print("采油数据训练集输入的形状:" + str(train_oil_data.size()))
时间: 2024-06-02 07:11:30 浏览: 25
这行代码是在Python中使用PyTorch库打印出采油数据训练集输入的形状。假设train_oil_data是一个PyTorch的Tensor张量,那么该行代码会输出一个字符串,其中包含train_oil_data的形状信息。具体来说,size()方法返回张量的尺寸信息,使用str()函数将其转换为字符串,然后与其他字符串拼接在一起,最终输出一个完整的字符串。
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detail_num = 0 for id_0 in range(0, len(list_0)): path = os.path.join(rootdir0, list_0[id_0]) if os.path.isfile(path): print(path) train_data = np.load(path) train_data = add_noise(train_data) train_len = int((len(train_data) - 5120) / 5120 + 1) for sub_id in range(0, train_len): sub_train_data = train_data[sub_id * 5120:sub_id * 5120 + 5120] str_num_train = str(num_train) np.save("../GB_data/" + Fault + "/noise_data/" + snr_str + "/train_data/" + str_num_train + "_train.npy", sub_train_data) np.save("../GB_data/" + Fault + "/noise_data/" + snr_str + "/train_lab/" + str_num_train + "_lab.npy", lab0) num_train += 1 for id_0 in range(0, len(T_list_0)): path = os.path.join(T_rootdir0, T_list_0[id_0]) if os.path.isfile(path): print(path) test_data = np.load(path) train_data = add_noise(train_data) test_len = int((len(test_data) - 5120) / 5120 + 1) for sub_id in range(0, test_len): sub_test_data = test_data[sub_id * 5120:sub_id * 5120 + 5120] str_num_test = str(num_test) np.save("../GB_data/" + Fault + "/noise_data/" + snr_str + "/test_data/" + str_num_test + "_test.npy", sub_test_data) np.save("../GB_data/" + Fault + "/noise_data/" + snr_str + "/test_lab/" + str_num_test + "_lab.npy", lab0) str_detail_num = str(detail_num) np.save("../GB_data/" + Fault + "/noise_data/" + snr_str + "/test_detail/0/" + str_detail_num + "_test.npy", sub_test_data) np.save( "../GB_data/" + Fault + "/noise_data/" + snr_str + "/test_lab_detail/0/" + str_detail_num + "_lab.npy", lab0) detail_num += 1 num_test += 1
这是一段Python代码,它从一个文件夹中读取数据,对每个数据进行噪声添加,并将处理后的数据和标签保存到不同的文件夹中。其中,训练数据被保存到"../GB_data/Fault/noise_data/snr_str/train_data/"文件夹中,测试数据被保存到"../GB_data/Fault/noise_data/snr_str/test_data/"文件夹中,详细的测试数据被保存到"../GB_data/Fault/noise_data/snr_str/test_detail/0/"文件夹中。这段代码还使用了numpy库来处理数据。
加载训练MNIST数据集 #加载数据集(输入代码): #查看训练数据集结构(输入代码): 输入结果: x_train.shape: y_train.shape: x_test.shape: y_test.shape:
以下是加载MNIST数据集和查看数据集结构的代码:
```python
import tensorflow as tf
# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# 输出训练集和测试集的形状
print("x_train.shape:", x_train.shape)
print("y_train.shape:", y_train.shape)
print("x_test.shape:", x_test.shape)
print("y_test.shape:", y_test.shape)
```
运行结果:
```
x_train.shape: (60000, 28, 28)
y_train.shape: (60000,)
x_test.shape: (10000, 28, 28)
y_test.shape: (10000,)
```
说明训练集包含60000个28x28的图像,测试集包含10000个28x28的图像。标签是长度为60000和10000的一维数组。