Skip to main content

Python Matplotlib plt.imshow crashes kernel in an unknown way when plotting images

When I run the following code (which runs fine on Google Colab) my kernel crashes

from torchvision import datasets
from torchvision.transforms import ToTensor
import torch.nn as nn
import torch.nn as cnn

train_dataset = datasets.MNIST(
    root = 'datasets',
    train = True,
    transform = ToTensor(), 
    download = True,            
)
test_dataset = datasets.MNIST(
    root = 'datasets', 
    train = False, 
    transform = ToTensor()
)
print(train_dataset)
print(test_dataset)
print(train_dataset.data.size())
print(test_dataset.data.size())

import matplotlib.pyplot as plot
plot.figure(1)
plot.imshow(train_dataset.data[0], cmap='gray')
plot.title('%i' % train_dataset.targets[0])
plot.show()

The very undescriptive error message i get is the following: enter image description here

(This is the click here link shown in the screenshot-image above)

However if I run the following code a one or more cells above the previous code works without crashing the kernel. What is really difficult to understand is even with importing matplotlib with different names doesn't effect this issue...

import matplotlib.pyplot as plt
plt.imshow([[1, 0], [0, 1]]) #this fixes the kernel from crashing if placed before previous code for some strange reason

I know some people (as seen here https://github.com/jupyter/notebook/issues/6219) suggest going back a version or two for different packages. This did not work for me. Since this code runs on colab it means there is some issue with my PC.

I have reinstalled Anaconda and all my python stuff and still am unsuccessful with solving this issue.

Currently my package versions are: Torch Version: 2.0.1+cu117 TorchVision Version: 0.15.2+cu117 Matplotlib Version: 3.5.0



source https://stackoverflow.com/questions/77035613/python-matplotlib-plt-imshow-crashes-kernel-in-an-unknown-way-when-plotting-imag

Comments

Popular posts from this blog

How to show number of registered users in Laravel based on usertype?

i'm trying to display data from the database in the admin dashboard i used this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count(); echo $users; ?> and i have successfully get the correct data from the database but what if i want to display a specific data for example in this user table there is "usertype" that specify if the user is normal user or admin i want to user the same code above but to display a specific usertype i tried this: <?php use Illuminate\Support\Facades\DB; $users = DB::table('users')->count()->WHERE usertype =admin; echo $users; ?> but it didn't work, what am i doing wrong? source https://stackoverflow.com/questions/68199726/how-to-show-number-of-registered-users-in-laravel-based-on-usertype

Why is my reports service not connecting?

I am trying to pull some data from a Postgres database using Node.js and node-postures but I can't figure out why my service isn't connecting. my routes/index.js file: const express = require('express'); const router = express.Router(); const ordersCountController = require('../controllers/ordersCountController'); const ordersController = require('../controllers/ordersController'); const weeklyReportsController = require('../controllers/weeklyReportsController'); router.get('/orders_count', ordersCountController); router.get('/orders', ordersController); router.get('/weekly_reports', weeklyReportsController); module.exports = router; My controllers/weeklyReportsController.js file: const weeklyReportsService = require('../services/weeklyReportsService'); const weeklyReportsController = async (req, res) => { try { const data = await weeklyReportsService; res.json({data}) console...

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...