) to rank hypotheses")
gflags.DEFINE_boolean("use_true_hyp", False, "predict using ground-truth description")
gflags.DEFINE_integer("n_sample_hyps", 5, "number of hypotheses to sample")
gflags.DEFINE_float("learning_rate", 0.001, "learning rate")
gflags.DEFINE_string("restore", None, "model to restore")
gflags.DEFINE_boolean("use_true_eval", False, "score with true evaluation function")
gflags.DEFINE_boolean("use_task_hyp", False, "task as hypothesis")
def _set_flags():
gflags.DEFINE_boolean("predict_hyp", False, "train to predict hypotheses")
gflags.DEFINE_boolean("infer_hyp", False, "use hypotheses at test time")
gflags.DEFINE_string("restore", None, "model to restore")
gflags.DEFINE_float("concept_prior", None,
"place a normal prior on concept representations")
gflags.DEFINE_integer(
"adapt_reprs", 100,
"number of representations to sample when doing adaptation")
gflags.DEFINE_integer(
"adapt_samples", 1000,
"number of episodes to spend evaluating sampled representations")
def __init__(self, name, flag_values):
super(SetTargetPoolBackup, self).__init__(name, flag_values)
flags.DEFINE_float(
'failover_ratio',
None, '--failover_ratio and --backup_pool must either be '
'both set or not set. If not set, existing failover '
'ratio will be removed from the target pool, which will '
'disable the fallback behavior of the primary target '
'pool. If set, the failover ratio of the primary target '
'pool will be replaced by this value.',
flag_values=flag_values)
flags.DEFINE_string(
'backup_pool',
None, '--backup_pool and --failover_ratio must either be '
'both set or not set. If not set, existing backup '
'pool will be removed from the target pool, which will '
'disable the fallback behavior of the primary target '
'pool. If set, the backup pool of the primary target '
'pool will be replaced by this value.',
flag_values=flag_values)
def load_defaults():
### Experiment Parameters
gflags.DEFINE_integer(
'num_experiments', 1,
'the number of times to train the model (with different initialization)'
)
gflags.DEFINE_integer(
'seed', 1, 'a random seed used in all randomized model initialization')
### Data Parameters
gflags.DEFINE_string('train_data', 'data/NTC_1.5/processed/train.utf8',
'the path to the train data file')
gflags.DEFINE_string('test_data', 'data/NTC_1.5/processed/test.utf8',
'the path to the test data file')
gflags.DEFINE_string('dev_data', 'data/NTC_1.5/processed/dev.utf8',
'the path to the dev data file')
gflags.DEFINE_integer(
'max_train_instances', 100000,
'max number of instances to read for each train/test/dev set')
### Model Parameters
## Representation
gflags.DEFINE_boolean(
'use_context', True,
'construct the representation from LSTM context embeddings')
gflags.DEFINE_boolean(
'use_sp', True,
'construct the representation directly from pred/arg embeddings')
gflags.DEFINE_integer('context_dims', 20,
'output dimension for LSTM context embeddings')
gflags.DEFINE_integer(
'sp_dims', 8,
'dimensionality of arg embedding for selectional preference')
gflags.DEFINE_integer('num_context_layers', 2,
'number of layers in the context LSTM')
gflags.DEFINE_float(
'context_dropout', 0.0,
'amount of dropout on each LSTM layer in the context representation')
"""Provide row of input image.""")
gflags.DEFINE_integer('input_col', 448,
"""Provide col of input image.""")
gflags.DEFINE_integer('output_row', 14,
"""Provide row of output shape.""")
gflags.DEFINE_integer('output_col', 14,
"""Provide col of output shape.""")
gflags.DEFINE_integer('num_class', 20,
"""Number of class of dataset.""")
gflags.DEFINE_string('eval_dir', 'result',
"""Directory where to write result files..""")
gflags.DEFINE_string('checkpoint_path', 'backup/model.ckpt-50000',
"""Model file after training.""")
gflags.DEFINE_string('gpu_list', '1',
"""GPU list, such as '0, 1, ....'.""")
gflags.DEFINE_float('gpu_usage', 0.8,
"""Per process gpu memory fraction.""")
CLASSES = []
COLORS = [(0, 255, 0), (0, 0, 255), (241, 90, 36),
(235, 0, 139), (0, 159, 255), (223, 255, 0),
(237, 34, 42), (180, 58, 228), (247, 147, 30), (2, 254, 207)]
COUNT = -1
def save_result(im_id, im_sz, boxes_list, eval_dir):
global COUNT
COUNT += 1
def get_flags():
gflags.DEFINE_enum("model_type", "transup", ["transup", "bprmf", "fm",
"transe", "transh", "transr", "transd",
"cfkg", "cke", "cofm", "jtransup"], "")
gflags.DEFINE_enum("dataset", "ml1m", ['kktix', "ml1m", "dbbook2014", "amazon-book", "last-fm", "yelp2018"], "including ratings.csv, r2kg.tsv and a kg dictionary containing kg_hop[0-9].dat")
gflags.DEFINE_bool(
"filter_wrong_corrupted",
True,
"If set to True, filter test samples from train and validations.")
gflags.DEFINE_bool("share_embeddings", False, "")
gflags.DEFINE_bool("use_st_gumbel", False, "")
gflags.DEFINE_integer("max_queue", 10, ".")
gflags.DEFINE_integer("num_processes", 40, ".")
gflags.DEFINE_float("learning_rate", 0.001, "Used in optimizer.")
gflags.DEFINE_float("norm_lambda", 1.0, "decay of joint model.")
gflags.DEFINE_float("kg_lambda", 1.0, "decay of kg model.")
