def by_infile(self, infile):
try:
shutil.rmtree(self.OUTPUT_DIR)
except:
pass
self.db_open()
json_data = self.get_events_from_infile(infile)
# build preprocessor
ppr = Preprocessor()
# Process raw data
#X, Y, events_found = ppr.get_raw_data(DIMENSION, [RAW_FILE], bad)
X, Y, events_found = ppr.get_from_json(self.DIMENSION, json_data)
X, Y = ppr.remove_outliers(X, Y)
X, Y = ppr.normalize(X, Y)
trX, trY, teX, teY, vaX, vaY = ppr.partition_for_training(
X, Y, 0.0, 1.0)
ppr.store_training_partitions(trX, trY, teX, teY, vaX, vaY,
self.INPUT_DIR)
# build adapter
adapter = MACAdapter(self.INPUT_DIR, self.DIMENSION, self.FOLDS)
# build model
convnet = ConvNet(self.DIMENSION)
# build server
server = ConvNetServer(adapter,
self.OUTPUT_DIR,
batch_size=self.BATCH_SIZE,
verbose=True,
use=True)
x, durs, _ = server.get_testing_batch()
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
convnet.restore(sess, self.INITIAL_WEIGHTS)
predictions = sess.run((convnet.predictor),
feed_dict={
convnet.x: x,
convnet.durs: durs
})
# Get event ids
_, _, ids = adapter.get_ids()
results = [{
"eventID": int(ids[i]),
"ml": {
"aircraftProbability":
round(np.around(predictions[i][0], decimals=4), 4),
"model":
self.MODEL
}
} for i in range(0, len(ids))]
for result in results:
self.insert_result_for_event(result)
self.db_close()
'''
# Only run if this is the main module to be run
if __name__ == '__main__':
# build preprocessor
ppr = Preprocessor()
# Process raw data
X, Y, events_found = ppr.get_raw_data(DIMENSION, [RAW_FILE], bad)
X, Y = ppr.remove_outliers(X, Y)
X, Y = ppr.normalize(X, Y)
trX, trY, teX, teY, vaX, vaY = ppr.partition_for_training(X, Y, 0.0, 1.0)
ppr.store_training_partitions(trX, trY, teX, teY, vaX, vaY, INPUT_DIR)
# build adapter
adapter = MACAdapter(INPUT_DIR, DIMENSION, FOLDS)
# build model
convnet = ConvNet(DIMENSION)
# build server
server = ConvNetServer(adapter,
OUTPUT_DIR,
batch_size=BATCH_SIZE,
verbose=True,
use=True)
x, durs, _ = server.get_testing_batch()
with tf.Session() as sess:
init = tf.global_variables_initializer()
'''
Tester
'''
# Number of trials to do for each fold (stats will be averaged)
TRIALS_PER_FOLD = 5
'''
SCRIPT
'''
# Only run if this is the main module to be run
if __name__ == '__main__':
# build adapter
adapter = MACAdapter(INPUT_DIR, DIMENSION, FOLDS)
# build model
convnet = ConvNet(DIMENSION)
# build server
server = ConvNetServer(adapter, OUTPUT_DIR,
batch_size = BATCH_SIZE,
verbose = False)
# build trainer
trainer = ConvNetTrainer(convnet, server, EPOCHS, STEPS_PER_EPOCH,
optimizer = OPTIMIZER,
opt_kwargs = OPT_KWARGS,
keep_prob = KEEP_PROB,
batch_size = BATCH_SIZE,
# file location of weights to restore from (i.e. weights/model1.ckpt)
INITIAL_WEIGHTS = './poor.ckpt'
# probability value to use for dropout
KEEP_PROB = 1.0
# training batch size
BATCH_SIZE = 400
# step at which to log status at modulo 0
DISPLAY_STEP = 5
'''
SCRIPT
'''
# Only run if this is the main module to be run
if __name__ == '__main__':
# build adapter
adapter = MACAdapter(INPUT_DIR)
# build model
convnet = ConvNet(10)
# build server
server = ConvNetServer(adapter, OUTPUT_DIR, batch_size=BATCH_SIZE)
# build trainer
trainer = ConvNetTrainer(convnet,
server,
EPOCHS,
STEPS_PER_EPOCH,
optimizer=OPTIMIZER,
opt_kwargs=OPT_KWARGS)