Table of Contents
Problem Description
wrong reason
solution
1. Check file and directory permissions
2. Change the user or group to which the file belongs
3. Run the program with administrator privileges
4. Change file path
5. Use the os module to set file permissions
in conclusion
Data set composition
Dataset content
Dataset purpose
Data set acquisition
Dataset visualization
Solving PermissionError: [Errno 13] Permission denied: ‘./data\mnist\train-images-idx3-ubyte’
Problem Description
When using Python for deep learning or machine learning tasks, some open source data sets, such as MNIST, are often used. However, when reading these data sets, you sometimes encounter error messages such as:
plaintextCopy codePermissionError: [Errno 13] Permission denied: './data\mnist\train-images-idx3-ubyte'
This error usually means that we do not have permission to read or write the specified file. This article explains the causes of this error and provides some solutions.
Error reason
This error is usually caused by incorrect permissions on the file or directory. In many operating systems, files and directories have permission attributes that control access to them. A PermissionError occurs if the current user does not have sufficient permissions to read or write the file.
Solution
Here are several common solutions, you can choose the solution that suits you based on your specific situation:
1. Check file and directory permissions
First, you need to check the permission settings of the relevant files and directories. Make sure you have read and write permissions to the file. You can view and modify file permissions using the file management tools or commands provided by your operating system.
2. Change the user or group to which the file belongs
If you cannot access a file, it may be because the user or group to which the file belongs does not match. You can change the owner of a file using the chown command (for Linux/Unix) or the icacls command (for Windows).
3. Run the program with administrator rights
If you are using a Windows system, you can try running your program with administrator rights. Right-click the program icon and select “Run as administrator.”
4. Change file path
Sometimes you can try copying the file to a different location, such as changing the file storage path or moving the file to a different directory. You can then try reading or writing the file to see if the permission errors still occur.
5. Use the ??os??module to set file permissions
In Python, you can use the ??os?? module to set file permissions. Use the ??os.chmod()?? function to change file permissions. Before changing permissions, make sure you have sufficient permissions to perform this operation.
pythonCopy codeimport os
#Set the read and write permissions of the file
os.chmod('./data/mnist/train-images-idx3-ubyte', 0o777)
Conclusion
When you encounter a ??PermissionError: [Errno 13] Permission denied?? error when using Python for deep learning or machine learning tasks, it means that you do not have permission to read or write the specified document. With solutions such as checking the permission settings for files and directories, changing the user or group the file belongs to, and running the program with administrator privileges, you can resolve the issue and continue with your task.
MNIST is a classic handwritten digit recognition data set that is widely used for training and testing of machine learning and deep learning. MNIST in Python is a dataset consisting of four binary files containing a large number of images of handwritten digits and corresponding labels. The MNIST dataset in Python will be introduced in detail below.
Dataset composition
The MNIST dataset consists of the following four files:
- train-images-idx3-ubyte: File containing 60,000 training images.
- train-labels-idx1-ubyte: File containing labels corresponding to 60,000 training images.
- t10k-images-idx3-ubyte: File containing 10,000 test images.
- t10k-labels-idx1-ubyte: File containing labels corresponding to 10,000 test images.
dataset content
Each image is a 28×28 pixel grayscale image with pixel values ranging from 0 to 255. The label is an integer between 0 and 9, representing the handwritten digit in the corresponding image.
Dataset Purpose
The MNIST dataset is widely used to train and test machine learning and deep learning models, especially for image recognition tasks. Because the MNIST dataset is small and can be trained and tested quickly, it is often used as a starting point for learning and debugging algorithms. Many classic deep learning models, such as convolutional neural networks (CNN), are verified and performance evaluated on the MNIST dataset.
Dataset acquisition
In Python, you can use the third-party library tensorflow or torchvision to obtain and load the MNIST dataset. These libraries provide API functions to download and parse MNIST dataset files and convert them into a form that can be used in models. For the tensorflow library, you can use the following code to get the MNIST dataset:
pythonCopy codefrom tensorflow.keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data()
For the torchvision library, you can use the following code to get the MNIST dataset:
pythonCopy codeimport torchvision.datasets as datasets train_dataset = datasets.MNIST(root='./data', train=True, transform=None, target_transform=None, download=True) test_dataset = datasets.MNIST(root='./data', train=False, transform=None, target_transform=None, download=True)
Dataset Visualization
The MNIST dataset can be visualized using libraries such as matplotlib to better understand and observe the images and labels in the dataset. By drawing an image and printing a label, you can view the style of handwritten numbers and corresponding labels.
The MNIST data set in Python is a classic handwritten digit recognition data set, containing a large number of handwritten digit images and corresponding labels. Due to the small size of the data set, it is widely used for training and testing of machine learning and deep learning. Through the third-party library tensorflow or torchvision, you can easily obtain and load the MNIST data set and conduct further model development and performance evaluation.
Practical application scenarios:
- Digit recognition: The most common application scenario of the MNIST data set is handwritten digit recognition. By training a model, images of handwritten digits input by users can be converted into corresponding digital labels, thereby achieving automated digit recognition.
- Text recognition: The MNIST dataset can also be used for text recognition tasks, such as recognizing printed letters or characters. By training a model, the input text image can be converted into the corresponding letter or character label.
- Image classification: In addition to number and text recognition, the MNIST dataset can also be applied to other image classification tasks. For example, identifying lesions in medical images, or identifying vehicle types in vehicle images, etc. Example code: The following is an example code for using the tensorflow library to train a simple convolutional neural network (CNN) model for digit recognition on the MNIST dataset:
pythonCopy codeimport tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
#Load the MNIST data set
(x_train, y_train), (x_test, y_test) = mnist.load_data()
#Data preprocessing
x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.0
x_test = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.0
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)
# Build model
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))
# Compile model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
#Train model
model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))
# Evaluate the model
loss, accuracy = model.evaluate(x_test, y_test)
print("Test Loss:", loss)
print("Test Accuracy:", accuracy)
This sample code loads the MNIST data set, preprocesses the image data, builds a simple CNN model, and compiles, trains, and evaluates the model. Finally, the loss and accuracy of the model on the test set are output.
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