def test_value_timestamp(self):
"""
Send value request to StatHat API with timestamp
"""
responses.add(
responses.POST,
'http://stathatapi.example/ez',
body='',
status=200,
content_type='application/json'
)
instance = StatHat('*****@*****.**')
instance.value('a_stat', 'a_value', 10000)
self.assertEqual(len(responses.calls), 1)
self.assertDictEqual(
json.loads(responses.calls[0].request.body),
{
'ezkey': '*****@*****.**',
'data': [
{'stat': 'a_stat', 'value': 'a_value', 't': 10000}
]
}
)
def main():
stathat = StatHat(STATHAT_KEY)
payload = {"ezkey": STATHAT_KEY, "data": []}
data = speedtest()
stathat.value('Latency', data['latency'])
stathat.value('Download', data['download'])
stathat.value('Upload', data['upload'])
print data
# Loop over epochs.
lr = args.lr
prev_val_loss = None
for epoch in range(1, args.epochs + 1):
epoch_start_time = time.time()
train()
val_loss = evaluate(val_data)
print('-' * 89)
print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
val_loss, math.exp(val_loss)))
print('-' * 89)
# Anneal the learning rate.
stats.value('validated_loss', val_loss)
if prev_val_loss and val_loss >= prev_val_loss:
lr /= 4
prev_val_loss = val_loss
with open(args.save + str(epoch) + '.pt', 'wb') as f:
torch.save(model, f)
# Run on test data and save the model.
test_loss = evaluate(test_data)
print('=' * 89)
print('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(
test_loss, math.exp(test_loss)))
print('=' * 89)
if args.save != '':
with open(args.save + '.pt', 'wb') as f:
