def test_mlp(X, Y):
dbn_network: UnsupervisedDBN = UnsupervisedDBN.load(DBN_NETWORK_FILENAME)
mlp_network: MLPClassifier = joblib.load(MLP_NETWORK_FILENAME)
first_positions = dbn_network.transform(X[:, 0])
second_positions = dbn_network.transform(X[:, 1])
X = np.concatenate((first_positions, second_positions), axis=1)
return mlp_network.score(X, Y)
def predict_mlp(first, second):
First = np.array(first)
Second = np.array(second)
dbn_network: UnsupervisedDBN = UnsupervisedDBN.load(DBN_NETWORK_FILENAME)
mlp_network: MLPClassifier = joblib.load(MLP_NETWORK_FILENAME)
first_position = dbn_network.transform(First)
second_position = dbn_network.transform(Second)
X = np.concatenate((first_position, second_position))
result = mlp_network.predict(X.reshape(1, -1))
return result.flatten().tolist()
def train_mlp(X, Y):
dbn_network = UnsupervisedDBN.load(DBN_NETWORK_FILENAME)
first_positions = dbn_network.transform(X[:, 0])
second_positions = dbn_network.transform(X[:, 1])
X = np.concatenate((first_positions, second_positions), axis=1)
print(f'Shape after Concatenation = {X.shape}')
mlp_network = MLPClassifier(hidden_layer_sizes=(90, 45, 20, 2),
learning_rate='constant',
learning_rate_init=1e-2,
max_iter=1000)
mlp_network.fit(X, Y)
joblib.dump(mlp_network, MLP_NETWORK_FILENAME)
def __init__(self, load=False):
if load:
self.bands = pickle.load(open("bands.pickle", "rb"))
self.dbn = UnsupervisedDBN.load("dbn.pickle")
self.prepca = pickle.load(open("prepca.pickle", "rb"))
self.pca = pickle.load(open("pca.pickle", "rb"))
else:
self.dbn = UnsupervisedDBN(hidden_layers_structure=[50, 50, 50],
batch_size=1024,
learning_rate_rbm=0.001,
n_epochs_rbm=5,
contrastive_divergence_iter=2,
activation_function='sigmoid')
self.bands = [
{"start": 0, "end": 150, "pca_components":30, "energy_factor": 5},
{"start": 120, "end":300, "pca_components":70, "energy_factor": 1},
{"start": 250, "end":513, "pca_components":60, "energy_factor": 0.5}
]
self.prepca = [PCA(n_components=band["pca_components"]) for band in self.bands]
self.pca = PCA(n_components=16)