def fit_model_threaded(model, data_gen_train, steps_per_epoch, configs):
"""thread worker for model fitting - so it doesn't freeze on jupyter notebook"""
model = lstm.build_network([ncols, 150, 150, 1])
model.fit_generator(data_gen_train,
steps_per_epoch=steps_per_epoch,
epochs=configs['model']['epochs'])
model.save(configs['model']['filename_model'])
print('> Model Trained! Weights saved in',
configs['model']['filename_model'])
return
configs['data']['filename_clean'],
batch_size=configs['data']['batch_size'])
with h5py.File(configs['data']['filename_clean'], 'r') as hf:
nrows = hf['x'].shape[0]
ncols = hf['x'].shape[2]
print(ncols)
print(hf['x'].shape)
ntrain = int(configs['data']['train_test_split'] * nrows)
steps_per_epoch = int(
(ntrain / configs['model']['epochs']) / configs['data']['batch_size'])
print('> Clean data has', nrows, 'data rows. Training on', ntrain, 'rows with',
steps_per_epoch, 'steps-per-epoch')
model = lstm.build_network(layers=[ncols, 150, 150, 1])
t = threading.Thread(target=fit_model_threaded,
args=[model, data_gen_train, steps_per_epoch, configs])
t.start()
data_gen_test = dl.generate_clean_data(
configs['data']['filename_clean'],
batch_size=configs['data']['batch_size'],
start_index=ntrain)
ntest = nrows - ntrain
steps_test = int(ntest / configs['data']['batch_size'])
print('> Testing model on', ntest, 'data rows with', steps_test, 'steps')
predictions = model.predict_generator(generator_strip_xy(
data_gen_test, true_values),
data_gen_train = dl.generate_clean_data(
configs['data']['filename_clean'],
batch_size=configs['data']['batch_size'])
with h5py.File(configs['data']['filename_clean'], 'r') as hf:
nrows = hf['x'].shape[0]
ncols = hf['x'].shape[2]
ntrain = int(configs['data']['train_test_split'] * nrows)
steps_per_epoch = int(
(ntrain / configs['model']['epochs']) / configs['data']['batch_size'])
print('> Clean data has', nrows, 'data rows. Training on', ntrain, 'rows with',
steps_per_epoch, 'steps-per-epoch')
model = lstm.build_network([ncols, 150, 150, 1])
t = threading.Thread(target=fit_model_threaded,
args=[model, data_gen_train, steps_per_epoch, configs])
t.start()
data_gen_test = dl.generate_clean_data(
configs['data']['filename_clean'],
batch_size=configs['data']['batch_size'],
start_index=ntrain)
ntest = nrows - ntrain
steps_test = int(ntest / configs['data']['batch_size'])
print('> Testing model on', ntest, 'data rows with', steps_test, 'steps')
predictions = model.predict_generator(generator_strip_xy(
data_gen_test, true_values),
ncols = hf['x'].shape[2]
ntrain = int(configs['data']['train_test_split'] * nrows)
steps_per_epoch = int((ntrain / configs['model']['epochs']) / configs['data']['batch_size'])
print('> Clean data has', nrows, 'data rows. Training on', ntrain, 'rows with', steps_per_epoch, 'steps-per-epoch')
data_gen_test = dl.generate_clean_data(
configs['data']['filename_clean'],
batch_size=configs['data']['batch_size'],
start_index=ntrain
)
model, history = lstm.build_network([ncols, 128, 256, 1], data_gen_train, data_gen_test, steps_per_epoch, configs)
MSE = 0
for error in history.history['mean_squared_error']:
MSE += error
MSE = ( MSE / len(history.history['mean_squared_error'])) * 100
print(f"> Model MSE : {MSE}")
data_gen_test = dl.generate_clean_data(
configs['data']['filename_clean'],
batch_size=configs['data']['batch_size'],
start_index=ntrain
)