gflags.DEFINE_integer(
"early_stopping_steps_to_wait",
70000,
"How many times will lr decrease? If set to 0, it remains constant.")
gflags.DEFINE_bool(
"L1_flag",
False,
"If set to True, use L1 distance as dissimilarity; else, use L2.")
gflags.DEFINE_bool(
"is_report",
False,
"If set to True, use L1 distance as dissimilarity; else, use L2.")
gflags.DEFINE_float("l2_lambda", 1e-5, "")
gflags.DEFINE_integer("embedding_size", 64, ".")
gflags.DEFINE_integer("negtive_samples", 1, ".")
gflags.DEFINE_integer("batch_size", 512, "Minibatch size.")
gflags.DEFINE_enum("optimizer_type", "Adagrad", ["Adam", "SGD", "Adagrad", "Rmsprop"], "")
gflags.DEFINE_float("learning_rate_decay_when_no_progress", 0.5,
"Used in optimizer. Decay the LR by this much every epoch steps if a new best has not been set in the last epoch.")
gflags.DEFINE_integer(
"eval_interval_steps",
14000,
"Evaluate at this interval in each epoch.")
gflags.DEFINE_integer(
"training_steps",
1400000,
"Stop training after this point.")
gflags.DEFINE_float("clipping_max_value", 5.0, "")
gflags.DEFINE_float("margin", 1.0, "Used in margin loss.")
gflags.DEFINE_float("momentum", 0.9, "The momentum of the optimizer.")
gflags.DEFINE_integer("seed", 0, "Fix the random seed. Except for 0, which means no setting of random seed.")
gflags.DEFINE_integer("topn", 10, "")
gflags.DEFINE_integer("num_preferences", 4, "")
gflags.DEFINE_float("joint_ratio", 0.5, "(0 - 1). The train ratio of recommendation, kg is 1 - joint_ratio.")
gflags.DEFINE_string("experiment_name", None, "")
gflags.DEFINE_string("data_path", None, "")
gflags.DEFINE_string("rec_test_files", None, "multiple filenames separated by ':'.")
gflags.DEFINE_string("kg_test_files", None, "multiple filenames separated by ':'.")
gflags.DEFINE_string("log_path", None, "")
gflags.DEFINE_enum("log_level", "debug", ["debug", "info"], "")
gflags.DEFINE_string(
"ckpt_path", None, "Where to save/load checkpoints. If not set, the same as log_path")
gflags.DEFINE_string(
"load_ckpt_file", None, "Where to load pretrained checkpoints under log path. multiple filenames separated by ':'.")
gflags.DEFINE_boolean(
"has_visualization",
True,
"if set True, use visdom for visualization.")
gflags.DEFINE_integer("visualization_port", 8097, "")
# todo: only eval when no train.dat when load data
gflags.DEFINE_boolean(
"eval_only_mode",
False,
"If set, a checkpoint is loaded and a forward pass is done to get the predicted candidates."
"Requirements: Must specify load_experiment_name.")
gflags.DEFINE_string("load_experiment_name", None, "")
# Random seed
gflags.DEFINE_bool('random_seed', True, 'Random seed')
# Input
gflags.DEFINE_integer('num_img', 45, 'Target Gesture Length')
gflags.DEFINE_integer('img_width', 100, 'Target Image Width')
gflags.DEFINE_integer('img_height', 100, 'Target Image Height')
gflags.DEFINE_string('img_mode', "grayscale", 'Load mode for images, either '
'rgb or grayscale')
# Training parameters
gflags.DEFINE_integer('batch_size', 64, 'Batch size in training and evaluation')
gflags.DEFINE_integer('epochs', 15, 'Number of epochs for training')
gflags.DEFINE_integer('initial_epoch', 0, 'Initial epoch to start training')
gflags.DEFINE_float('initial_lr', 1e-4, 'Initial learning rate for adam')
# Files
gflags.DEFINE_string('experiment_rootdir', "./models/test_5", 'Folder '
' containing all the logs, model weights and results')
gflags.DEFINE_string('data_path', "./ProcessedData",
'Folder containing the whole dataset')
gflags.DEFINE_string('video_dir', "../video_1", 'Folder containing'
' only one experiment to be processed')
gflags.DEFINE_string('exp_name', "exp_1", 'Name of the experiment'
' to be processed')
# Model
gflags.DEFINE_bool('restore_model', True, 'Whether to restore a trained'
' model for training')
gflags.DEFINE_string('weights_fname', './models/test_3/weights_050.h5',
import gflags
import logging
import numpy as np
from needle.agents import BasicAgent, register_agent
from needle.agents.TNPG.net import Net
from needle.agents.TRPO.critic import Critic
from needle.helper.conjugate_gradient import conjugate_gradient
from needle.helper.OU_process import OUProcess
from needle.helper.softmax_sampler import SoftmaxSampler
from needle.helper.batcher import Batcher
from needle.helper.utils import decay_cumsum, softmax
gflags.DEFINE_float("GAE_lambda", 0.98, "GAE lambda")
gflags.DEFINE_float("critic_eps", 0.01, "critic's trust region")
gflags.DEFINE_float("line_search_decay", 0.7, "line search's decay factor")
FLAGS = gflags.FLAGS
@register_agent("TRPO")
class Agent(SoftmaxSampler, Batcher, BasicAgent):
def __init__(self, input_dim, output_dim):
self.input_dim = input_dim
self.output_dim = output_dim
self.counter = 0
self.net = Net(input_dim, output_dim)
self.net.build_infer()
self.net.build_train()
self.critic = Critic(input_dim)
