def main():
# 读取原始文件数据，标签
print("-------读取原始文件数据，标签--------")
signal_data, signal_labels = readFile(filepath)
# 将数据和标签的形状进行调整
print("-----将数据和标签的形状进行调整------")
signal_re, labels_re = data_reshape(signal_data, signal_labels)
re_data = pre_data_reshape(signal_re)
# 数据进行Z-score
print("----------数据进行Z-score----------")
z_score_data = more_norm_dataset(re_data)
# 数据进行1D->2D的转化
print("--------数据进行1D->2D的转化--------")
data_1Dto2D = more_dataset_1Dto2D(z_score_data)
print(data_1Dto2D.shape)
dict_data = {"data": data_1Dto2D, "labels": labels_re}
with open('CNN_train.pkl', 'wb') as f:
pickle.dump(dict_data, f, pickle.HIGHEST_PROTOCOL)
# 读取.pkl文件
# with open('CNN_train.pkl', 'rb') as f:
# data = pickle.load(f)
# data_1Dto2D = data['data']
# labels_re = data['labels']
# 开始CNN的训练
print("-----------开始CNN的训练-----------")
backward(data_1Dto2D, labels_re)
def main():
signal_data, signal_labels = readFile(backward.filepath)
signal_re, labels_re = data_reshape_test(signal_data, signal_labels)
print("signal_re.shape:")
print(signal_re.shape)
print("labels_re.shape:")
print(labels_re.shape)
re_data = pre_data_reshape(signal_re)
print("re_data.shape:")
print(re_data.shape)
z_score_data = more_norm_dataset(re_data)
data_1Dto2D = more_dataset_1Dto2D(z_score_data)
print("data_1Dto2D.shape:")
print(data_1Dto2D.shape)
test(data_1Dto2D, labels_re)
def main():
signal_data, signal_labels = readFile(backward.filepath)
signal_re, labels_re = data_reshape_test(signal_data, signal_labels)
print("signal_re.shape:")
print(signal_re.shape)
print("labels_re.shape:")
print(labels_re.shape)
re_data = pre_data_reshape(signal_re)
print("re_data.shape:")
print(re_data.shape)
z_score_data = more_norm_dataset(re_data)
data_1Dto2D = more_dataset_1Dto2D(z_score_data)
print("data_1Dto2D.shape:")
print(data_1Dto2D.shape)
dict_data = {"data": data_1Dto2D, "labels": labels_re}
with open('CNN_test.pkl', 'wb') as f:
pickle.dump(dict_data, f, pickle.HIGHEST_PROTOCOL)
print("okkkkkkkkkkkkk")
def main():
# 读取原始文件数据，标签
print("-------读取原始文件数据，标签--------")
signal_data, signal_labels = readFile(filepath)
# 将数据和标签的形状进行调整
print("-----将数据和标签的形状进行调整------")
signal_re, labels_re = data_reshape(signal_data, signal_labels)
re_data = pre_data_reshape(signal_re)
# 数据进行Z-score
print("----------数据进行Z-score----------")
z_score_data = more_norm_dataset(re_data)
# 数据进行1D->2D的转化
print("--------数据进行1D->2D的转化--------")
data_1Dto2D = more_dataset_1Dto2D(z_score_data)
print(data_1Dto2D.shape)
# 开始CNN的训练
print("-----------开始CNN的训练-----------")
backward(data_1Dto2D, labels_re)