# Expand dimensions to match expected EEGNet input
X = (np.expand_dims(X, axis=-1))
# number of temporal sample per trial
n_samples = np.shape(X)[2]
# convert labels to one-hot encodings.
y_cat = np_utils.to_categorical(y)
# using 5 folds
kf = KFold(n_splits = num_splits)
split_ctr = 0
for train, test in kf.split(X, y):
# init model
model = models.EEGNet(nb_classes = num_classes, Chans=n_ch, Samples=n_samples, regRate=0.25,
dropoutRate=0.2, kernLength=kernLength, poolLength=poolLength, numFilters=8,
dropoutType='Dropout')
#print(model.summary())
# Set Learning Rate
adam_alpha = Adam(lr=(0.0001))
model.compile(loss='categorical_crossentropy', optimizer=adam_alpha, metrics = ['accuracy'])
np.random.seed(42*(split_ctr+1))
np.random.shuffle(train)
# do training
history = model.fit(X[train], y_cat[train],
validation_data=(X[test], y_cat[test]),
batch_size = 16, epochs = n_epochs, callbacks=[lrate], verbose = 2)
acc[split_ctr] = save_results(history,num_classes,n_ds,n_ch,T,split_ctr)
print(x_train.shape)
nb_classes = len(np.unique(
y_train)) # np.unique是去除重复数字然后排序输出，这样输出它的长度表明总共的分类数-可尝试增加分类数看结果的变化
samples = x_train.shape[3]
channels = x_train.shape[2]
racc = []
rbca = []
rasr = []
pruning_idx = [30, 60, 180] #CNN剪枝的参数
for i in range(repeat):
# Build Model
if model_used == 'EEGNet':
model = models.EEGNet(nb_classes=nb_classes,
Chans=channels,
Samples=samples)
elif model_used == 'DeepCNN':
model = models.DeepConvNet(nb_classes=nb_classes,
Chans=channels,
Samples=samples)
else:
raise Exception('No such model:{}'.format(model_used))
#######读取之前存储的模型
model = load_model(filepath='model_poison_MI_{}_{}_{}.h5'.format(
npp_params[0], npp_params[1], npp_params[2]))
#model = load_model(filepath='model_poison_ERN_pruning6_{}_{}_{}.h5'.format(npp_params[0], npp_params[1], npp_params[2]))
#model = load_model(filepath='model_orginal_retrain{}_{}_{}.h5'.format(npp_params[0], npp_params[1], npp_params[2]))
#model = load_model(filepath='model_CNNERN_pruning_{}_{}_{}_{}.h5'.format(npp_params[0], pruning_idx[0], pruning_idx[1],pruning_idx[2]))
#model = load_model(filepath='model_orginal_poison_CNNP300pruning-{}_{}_{}.h5'.format(npp_params[0], npp_params[1], npp_params[2]))
def Net(n_class, n_ch, n_time):
embedding_net = models.EEGNet(n_class, n_ch, n_time)
model = models.FcClfNet(embedding_net)
return model