def __init__(self, name, flag_values):
super(AddTargetPool, self).__init__(name, flag_values)
flags.DEFINE_string('description',
'',
'An optional Target Pool description',
flag_values=flag_values)
flags.DEFINE_list(
'health_checks', [],
'Specifies a HttpHealthCheck resource to use to '
'determine the health of VMs in this pool. '
'If no health check is specified, traffic will be '
'sent to all instances in this target pool as if the '
'instances were healthy, but the health status of this '
'pool will appear as unhealthy as a warning that this '
'target pool does not have a health check.',
flag_values=flag_values)
flags.DEFINE_list(
'instances', [],
'[Required] Specifies a list of instances that will '
'receive traffic directed to this target pool. Each '
'entry must be specified by the instance name '
'(e.g., \'--instances=myinstance\') or a relative or '
'fully-qualified path to the instance (e.g., '
'\'--instances=<zone>/instances/myotherinstance\'). To '
'specify multiple instances, provide them as '
'comma-separated entries. All instances in one target '
'pool must belong to the same region as the target pool. '
'Instances do not need to exist at the time the target '
'pool is created and can be created afterwards.',
flag_values=flag_values)
gcutil_flags.DEFINE_case_insensitive_enum(
'session_affinity',
self.DEFAULT_SESSION_AFFINITY,
['NONE', 'CLIENT_IP', 'CLIENT_IP_PROTO'],
'Specifies the session affinity option for the '
'connection. Options include:'
'\n NONE: connections from the same client IP '
'may go to any VM in the target pool '
'\n CLIENT_IP: connections from the same client IP '
'will go to the same VM in the target pool; '
'\n CLIENT_IP_PROTO: connections from the same '
'client IP with the same IP protocol will go to the '
'same VM in the targetpool. ',
flag_values=flag_values)
flags.DEFINE_float(
'failover_ratio',
None, 'If set, --backup_pool must also be set to point to an '
'existing target pool in the same region. They together '
'define the fallback behavior of the target pool '
'(primary pool) to be created by this command: if the '
'ratio of the healthy VMs in the primary pool is at '
'or below this number, traffic arriving at the '
'load-balanced IP will be directed to the backup pool. '
'If not set, the traaffic will be directed the VMs in '
'this pool in the "force" mode, where traffic will be '
'spread to the healthy VMs with the best effort, or '
'to all VMs when no VM is healthy.',
flag_values=flag_values)
flags.DEFINE_string(
'backup_pool',
None, 'Together with --failover_ratio, this flag defines '
'the fallback behavior of the target pool '
'(primary pool) to be created by this command: if the '
'ratio of the healthy VMs in the primary pool is at '
'or below --failover_ratio, traffic arriving at the '
'load-balanced IP will be directed to the backup '
'pool. ',
flag_values=flag_values)
import gflags
FLAGS = gflags.FLAGS
# Dataset selection parameters
gflags.DEFINE_string('trajectory_name', 'bentDice', 'The name of the trajectory to use from the dataset')
gflags.DEFINE_string('yaw_type', 'yawForward', 'The yaw type to use from the dataset')
gflags.DEFINE_float('max_speed', 2.0, 'The maximum speed of the drone in the dataset')
import gflags
FLAGS = gflags.FLAGS
# Random seed
gflags.DEFINE_bool('random_seed', True, 'Random seed')
# Input
gflags.DEFINE_integer('target_level', -14, 'Target Level dB')
gflags.DEFINE_float('target_nu', 0.1995262315, 'Target Level nu')
gflags.DEFINE_integer('sr', 44100, 'Sample Rate')
gflags.DEFINE_integer('img_width', 862, 'Target Image Width')
gflags.DEFINE_integer('img_height', 96, 'Target Image Height')
gflags.DEFINE_string('img_mode', "rgb",
'Load mode for images, either rgb or grayscale')
gflags.DEFINE_string('h_grid', 50, 'Horizontal size of the grid classifiers')
gflags.DEFINE_string('v_grid', 25, 'Vertical size of the grid classifiers')
# Training parameters
gflags.DEFINE_integer('batch_size', 128,
'Batch size in training and evaluation')
gflags.DEFINE_integer('epochs', 100, 'Number of epochs for training')
gflags.DEFINE_integer('verbose', 1, 'Type of verbose for training')
gflags.DEFINE_integer('initial_epoch', 0, 'Initial epoch to start training')
gflags.DEFINE_float('initial_lr', 1e-3, 'Initial learning rate for adam')
gflags.DEFINE_string('f_output', 'softmax', 'Output function')
# Testing parameters
gflags.DEFINE_float('IOU', 0.5, 'Threshold for the IoU')
gflags.DEFINE_float('NMS', 0.3, 'Threshold for the NMS')
gflags.DEFINE_float(
'url to which output is posted. The url must include param name, '
'value for which is populated with task_id from puller while posting '
'the data. Format of output url is absolute url which handles the'
'post request from task queue puller.'
'(Eg: "http://taskpuller.appspot.com/taskdata?name=").'
