def train_initial(name, n_seq, n_labels, n_dimension, n_hidden_1, n_hidden_2, epochs, save):
usage_ratio = 1
# epochs = 150
print '========================= Reading ========================='
X_train, y_train, X_test, y_test = test.read_data(name=name, n_seq=n_seq, n_labels=n_labels, n_dimension=n_dimension)
data = (X_train, y_train, X_test, y_test)
print '========================= Modeling ========================='
model = lstm.build_model(n_dimension=n_dimension, n_labels=n_labels, n_seq=n_seq, n_hidden_1=n_hidden_1, n_hidden_2=n_hidden_2)
print '========================= Training =========================='
model = lstm.run_network(model=model, data=data, epochs=epochs, usage_ratio=usage_ratio, save=True, save_name=name)
print '========================= Testing =========================='
test.test_all_metrics(model, data=data, usage_ratio=usage_ratio)
def train_initial_divide(marker, n_dimension, n_seq, n_hidden_1, n_hidden_2, epochs, save):
usage_ratio = 1
# epochs = 150
print '========================= Reading ========================='
X_train, y_train, X_test, y_test = pretraining.divide_save(
filename = '../data/torque_participants/S01_valid_' + marker + '.csv',
savename = marker + '_' + n_dimension + '_' + n_seq,
n_seq = n_seq
)
data = (X_train, y_train, X_test, y_test)
print '========================= Modeling ========================='
model = lstm.build_model(n_dimension=n_dimension, n_seq=n_seq, n_hidden_1=n_hidden_1, n_hidden_2=n_hidden_2)
print '========================= Training =========================='
model = lstm.run_network(model=model, data=data, epochs=epochs, usage_ratio=usage_ratio, save=True, save_name=marker + '_' + n_dimension + '_' + n_seq)
print '========================= Testing =========================='
test.test_all_metrics(model, data=data, usage_ratio=usage_ratio)
