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)
import argparse
import os.path
import sys
import time
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
import logging
from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
from datetime import datetime
from tensorflow.python.platform import gfile
from speechclas import paths, config, input_data, models, freeze, utils, model_utils
from tensorflow.python.framework import graph_util
CONF = config.conf_dict()
timestamp = datetime.now().strftime('%Y-%m-%d_%H%M%S')
def train_fn(TIMESTAMP, CONF):
sess = tf.InteractiveSession()
paths.timestamp = TIMESTAMP
paths.CONF = CONF
print(CONF)
utils.create_dir_tree()
#Activate only if you want to make a backup of the splits used for the training
#utils.backup_splits()
# logging.set_verbosity(logging.INFO)