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))
