A memo when executing the deep learning sample code created from scratch with Google Colaboratory

Introduction This is the content of ** "Deep Learning from scratch-The theory and implementation of deep learning learned with Python" published by O'REILY ** when executing the sample code in the Google Colaboratory environment.

--Get sample data of books from GitHub --About how to mount data (Introduction of connection method because it cannot be used just by uploading data to My Drive) --About image display (Since the image was not displayed in Google Colaboratory even if the sample code was written, an alternative method was introduced)

Chapter 3 Neural Network 3.6.1 ** MNIST dataset ** 3.7.1 ** Neural network inference processing **

It will be a memo when you execute the above contents. (P72〜P75)

Data acquisition ** Get data from GitHub. **(download) https://github.com/oreilly-japan/deep-learning-from-scratch Folder name = ** deep-learning-from-scratch-master **

About uploading and mounting data 1. Upload the folder in My Drive ** Google Drive **. 2. Create a new Google Colaboratory. 3. Mount the drive. (Make the data available in the notebook)

--Click the icon to start the process.
mount_1.png --Connect to Google Drive
connection.png --When drive is displayed, connection is complete
mount_2.png

Move directory You need to change the current directory before entering the first code. cd.png

Import of load_mnist function load_minst関数インポート.png

Run mnist_show.py ――It is possible to omit the code up to the 6th line. (Already executed when importing load_mnist) --Number of the first training image = ** 5 ** --Number of one-dimensional arrays = ** 784, ** --Retransformed from the one-dimensional array to the original shape = ** 28, 28 ** --The training image that should have been displayed is not displayed. = ** It is stored in img but is not displayed. ** ** mnist_show.png

Image display --I tried another display method to check if it is really ** 5 **. --Import matplotlib and display the image. ――We succeeded in displaying the first training image.
5img.png

Run neuralnet_mnist.py --Correct recognition accuracy = ** 0.9352 (93.52%). ** **

Finally I just ran the sample code in Google Colaboratory, but I spent a lot of time. I posted it for the first time so that people who are worried about similar content can use it as much as possible.

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