我的数据集(我们进行二进制分类的网络流量数据集)-
特征数为 25,我已经对数据集进行了标准化。
我的 ELM 模型-
class ELM:
def __init__(self, num_input_nodes, num_hidden_units, num_out_units, activation='sigmoid',
loss='bce', beta_init=None, w_init=None, bias_init=None):
self._num_input_nodes = num_input_nodes
self._num_hidden_units = num_hidden_units
self._num_out_units = num_out_units
self._activation = getActivation(activation)
self._loss = getLoss(loss)
if isinstance(beta_init, np.ndarray):
self._beta = beta_init
else:
self._beta = np.random.uniform(-1., 1., size=(self._num_hidden_units, self._num_out_units))
if isinstance(w_init, np.ndarray):
self._w = w_init
else:
self._w = np.random.uniform(-1, 1, size=(self._num_input_nodes, self._num_hidden_units))
if isinstance(bias_init, np.ndarray):
self._bias = bias_init
else:
self._bias = np.zeros(shape=(self._num_hidden_units,))
print('Bias shape:', self._bias.shape)
print('W shape:', self._w.shape)
print('Beta shape:', self._beta.shape)
def fit(self, X, Y, display_time=False):
H = self._activation(X.dot(self._w) + self._bias)
# Moore–Penrose pseudo inverse
if display_time:
start = time.time()
H_pinv = np.linalg.pinv(H)
if display_time:
stop = time.time()
print(f'Train time: {stop-start}')
self._beta = H_pinv.dot(Y)
# print('Fit Beta shape:', self._beta.shape)
def __call__(self, X):
H = self._activation(X.dot(self._w) + self._bias)
return H.dot(self._beta)
def evaluate(self, X, Y):
pred = self(X)
# Loss (base on model setting)
loss = self._loss(Y, pred)
# Accuracy
acc = np.sum(np.argmax(pred, axis=-1) == np.argmax(Y, axis=-1)) / len(Y)
# Unweighted Average Recall
# TODO
return loss, acc
# Network Settings
num_classes = 1
num_hidden_layers = 512
input_length = 25
当我尝试为我的数据集运行这个时,准确度变为零。我将 sigmoid 作为激活函数,将二元交叉熵作为我的二元分类任务的损失。