batch_size=32,
dropout=1)
if select_case == 3:
classifier = supervised_sAE(
out_func='softmax',
en_func='affine', # encoder：[sigmoid] | [affine]
use_for='classification',
loss_func=
'mse', # decoder：[sigmoid] with ‘cross_entropy’ | [affine] with ‘mse’
ae_type='ae', # ae | dae | sae
noise_type='gs', # Gaussian noise (gs) | Masking noise (mn)
beta=0.6, # 惩罚因子权重（KL项 | 非噪声样本项）
p=0.01, # DAE：样本该维作为噪声的概率 / SAE稀疏性参数：期望的隐层平均活跃度（在训练批次上取平均）
sup_ae_struct=[x_dim, 200, 50, y_dim],
sup_ae_epochs=100,
ae_epochs=30,
batch_size=32,
ae_lr=1e-3,
dropout=1)
Initializer.sess_init_all(sess) # 初始化变量
summ = Summaries(os.path.basename(__file__), sess=sess)
classifier.train_model(X_train, Y_train, sess, summ)
# Test
print("[Test data...]")
Y_pred = classifier.test_model(X_test, Y_test, sess)
summ.train_writer.close()
sess.close()
dbn_struct=[dim, 100, 100, fault],
rbm_v_type='bin',
rbm_epochs=10,
batch_size=32,
cd_k=10,
rbm_lr=1e-3,
dropout=0.95)
if select_case == 2:
classifier = CNN(output_act_func='softmax',
hidden_act_func='relu',
loss_fuc='cross_entropy',
use_for='classification',
cnn_lr=1e-3,
cnn_epochs=100,
img_shape=[dynamic, 52],
channels=[1, 6, 6, 64, fault],
fsize=[[4, 4], [3, 3]],
ksize=[[2, 2], [2, 2]],
batch_size=32,
dropout=0.9)
classifier.build_model()
classifier.train_model(X_train, Y_train, sess)
# Test
Y_pred = list()
print("[Test data...]")
for i in range(fault):
print(">>>Test fault {}:".format(i))
Y_pred.append(classifier.test_model(X_test[i], Y_test[i], sess))