def train(user_conf):
"""
Parameters
----------
user_conf : dict
Json dict (created with json.dumps) with the user's configuration parameters that will replace the defaults.
Must be loaded with json.loads()
For example:
user_conf={'num_classes': 'null', 'lr_step_decay': '0.1', 'lr_step_schedule': '[0.7, 0.9]', 'use_early_stopping': 'false'}
"""
CONF = config.CONF
# Update the conf with the user input
for group, val in sorted(CONF.items()):
for g_key, g_val in sorted(val.items()):
g_val['value'] = json.loads(user_conf[g_key])
# Check the configuration
try:
config.check_conf(conf=CONF)
except Exception as e:
raise BadRequest(e)
CONF = config.conf_dict(conf=CONF)
timestamp = datetime.now().strftime('%Y-%m-%d_%H%M%S')
config.print_conf_table(CONF)
K.clear_session() # remove the model loaded for prediction
train_fn(TIMESTAMP=timestamp, CONF=CONF)
# Sync with NextCloud folders (if NextCloud is available)
try:
mount_nextcloud(paths.get_models_dir(), 'ncplants:/models')
except Exception as e:
print(e)
def train(**args):
"""
Train an image classifier
"""
update_with_query_conf(user_args=args)
CONF = config.conf_dict
timestamp = datetime.now().strftime('%Y-%m-%d_%H%M%S')
config.print_conf_table(CONF)
K.clear_session() # remove the model loaded for prediction
train_fn(TIMESTAMP=timestamp, CONF=CONF)
# Sync with NextCloud folders (if NextCloud is available)
try:
mount_nextcloud(paths.get_models_dir(), 'ncplants:/models')
except Exception as e:
print(e)
import re
from collections import OrderedDict
import urllib.request
import requests
from tensorflow.keras import backend as K
from webargs import fields
from aiohttp.web import HTTPBadRequest
from speechclas import paths, utils, config, label_wav
from speechclas.data_utils import mount_nextcloud
from speechclas.train_runfile import train_fn
# Mount NextCloud folders (if NextCloud is available)
try:
mount_nextcloud('ncplants:/data/dataset_files', paths.get_splits_dir())
mount_nextcloud('ncplants:/data/images', paths.get_audio_dir())
#mount_nextcloud('ncplants:/models', paths.get_models_dir())
except Exception as e:
print(e)
# Empty model variables for inference (will be loaded the first time we perform inference)
loaded = False
graph, model, conf, class_names, class_info = None, None, None, None, None
# Additional parameters
allowed_extensions = set(['wav']) # allow only certain file extensions
top_K = 5 # number of top classes predictions to return
def load_inference_model():