'The Param value is always the task_id. The handler for this post'
'should be able to associate the task with its id and take'
'appropriate action. Use the appengine_access_token.py tool to'
'generate the token and store it in a file before you start.')
flags.DEFINE_string(
'appengine_access_token_file', None,
'File containing an Appengine Access token, if any. If present this'
'token is added to the output_url request, so that the output_url can'
'be an authenticated end-point. Use the appengine_access_token.py tool'
'to generate the token and store it in a file before you start.')
flags.DEFINE_float('task_timeout_secs', '3600', 'timeout to kill the task')
class ClientTaskInitError(Exception):
"""Raised when initialization of client task fails."""
def __init__(self, task_id, error_str):
Exception.__init__(self)
self.task_id = task_id
self.error_str = error_str
def __str__(self):
return ('Error initializing task "%s". Error details "%s". ' %
(self.task_id, self.error_str))
class ClientTask(object):
FLAGS = flags.FLAGS
flags.DEFINE_string("train", "data/joint/shuffle.train.txt",
"training data")
flags.DEFINE_string("dev", "data/joint/shuffle.dev.txt", "development data")
flags.DEFINE_string("save", None, "model saving path")
flags.DEFINE_integer("epoch", 30, "number of training epochs")
flags.DEFINE_integer("batch", 1, "Minibatch size")
flags.DEFINE_bool("extlabelfeatures", True,
"extend label features to include two children of s0")
# arguments for span-based parser
flags.DEFINE_integer("dynet_mem", 10000,
"Memory allocation for Dynet. (DEFAULT=10000)")
flags.DEFINE_float("dynet_l2", 0, "L2 regularization parmeter. (DEFAULT=0)")
flags.DEFINE_integer("dynet_seed", 123, "Seed for PNG (0: generate)")
flags.DEFINE_integer("word_dims", 50,
"Embedding dimensions for word forms. (DEFAULT=50)")
flags.DEFINE_integer("tag_dims", 20,
"Embedding dimensions of POS tags. (DEFAULT=2-)")
flags.DEFINE_integer("lstm_units", 200,
"Number of LSTM units in each layer/direction. "
"(DEFAULT=200)")
flags.DEFINE_integer("hidden_units", 200,
"Number of hidden units for each FC ReLU layer. "
"(DEFAULT=200)")
flags.DEFINE_float("droprate", 0.5, "Drouput probability. (DEFAULT=0.5)")
flags.DEFINE_float("unk_param", 0.8375,
"Parameter z for random UNKing. (DEFAULT=0.8375)")
flags.DEFINE_float("alpha", 1.0,
import codecs
import sys
import re
import gflags
FLAGS = gflags.FLAGS
gflags.DEFINE_string("dictfile", None,
"dict file with frequencies and reverse-sorted")
gflags.DEFINE_integer("topn", None,
"Top N frequency tokens u want to filter the data with")
gflags.DEFINE_float("unkratio", 0.2,
"unk token ratio limit for each sentence to filter")
gflags.MarkFlagAsRequired('dictfile')
gflags.MarkFlagAsRequired('topn')
try:
FLAGS(sys.argv)
except gflags.FlagsError as e:
print "\n%s" % e
print FLAGS.GetHelp(include_special_flags=False)
sys.exit(1)
dictname = FLAGS.dictfile
topn = FLAGS.topn
unkratio = FLAGS.unkratio
dic = {}
count = 0
with codecs.open(dictname) as f:
for line in f:
import os
import glob
import sys
import gflags
from rmp_nav.simulation import agent_factory
from rmp_nav.common.utils import get_model_dir, get_gibson_asset_dir, get_config_dir, get_data_dir
from topological_nav.controller import inference
from topological_nav.controller.dataset import DatasetSourceTargetPairMultiframeDst
from topological_nav.tools.eval_controller_common import EvaluatorMultiframeDst
gflags.DEFINE_string('env', 'space8', '')
gflags.DEFINE_integer('start_idx', 0, '')
gflags.DEFINE_integer('n_traj', 200, '')
gflags.DEFINE_boolean('visualize', True, '')
gflags.DEFINE_boolean('save_screenshot', False, '')
gflags.DEFINE_float('zoom', 1.0, '')
FLAGS = gflags.FLAGS
FLAGS(sys.argv)
weights_file = os.path.join(
get_model_dir(),
'topological_nav/controller/multiframe_dst/gtwp-normwp-farwp-jitter-weightedloss-checkwp-nf6-interval3-dmax3-z0228-model.8'
)
_env_dict = {}
def register(f):
_env_dict[f.__name__] = f
return f
def get_flags():
# Debug settings.
gflags.DEFINE_bool(
"debug",
False,
"Set to True to disable debug_mode and type_checking.")
gflags.DEFINE_bool(
"show_progress_bar",
True,
"Turn this off when running experiments on HPC.")
gflags.DEFINE_string("git_branch_name", "", "Set automatically.")
gflags.DEFINE_string("slurm_job_id", "", "Set automatically.")
gflags.DEFINE_integer(
"deque_length",
100,
"Max trailing examples to use when computing average training statistics.")
gflags.DEFINE_string("git_sha", "", "Set automatically.")
gflags.DEFINE_string("experiment_name", "", "")
gflags.DEFINE_string("load_experiment_name", None, "")
# Data types.
gflags.DEFINE_enum("data_type",
"bl",
["bl",
"sst",
"sst-binary",
"nli",
"arithmetic",
"listops",
"sign",
"eq",
"relational"],
"Which data handler and classifier to use.")
# Choose Genre.
# 'fiction', 'government', 'slate', 'telephone', 'travel'
# 'facetoface', 'letters', 'nineeleven', 'oup', 'verbatim'
gflags.DEFINE_string("train_genre", None, "Filter MultiNLI data by genre.")
gflags.DEFINE_string("eval_genre", None, "Filter MultiNLI data by genre.")
# Where to store checkpoints
gflags.DEFINE_string(
"log_path",
"./logs",
"A directory in which to write logs.")
gflags.DEFINE_string(
"load_log_path",
None,
"A directory from which to read logs.")
gflags.DEFINE_boolean(
"write_proto_to_log",
False,
"Write logs in a protocol buffer format.")
gflags.DEFINE_string(
"ckpt_path", None, "Where to save/load checkpoints. Can be either "
"a filename or a directory. In the latter case, the experiment name serves as the "
"base for the filename.")
gflags.DEFINE_integer(
"ckpt_step",
1000,
"Steps to run before considering saving checkpoint.")
gflags.DEFINE_boolean(
"load_best",
False,
"If True, attempt to load 'best' checkpoint.")
