<jupyter_start><jupyter_text>**Achieved an accuracy of around ~99.4%**<jupyter_code># Comparing with some data from the training set

predictions = model.predict(X_Test[100:110])
print(np.argmax(predictions,axis=1))

print(Y_Test[100:110])
for i in range(0,10):
  image = np.array(X_Test[i],dtype='float')
  image_reshaped = image.reshape((28,28))
  plt.imshow(image_reshaped,cmap='gray')
  plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15777262180974477, 'fraction_numerical': 0.05336426914153132, 'mean_word_length': 8.0, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '405469', 'license': 'MIT', 'n_tokens_mistral': 183, 'n_tokens_neox': 170, 'n_words': 28, 'path': 'Hand_Written_Digits_CNN_Updated.ipynb', 'repo_name': 'GSNCodes/Handwritten_Digits_CNN', 'size': '431'}