Generating image from MNIST Data
I am quite new to python and pytorch. Please review my code below. I have tried everything I know but I am not able to create a MNIST data set image  out of the matrix below. I expect the image should be 1.
It would be great if someone can help me in it.
import torch
import torch.nn.functional as F
import torch.optim as optim

import torchquantum as tq
import torchquantum.functional as tqf

from torchquantum.datasets import MNIST
from torch.optim.lr_scheduler import CosineAnnealingLR

import random
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
dataset = MNIST(root='../Data_Manu',
                 train_valid_split_ratio=[0.9, 0.1],
            digits_of_interest=[3, 6],
            n_test_samples=75)


data_value =dataset['train'][0]
## Output is below

{'image': tensor([[[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242,  0.1740,  2.5415,  2.7960,
2.7960,  1.4214, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242,  1.1668,  2.5415,  2.7833,  2.7833,
2.7833,  2.2105, -0.1696, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.2842,  0.3140,  2.3887,  2.7960,  2.7833,  2.7069,
2.3124,  1.1668, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4115,  1.5487,  2.7833,  2.7833,  2.7960,  2.2487,  0.7468,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
1.7523,  2.7833,  2.7833,  2.7833,  2.1978, -0.1696, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.2842,  0.5049,
2.7960,  2.7833,  2.7833,  2.2487, -0.1696, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242,  1.3577,  2.7833,
2.7960,  2.7833,  2.1851, -0.0296, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242,  1.1668,  2.3887,  2.7833,
2.7960,  2.2487, -0.0296, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242,  0.9759,  2.7960,  2.7960,  2.7960,
2.8215,  1.0904, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4115,  1.4723,  2.7833,  2.7833,  2.7833,
0.0213, -0.3606, -0.4242,  0.1104, -0.0169, -0.2969, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.1569,  2.7833,  2.7833,  2.7833,  1.4596,
-0.4242, -0.0169,  1.3577,  2.3887,  2.2742,  1.4723, -0.0169,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.1569,  2.1978,  2.7833,  2.7833,  2.7833,  0.9504,
1.4214,  2.5924,  2.7833,  2.7833,  2.7960,  2.7833,  2.3124,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242,  0.0467,  2.7960,  2.7960,  2.7960,  2.7960,  2.7960,
2.8215,  2.7960,  2.7960,  2.7960,  2.8215,  2.7960,  2.5287,
0.1740, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242,  0.0467,  2.7833,  2.7833,  2.7833,  2.7833,  2.7833,
2.7960,  2.7833,  2.7833,  2.7833,  2.7960,  2.7833,  2.6433,
0.5559, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242,  1.3577,  2.7833,  2.7833,  2.7833,  2.7833,  2.7833,
2.7960,  2.7833,  2.7833,  2.7833,  2.7960,  2.7833,  2.3124,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242,  1.8796,  2.7833,  2.7833,  2.7833,  2.7833,  2.7833,
2.7960,  2.7833,  2.7833,  2.7833,  2.7960,  2.2487,  0.7468,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242,  1.8923,  2.7960,  2.7960,  2.7960,  2.7960,  2.7960,
2.8215,  2.7960,  2.7960,  2.7960,  1.4214, -0.1696, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242,  1.3450,  2.7833,  2.7833,  2.7833,  2.7833,  2.7833,
2.7960,  2.7833,  2.1214,  0.8104, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242,  0.0467,  2.7833,  2.7833,  2.7833,  2.4524,  2.3124,
0.4922,  0.4795, -0.1696, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.2206,  1.9942,  2.7833,  2.2487, -0.0296, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242],
[-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242,
-0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242, -0.4242]]]),
'digit': 1}
plt.imshow(data_value.numpy()[0], cmap='gray')

AttributeError                            Traceback (most recent call last)
&lt;ipython-input-7-498b4257facf&gt; in &lt;module&gt;
----&gt; 1 plt.imshow(data_value.numpy()[0], cmap='gray')

AttributeError: 'dict' object has no attribute 'numpy'

Thank you for the great help.

Try change this plt.imshow(data_value.numpy()[0], cmap='gray') to plt.imshow(data_value['image'].numpy()[0], cmap='gray').
Your output is not a torch.Tensor is dict than contains two labels &quot;image&quot; (Tensor) and &quot;digit&quot; (int).
Is for that reason you have this error AttributeError: 'dict' object has no attribute 'numpy'