# Data settings.
gflags.DEFINE_string("training_data_path", None, "")
gflags.DEFINE_string(
"eval_data_path", None, "Can contain multiple file paths, separated "
"using ':' tokens. The first file should be the dev set, and is used for determining "
"when to save the early stopping 'best' checkpoints.")
gflags.DEFINE_integer("seq_length", 200, "")
gflags.DEFINE_boolean(
"allow_cropping",
False,
"Trim overly long training examples to fit. If not set, skip them.")
gflags.DEFINE_integer("eval_seq_length", None, "")
gflags.DEFINE_boolean(
"allow_eval_cropping",
False,
"Trim overly long evaluation examples to fit. If not set, crash on overly long examples.")
gflags.DEFINE_boolean(
"smart_batching",
True,
"Organize batches using sequence length.")
gflags.DEFINE_boolean("use_peano", True, "A mind-blowing sorting key.")
gflags.DEFINE_integer(
"eval_data_limit",
None,
"Truncate evaluation set to this many batches. -1 indicates no truncation.")
gflags.DEFINE_boolean(
"bucket_eval",
True,
"Bucket evaluation data for speed improvement.")
gflags.DEFINE_boolean("shuffle_eval", False, "Shuffle evaluation data.")
gflags.DEFINE_integer(
"shuffle_eval_seed",
123,
"Seed shuffling of eval data.")
gflags.DEFINE_string("embedding_data_path", None,
"If set, load GloVe-formatted embeddings from here.")
gflags.DEFINE_boolean("fine_tune_loaded_embeddings", False,
"If set, backpropagate into embeddings even when initializing from pretrained.")
# Model architecture settings.
gflags.DEFINE_enum(
"model_type", "RNN", [
"CBOW", "RNN", "SPINN", "RLSPINN", "ChoiPyramid", "Maillard", "LMS"], "")
gflags.DEFINE_integer("gpu", -1, "")
gflags.DEFINE_integer("model_dim", 8, "")
gflags.DEFINE_integer("word_embedding_dim", 8, "")
gflags.DEFINE_boolean("lowercase", False, "When True, ignore case.")
gflags.DEFINE_boolean("use_internal_parser", False, "Use predicted parse.")
gflags.DEFINE_boolean(
"validate_transitions",
True,
"Constrain predicted transitions to ones that give a valid parse tree.")
gflags.DEFINE_float(
"embedding_keep_rate",
1.0,
"Used for dropout on transformed embeddings and in the encoder RNN.")
gflags.DEFINE_boolean("use_difference_feature", True, "")
gflags.DEFINE_boolean("use_product_feature", True, "")
# SPINN tracking LSTM settings.
gflags.DEFINE_integer(
"tracking_lstm_hidden_dim",
None,
"Set to none to avoid using tracker.")
gflags.DEFINE_boolean(
"tracking_ln",
False,
"When True, layer normalization is used in tracking.")
gflags.DEFINE_float(
"transition_weight",
None,
"Set to none to avoid predicting transitions.")
gflags.DEFINE_boolean("lateral_tracking", True,
"Use previous tracker state as input for new state.")
gflags.DEFINE_boolean(
"use_tracking_in_composition",
True,
"Use tracking lstm output as input for the reduce function.")
gflags.DEFINE_boolean(
"composition_ln",
True,
"When True, layer normalization is used in TreeLSTM composition.")
gflags.DEFINE_boolean("predict_use_cell", True,
"Use cell output as feature for transition net.")
# SPINN composition function settings.
gflags.DEFINE_enum(
"reduce", "treelstm", [
"treelstm", "treegru", "tanh", "lms"], "Specify composition function.")
# ChoiPyramid/ST-Gumbel model settings
gflags.DEFINE_boolean(
"pyramid_trainable_temperature",
None,
"If set, add a scalar trained temperature parameter.")
gflags.DEFINE_float("pyramid_temperature_decay_per_10k_steps",
0.5, "What it says on the box. Does not impact SparseAdam (for word embedding fine-tuning).")
gflags.DEFINE_float(
"pyramid_temperature_cycle_length",
0.0,
"For wake-sleep-style experiments. 0.0 disables this feature.")
# Embedding preprocessing settings.
gflags.DEFINE_enum("encode",
"projection",
["pass",
"projection",
"gru",
"attn"],
"Encode embeddings with sequential context.")
gflags.DEFINE_boolean("encode_reverse", False, "Encode in reverse order.")
gflags.DEFINE_boolean(
"encode_bidirectional",
False,
"Encode in both directions.")
gflags.DEFINE_integer(
"encode_num_layers",
1,
"RNN layers in encoding net.")
# RL settings.
gflags.DEFINE_float(
"rl_mu",
0.1,
"Use in exponential moving average baseline.")
gflags.DEFINE_enum("rl_baseline",
"ema",
["ema",
"pass",
"greedy",
"value"],
"Different configurations to approximate reward function.")
gflags.DEFINE_enum("rl_reward", "standard", ["standard", "xent"],
"Different reward functions to use.")
gflags.DEFINE_float("rl_weight", 1.0, "Hyperparam for REINFORCE loss.")
gflags.DEFINE_boolean("rl_whiten", False, "Reduce variance in advantage.")
gflags.DEFINE_boolean(
"rl_valid",
True,
"Only consider non-validated actions.")
gflags.DEFINE_float(
"rl_epsilon",
1.0,
"Percent of sampled actions during train time.")
gflags.DEFINE_float(
"rl_epsilon_decay",
50000,
"Step constant in epsilon delay equation.")
gflags.DEFINE_float(
"rl_confidence_interval",
1000,
"Penalize probabilities of transitions.")
gflags.DEFINE_float(
"rl_confidence_penalty",
None,
"Penalize probabilities of transitions.")
gflags.DEFINE_boolean(
"rl_catalan",
False,
"Sample over a uniform distribution of binary trees.")
gflags.DEFINE_boolean(
"rl_catalan_backprop",
False,
"Sample over a uniform distribution of binary trees.")
gflags.DEFINE_boolean(
"rl_wake_sleep",
False,
"Inverse relationship between temperature and rl_weight.")
gflags.DEFINE_boolean(
"rl_transition_acc_as_reward",
False,
"Use the transition accuracy as the reward. For debugging only.")
