Forums - Is there a way to reverse the MNIST?

import tensorflow as tf
import numpy as np
import cv2
import matplotlib as plt
import os
from PIL import Image 

X = np.array([[0.01],[0.02],[0.03],[0.04],[0.05],[0.06],[0.07],[0.08],[0.9],[0.10],[0.11],[0.12],[0.13],[0.14],[0.23],[0.57],[0.64],[0.01]])
#X = np.reshape(X, (-1, 1+18 - 1))
print(np.shape(X))
Y = np.array([[cv2.imread("C:/Users/17324/Downloads/aiFlow/one.png")[:,:,0]], [cv2.imread("C:/Users/17324/Downloads/aiFlow/Two.png")[:,:,0]],
[cv2.imread("C:/Users/17324/Downloads/aiFlow/Three.png")[:,:,0]],[cv2.imread("C:/Users/17324/Downloads/aiFlow/Four.png")[:,:,0]],
[cv2.imread("C:/Users/17324/Downloads/aiFlow/Five.png")[:,:,0]],[cv2.imread("C:/Users/17324/Downloads/aiFlow/Six.png")[:,:,0]],
[cv2.imread("C:/Users/17324/Downloads/aiFlow/Seven.png")[:,:,0]],[cv2.imread("C:/Users/17324/Downloads/aiFlow/Eight.png")[:,:,0]],
[cv2.imread("C:/Users/17324/Downloads/aiFlow/Nine.png")[:,:,0]],[cv2.imread("C:/Users/17324/Downloads/aiFlow/Ten.png")[:,:,0]],
[cv2.imread("C:/Users/17324/Downloads/aiFlow/Eleven.png")[:,:,0]],[cv2.imread("C:/Users/17324/Downloads/aiFlow/Twelve.png")[:,:,0]],
[cv2.imread("C:/Users/17324/Downloads/aiFlow/Thirteen.png")[:,:,0]],[cv2.imread("C:/Users/17324/Downloads/aiFlow/Fourteen.png")[:,:,0]],
[cv2.imread("C:/Users/17324/Downloads/aiFlow/23.png")[:,:,0]],[cv2.imread("C:/Users/17324/Downloads/aiFlow/57.png")[:,:,0]],
[cv2.imread("C:/Users/17324/Downloads/aiFlow/64.png")[:,:,0]],
[cv2.imread("C:/Users/17324/Downloads/aiFlow/one.png")[:,:,0]]])
Y = np.reshape(Y, (18, 28, 28))
Y = np.reshape(Y, (18, 784))
print(Y)
print(np.shape(Y))
#Y = np.invert(np.array([Y]))
# = np.reshape(Y, (18, 784))
print(Y[1])
Image.fromarray(np.reshape(Y[1], (28, 28))).show()
model = tf.keras.Sequential()
#model.add(tf.keras.layers.Flatten(units = (28, 28)))
model.add(tf.keras.layers.Dense(units = 1))
model.add(tf.keras.layers.Dense(units = 1280, activation = 'sigmoid'))
model.add(tf.keras.layers.Dense(units = 2560, activation = 'relu'))
model.add(tf.keras.layers.Dense(units = 2560, activation = 'relu'))
model.add(tf.keras.layers.Dense(units = 2560, activation = 'relu'))
model.add(tf.keras.layers.Dense(units = 2560, activation = 'relu'))
model.add(tf.keras.layers.Dense(units = 7840, activation = 'relu'))
model.add(tf.keras.layers.Dense(units = 784, activation = 'relu'))
model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
model.fit(X, Y, epochs = 1000)
img = [[cv2.imread("C:/Users/17324/Downloads/aiFlow/Three.png")[:,:,0]]]  
print(np.shape(img))
img = np.reshape(img, (1, 784))
#img = np.reshape(img, (18, 28, 28))
#img = np.reshape(img, (18, 784))
#im = np.reshape(im, (28, 28))
#img.save('my.png')
#print(model.predict([[img]]))
print(model.predict([[0.09]]))

#img.show()
#model.predict(cv2.imread("C:/Users/17324/Downloads/aiFlow/Eight.png"))

Yes, there is a way to reverse the MNIST dataset. The MNIST dataset consists of handwritten digits, and by using machine learning techniques, it is possible to develop models that can recognize and classify these digits. To reverse the MNIST dataset, you would essentially be trying to generate new handwritten digits based on the existing dataset. This can be done using generative models like Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs). These models learn the underlying patterns and characteristics of the MNIST dataset and can generate new realistic-looking handwritten digits. It's an interesting and challenging task that involves exploring the field of generative modeling and deep learning. Good luck with your exploration! fm whatsapp

Yes, There are a few alternative interpretations for reversing the handwritten digits in the MNIST dataset. If you're referring to flipping the dataset's order, you're moving the instances around so they display in a different order. Programming language or library randomization approaches can be used to accomplish this. https://www.myfedloan.us/

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The handwritten digits dataset is reversed by creating a mirror image or flipped version of the MNIST images. Individual photos may be flipped or reversed, but doing so may alter their meaning and make them more challenging to correctly identify, which may reduce the dataset's value for developing machine learning models.

Yes you can reverse the mist, TO do this you need to perform the digit inversion task.

load the MNIST dataset, invert the colors by subtracting each pixel value from 255, and then display the original and inverted images. Try this it will work as you asked. If you face any issues then free to ask, I will get that.
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Reversing Digit Recognition: Teknopediia

Complex task, as recognition models do not retain the full information of the original image.
Explore generative models like VAEs or GANs to generate images resembling the original digits.
Reversing Data Transformation:

Understand the pre-processing steps applied to the MNIST dataset.
Apply inverse operations, such as denormalization, to obtain the Ngobrol Games
original pixel values for visualization.
I hope this explanation provides clarity on the possible interpretations of "reversing" the MNIST dataset. If you have a specific context or additional details in mind, please feel free to provide them for a more tailored response.