<jupyter_start><jupyter_text>Let's see some examples from the test set<jupyter_code>i = 0
s = 'Classified as: '+ str(pred[i])
plt.title(s)
plt.axis('off')
plt.imshow(X_test[i].reshape(28,28))
plt.show()
 i = 1
s = 'Classified as: '+ str(pred[i])
plt.title(s)
plt.axis('off')
plt.imshow(X_test[i].reshape(28,28))
plt.show()
 i = 2
s = 'Classified as: '+ str(pred[i])
plt.title(s)
plt.axis('off')
plt.imshow(X_test[i].reshape(28,28))
plt.show()
 i = 3
s = 'Classified as: '+ str(pred[i])
plt.title(s)
plt.axis('off')
plt.imshow(X_test[i].reshape(28,28))
plt.show()
 i = 4
s = 'Classified as: '+ str(pred[i])
plt.title(s)
plt.axis('off')
plt.imshow(X_test[i].reshape(28,28))
plt.show()
 i = 5
s = 'Classified as: '+ str(pred[i])
plt.title(s)
plt.axis('off')
plt.imshow(X_test[i].reshape(28,28))
plt.show()
 i = 6
s = 'Classified as: '+ str(pred[i])
plt.title(s)
plt.axis('off')
plt.imshow(X_test[i].reshape(28,28))
plt.show()
 i = 8
s = 'Classified as: '+ str(pred[i])
plt.title(s)
plt.axis('off')
plt.imshow(X_test[i].reshape(28,28))
plt.show()<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '186967', 'license': 'MIT', 'n_tokens_mistral': 565, 'n_tokens_neox': 499, 'n_words': 87, 'path': 'NeuralNetwork_backpropagation_handwritten_data.ipynb', 'repo_name': 'rahul23aug/PY-machine_learning', 'size': '1072'}