# MLP settings.
gflags.DEFINE_integer(
"mlp_dim",
256,
"Dimension of intermediate MLP layers.")
gflags.DEFINE_integer("num_mlp_layers", 1, "Number of MLP layers.")
gflags.DEFINE_boolean(
"mlp_ln",
True,
"When True, layer normalization is used between MLP layers.")
gflags.DEFINE_float("semantic_classifier_keep_rate", 0.9,
"Used for dropout in the semantic task classifier.")
# Optimization settings.
gflags.DEFINE_enum("optimizer_type", "SGD", ["Adam", "SGD"], "")
gflags.DEFINE_integer(
"training_steps",
1000000,
"Stop training after this point.")
gflags.DEFINE_integer("batch_size", 32, "Minibatch size.")
gflags.DEFINE_float("learning_rate", 0.5, "Used in optimizer.") # https://twitter.com/karpathy/status/801621764144971776
gflags.DEFINE_float("learning_rate_decay_when_no_progress", 0.5,
"Used in optimizer. Decay the LR by this much every epoch steps if a new best has not been set in the last epoch.")
gflags.DEFINE_float("clipping_max_value", 5.0, "")
gflags.DEFINE_float("l2_lambda", 1e-5, "")
# Display settings.
gflags.DEFINE_integer(
"statistics_interval_steps",
100,
"Log training set performance statistics at this interval.")
gflags.DEFINE_integer(
"eval_interval_steps",
100,
"Evaluate at this interval.")
gflags.DEFINE_integer(
"sample_interval_steps",
None,
"Sample transitions at this interval.")
gflags.DEFINE_integer("ckpt_interval_steps", 5000,
"Update the checkpoint on disk at this interval.")
gflags.DEFINE_boolean(
"ckpt_on_best_dev_error",
True,
"If error on the first eval set (the dev set) is "
"at most 0.99 of error at the previous checkpoint, save a special 'best' checkpoint.")
gflags.DEFINE_integer(
"early_stopping_steps_to_wait",
50000,
"If development set error doesn't improve significantly in this many steps, cease training.")
gflags.DEFINE_boolean("evalb", False, "Print transition statistics.")
gflags.DEFINE_integer("num_samples", 0, "Print sampled transitions.")
# Evaluation settings
gflags.DEFINE_boolean(
"expanded_eval_only_mode",
False,
"If set, a checkpoint is loaded and a forward pass is done to get the predicted "
"transitions. The inferred parses are written to the supplied file(s) along with example-"
"by-example accuracy information. Requirements: Must specify checkpoint path.") # TODO: Rename.
gflags.DEFINE_boolean(
"expanded_eval_only_mode_use_best_checkpoint",
True,
"When in expanded_eval_only_mode, load the ckpt_best checkpoint.")
gflags.DEFINE_boolean("write_eval_report", False, "")
gflags.DEFINE_boolean(
"eval_report_use_preds", True, "If False, use the given transitions in the report, "
"otherwise use predicted transitions. Note that when predicting transitions but not using them, the "
"reported predictions will look very odd / not valid.") # TODO: Remove.
# Maillard Pyramid
gflags.DEFINE_boolean(
"cosine",
False,
"If true, use cosine similarity instead of dot product in measuring score for compositions.")
gflags.DEFINE_enum(
"parent_selection",
"gumbel",
["gumbel",
"st-gumbel",
"softmax"],
"Which function to use to select or calculate the parent.")
gflags.DEFINE_boolean(
"right_branching",
False,
"Force right-branching composition.")
gflags.DEFINE_boolean(
"debug_branching",
False,
"use alternative style of right-branching composition.")
gflags.DEFINE_boolean(
"uniform_branching",
False,
"Uniform distribution instead of gumble softmax weighting during chart parsing.")
gflags.DEFINE_boolean(
"random_branching",
False,
"Random weighting instead of gumble softmax weighting during chart parsing.")
gflags.DEFINE_boolean(
"st_gumbel",
False,
"ST-gumble softmax weighting during chart parsing.")
flags.DEFINE_boolean("naiveavg", False, "naive sum for averaging (slow!)")
flags.DEFINE_string ("train", None, "training corpus")
flags.DEFINE_string ("dev", None, "dev corpus")
flags.DEFINE_string ("out", None, "output file (for weights)")
flags.DEFINE_boolean ("finaldump", False, "dump best weights at the end instead of at each new high")
flags.DEFINE_integer ("multi", 20, "multiprocessing: # of CPUs, -1 to use all CPUs")
##flags.DEFINE_boolean("singletrain", False, "use single thread for training")
##flags.DEFINE_boolean("singledev", False, "use single thread for eval_on_dev")
##flags.DEFINE_boolean("single", True, "--singletrain --singledev")
flags.DEFINE_string("update", "max", "update method: early, noearly, hybrid, late, max")
flags.DEFINE_boolean("shuffle", False, "shuffle training data before each iteration")
flags.DEFINE_float("learning_rate", 1.0, "learning rate")
flags.DEFINE_boolean("allmodels", False, "dumpto separate model files at each best-so-far iteration")
#from mydouble import counts
import gc
import os
class Perceptron(object):
def __init__(self, decoder):
Perceptron.learning_rate = FLAGS.learning_rate
Perceptron.singletrain = FLAGS.multi == 1
Perceptron.singledev = FLAGS.multi == 1
gflags.DEFINE_integer("update_iterations", None,
"Number of iterations to output logging information.")
