def clone():
tmp = load_object("seed-9473.save")
net = Network()
net.add(FCLayer(30, 30))
net.add(ActivationLayer(tanh, tanh_prime))
net.add(FCLayer(30, 30))
net.add(ActivationLayer(tanh, tanh_prime))
net.add(FCLayer(30, 30))
net.add(ActivationLayer(tanh, tanh_prime))
net.add(FCLayer(30, 2))
net.add(ActivationLayer(softmax, softmax_prime))
net.layers[0] = tmp.layers[0]
net.layers[1] = tmp.layers[1]
net.layers[2] = tmp.layers[2]
net.layers[3] = tmp.layers[3]
net.layers[4] = tmp.layers[4]
net.layers[5] = tmp.layers[5]
net.layers[6] = tmp.layers[6]
save_object(net, f'seed-clone-9473-soft.save')
def train(args, df_x, df_y):
"""
:param args: dict from argparse
:param df_x: DataFrame containing xs
:param df_y: DataFrame containing ys
:return: Network's instance
"""
df_x_train, df_x_test, df_y_train, df_y_test = train_test_split(
df_x, df_y, test_size=0.2, random_state=0)
net = Network()
# Loads a seed if load
if args.load:
if os.path.exists(os.path.join(os.getcwd(), args.load)):
net = load_object(args.load)
else:
sys.exit(display_errors_dict('wrong_load'))
else:
net.add(FCLayer(30, 30))
net.add(ActivationLayer(tanh, tanh_prime))
net.add(FCLayer(30, 30))
net.add(ActivationLayer(tanh, tanh_prime))
net.add(FCLayer(30, 30))
net.add(ActivationLayer(tanh, tanh_prime))
net.add(FCLayer(30, 2))
net.add(ActivationLayer(softmax, softmax_prime))
# Saves seed
if args.save and not args.load:
save_object(net, f'saved-seed.save')
# Trains model
net.use(mse, mse_prime)
net.fit(df_x_train,
df_y_train,
df_x_test,
df_y_test,
epoch=args.epoch,
learning_rate=args.learning,
error_rate=0.01)
plot_loss(net)
# Evaluates with the 20% DataFrame
rse_acc = evaluate_and_predict(net, df_x_test, df_y_test)
# Renames seed with accuracy score
if args.save and not args.load:
move('saved-seed.save', f'seed-{int(rse_acc * 10000)}.save')
# Saves network objet
save_object(net, 'network.save')
return net