def objective_fn(**kwargs):
data_config, nn_config, total_intervals = make_model(**kwargs)
df = pd.read_csv('data/all_data_30min.csv')
model = Model(data_config=data_config,
nn_config=nn_config,
data=df,
intervals=total_intervals)
model.build_nn()
history = model.train_nn(indices='random')
return np.min(history.history['val_loss'])
if __name__ == "__main__":
input_features = [
'tide_cm', 'wat_temp_c', 'sal_psu', 'air_temp_c', 'pcp_mm', 'pcp3_mm',
'wind_speed_mps', 'rel_hum'
]
# column in dataframe to bse used as output/target
outputs = ['blaTEM_coppml']
data_config, nn_config, total_intervals = make_model(batch_size=16,
lookback=1,
inputs=input_features,
outputs=outputs,
lr=0.0001)
df = pd.read_csv('../data/all_data_30min.csv')
model = Model(data_config=data_config,
nn_config=nn_config,
data=df,
intervals=total_intervals)
model.build_nn()
history = model.train_nn(indices='random')
y, obs = model.predict(st=0,
use_datetime_index=False,
marker='.',
linestyle='')
model.view_model(st=0)