def create_model(hyperparams):
lr = hyperparams['eta']
mom = hyperparams['alpha']
l2 = hyperparams['lambda']
dim_hid = int(hyperparams['hidden_nodes'])
_fun = hyperparams['functions']
kernel_init = hyperparams['kernel_init']
module = __import__('kernel_initialization')
KernelInit = getattr(module, kernel_init)
model = NeuralNetwork(loss='mse', metric='mee')
model.add_layer(dim_hid,
input_dim=INPUT_DIM,
activation=_fun,
kernel_initialization=KernelInit())
model.add_output_layer(2,
activation=_fun,
kernel_initialization=KernelInit())
model.compile(lr=lr, momentum=mom, l2=l2)
return model
parser = Monks_parser(path_tr, path_ts)
X_train, Y_train, X_test, Y_test = parser.parse(dim_in, dim_out, one_hot)
#Y_train = change_output_value(Y_train, 0, -1)
#Y_test = change_output_value(Y_test, 0, -1)
#X_train, Y_train, X_val, Y_val = train_test_split(X_train, Y_train, test_size=0.25)
dim_in = one_hot
dim_hid = 4
model = NeuralNetwork('mse', 'accuracy')
model.add_layer(dim_hid, input_dim=dim_in, activation='relu', kernel_initialization=RandomUniformInitialization(-0.5, 0.5))
model.add_output_layer(dim_out, activation='sigmoid', kernel_initialization=RandomUniformInitialization(-0.5, 0.5))
model.compile(0.8, 0.7, 0.0)
model.fit(
X_train, Y_train, 500, X_train.shape[0], ts=(X_test, Y_test),
verbose=True, tol=1e-2
)
err_tr, acc_tr = model.evaluate(X_train, Y_train)
err_ts, acc_ts = model.evaluate(X_test, Y_test)
acc_tr = acc_tr*100
acc_ts = acc_ts*100
errors = [err_tr, err_ts]
accuracy = [acc_tr, acc_ts]
res = {