gflags.DEFINE_integer("iter_per_epoch", None,
"Number of iterations per epoch. Leave empty.")
gflags.DEFINE_integer("save_iterations", None,
("Number of iterations to save the network (expensive "
"to do this)."))
gflags.DEFINE_integer("total_epochs", 500, "Total number of epochs.")
gflags.DEFINE_boolean("reweight", True, "Try re-weighting.")
gflags.DEFINE_string("frames",
"-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10",
"Frames to process.")
# gflags.DEFINE_float(
# "hantman_weight_decay", 0.0001, "Weight decay value.")
gflags.DEFINE_float("learning_rate", 0.001, "Learning rate.")
gflags.DEFINE_integer("hantman_mini_batch", 256,
"Mini batch size for training.")
# needed to limit how much to process during eval. important for speed purposes.
gflags.DEFINE_integer("seq_len", 1500, "Sequence length.")
gflags.MarkFlagAsRequired("out_dir")
gflags.MarkFlagAsRequired("train_file")
gflags.MarkFlagAsRequired("test_file")
# gflags.DEFINE_boolean("help", False, "Help")
gflags.ADOPT_module_key_flags(arg_parsing)
gflags.ADOPT_module_key_flags(flags.cuda_flags)
g_label_names = ["lift", "hand", "grab", "supinate", "mouth", "chew"]
import sys
from collections import deque
import os
if __name__ == '__main__':
Flags = gflags.FLAGS
gflags.DEFINE_bool("cuda", True, "use cuda")
gflags.DEFINE_string("train_path", "/home/data/pin/data/omniglot/images_background", "training folder")
gflags.DEFINE_string("test_path", "/home/data/pin/data/omniglot/images_evaluation", 'path of testing folder')
gflags.DEFINE_integer("way", 20, "how much way one-shot learning")
gflags.DEFINE_string("times", 400, "number of samples to test accuracy")
gflags.DEFINE_integer("workers", 4, "number of dataLoader workers")
gflags.DEFINE_integer("batch_size", 128, "number of batch size")
gflags.DEFINE_float("lr", 0.00006, "learning rate")
gflags.DEFINE_integer("show_every", 10, "show result after each show_every iter.")
gflags.DEFINE_integer("save_every", 100, "save model after each save_every iter.")
gflags.DEFINE_integer("test_every", 100, "test model after each test_every iter.")
gflags.DEFINE_integer("max_iter", 50000, "number of iterations before stopping")
gflags.DEFINE_string("model_path", "/home/data/pin/model/siamese", "path to store model")
gflags.DEFINE_string("gpu_ids", "0,1,2,3", "gpu ids used to train")
Flags(sys.argv)
data_transforms = transforms.Compose([
transforms.RandomAffine(15),
transforms.ToTensor()
])
# See the License for the specific language governing permissions and
# limitations under the License.
"""A Cisco XR device .
This module implements the base device interface of base_device.py for
CiscoXR devices.
"""
import gflags
import paramiko_device
import push_exceptions as exceptions
FLAGS = gflags.FLAGS
gflags.DEFINE_float('ciscoxr_timeout_response', None,
'CiscoXR device response timeout in seconds.')
gflags.DEFINE_float('ciscoxr_timeout_connect', None,
'CiscoXR device connect timeout in seconds.')
gflags.DEFINE_float('ciscoxr_timeout_idle', None,
'CiscoXR device idle timeout in seconds.')
gflags.DEFINE_float('ciscoxr_timeout_disconnect', None,
'CiscoXR device disconnect timeout in seconds.')
gflags.DEFINE_float('ciscoxr_timeout_act_user', None,
'CiscoXR device user activation timeout in seconds.')
# pylint: disable=arguments-differ
# 38:CiscoXrDevice._Cmd: Arguments number differs from overridden method.
class CiscoXrDevice(paramiko_device.ParamikoDevice):
"""A base device model suitable for CiscoXR devices.
import cv2
import cv_bridge
import gflags
import glog
import rosbag
import yaml
# Requried flags.
gflags.DEFINE_string('input_bag', None, 'Input bag path.')
# Optional flags.
gflags.DEFINE_string('output_path', './', 'Output path.')
gflags.DEFINE_string('weather', 'CLEAR', 'Options: CLEAR, SUNNY, RAINY.')
gflags.DEFINE_string('scene', 'CITY', 'Options: CITY, HIGHWAY.')
gflags.DEFINE_string('time_interval', 'DAYTIME', 'Options: DAYTIME, NIGHT.')
gflags.DEFINE_float('extract_rate', 3, 'Rate to extract image, in seconds.')
# Stable flags which rarely change.
# gflags.DEFINE_string('topic', '/apollo/sensor/camera/obstacle/front_6mm','Source topic.')
gflags.DEFINE_string('topic', '/apollo/sensor/camera/traffic/image_short',
'Source topic.')
gflags.DEFINE_integer('sensor_id', 436, 'Source sensor ID.')
gflags.DEFINE_string('capture_place', 'Multiple', 'E.g.: Multiple, Sunnyvale.')
def extract_meta_info(bag):
"""Extract information from a bag file, return an info dict."""
# Extract from bag info.
info_dict = yaml.load(bag._get_yaml_info())
meta_info = {
'car_id': 'MKZ056',
import gflags
import imageio
import numpy
from PIL import Image
from PIL.ImageDraw import Draw
gflags.DEFINE_string('inp', None, 'Input screenshot image')
gflags.DEFINE_string('out', None, 'Output antimated GIF')
gflags.DEFINE_integer('h', 0, 'Window height')
gflags.DEFINE_integer('maxspeed', 200, 'Max speed on scroll px/frame')
gflags.DEFINE_list('stops', [], 'List of stops for scrolling')
gflags.DEFINE_integer('zoom', 0, 'Number of steps on initial zoom in')
gflags.DEFINE_float('zoom_frac', .3, 'Fraction of screenshot to see on zoomout')
gflags.register_validator('inp', os.path.exists, 'Input screenshot required')
gflags.register_validator('h', lambda v: v > 0, 'Window height required')
F = gflags.FLAGS
def add_frame(frames, frame, duration):
frames.append((prettify(frame), duration))
def prettify(frame):
off = 5
h, w = frame.shape[:2]
pretty = Image.new('RGB', (w + off, h + off), '#ffffff')
import cPickle as pickle
from iceberk import mpi, classifier, mathutil
from iceberk.experimental.meu_logistic_loss import loss_meu_logistic
import numpy as np
import logging
import os, sys
from sklearn.cross_validation import StratifiedKFold
import time
import gflags
########
# Settings
########
FEATDIR = "/tscratch/tmp/jiayq/imagenet-sbow/"
gflags.DEFINE_bool("load", False, "If set, load the data and then stop.")
gflags.DEFINE_float("reg", 0.01, "The reg term")
gflags.DEFINE_integer("minibatch", 100000, "The minibatch size")
gflags.DEFINE_bool("svm", False, "If set, run SVM")
gflags.DEFINE_bool("hier", False, "If set, use hierarchical loss")
FLAGS = gflags.FLAGS
FLAGS(sys.argv)
########
# Main script
########
if mpi.SIZE > 1:
raise RuntimeError, "This script runs on single machines only."
np.random.seed(42 + mpi.RANK)
mpi.root_log_level(level=logging.DEBUG)
logging.info("Loading data...")
import sys
sys.path.insert(0, caffe_dir)
import util
import gflags
import numpy as np
import caffe
if not gflags.FLAGS.has_key("model_dir"):
gflags.DEFINE_string("model_dir", "@model_path", "set model dir")
if not gflags.FLAGS.has_key("feature_layer_names"):
gflags.DEFINE_string("feature_layer_names", "['@feature_layer_name']",
"set feature layer names")
if not gflags.FLAGS.has_key("device_id"):
gflags.DEFINE_integer("device_id", 0, "set device id")
if not gflags.FLAGS.has_key("ratio"):
gflags.DEFINE_float("ratio", -1.0, "set image ratio")
if not gflags.FLAGS.has_key("scale"):
gflags.DEFINE_float("scale", 1.1, "set image scale")
if not gflags.FLAGS.has_key("resize_height"):
gflags.DEFINE_integer("resize_height", 256, "set image size")
if not gflags.FLAGS.has_key("resize_width"):
gflags.DEFINE_integer("resize_width", 256, "set image size")
if not gflags.FLAGS.has_key("raw_scale"):
gflags.DEFINE_float("raw_scale", 255.0, "set raw scale")
if not gflags.FLAGS.has_key("input_scale"):
gflags.DEFINE_float("input_scale", 1.0, "set input scale")
if not gflags.FLAGS.has_key("gray"):
gflags.DEFINE_boolean("gray", False, "set gray")
if not gflags.FLAGS.has_key("oversample"):
gflags.DEFINE_boolean("oversample", False, "set oversample")
#!/usr/bin/python
import os
import sys
import gflags
import pandas as pd
import numpy as np
from market import market
from portfolio import portfolio
from simulations_code import simulate
gflags.DEFINE_multi_int('hour', 24, 'Hours in between market')
gflags.DEFINE_float(
'min_percentage_change', 0.1,
"Minimum variation in 'balance' needed to place an order."
"1 is 100%")
gflags.DEFINE_string('state_csv', None, "path to csv containing a 'state'")
FLAGS = gflags.FLAGS
gflags.RegisterValidator('min_percentage_change', lambda x: x >= 0,
'Should be positive or 0')
#gflags.RegisterValidator('state_csv', os.path.isfile)
if __name__ == "__main__":
try:
argv = FLAGS(sys.argv)
except gflags.FlagsError as e:
print "%s\nUsage: %s ARGS\n%s" % (e, sys.argv[0], FLAGS)
sys.exit(1)
print '*' * 80
# -*- coding:utf-8 -*-
from collections import defaultdict
import create_features as cf
import predict_nn as nn
import gflags
import sys
FLAGS = gflags.FLAGS
gflags.DEFINE_boolean('use_average', False, 'Use averaged perceptoron.' )
gflags.DEFINE_float('use_margin', 0.0, 'Use margin perceptoron.' )
gflags.DEFINE_float('l1_value', 0.0, 'Use L1 regularization.')
def get_label_and_sentence(line):
parts = line.rstrip().split()
label = parts.pop(0)
sentence = " ".join(parts)
return (label, sentence)
def update_weights(w, phi, y):
for name, value in phi.items():
w[name] += value * y
def learning(weight):
for line in iter(sys.stdin.readline, ""):
label, sent = get_label_and_sentence(line)
phi = cf.create_features(sent)
pre_label = po.predict_one(weight, phi)
if int(pre_label) != int(label):
update_weights(weight, phi, int(label))