<jupyter_start><jupyter_text>Etiquetas0 = Accuracy1 = Validation Accuracy2 = Loss3 = Validation Loss<jupyter_code>print(np.arange(1, 11))
print(logs)
import csv
myFile = open('../Logs/ldmark2.csv', 'w')
with myFile:
    writer = csv.writer(myFile)
    writer.writerows(logs)
     
print("Writing complete")
plt.plot(np.arange(1, 10), logs[0], color='g')
plt.plot(np.arange(1, 10), logs[1], color='orange')
plt.xlabel('Epocas')
plt.ylabel('Exactitud')
plt.title('Exactitud')
red_patch = mpatches.Patch(color='g', label='Exactitud de entrenamiento')
orange_patch = mpatches.Patch(color='orange', label='Exactitud de validacion')
plt.legend(handles=[red_patch, orange_patch])
plt.show()
plt.plot(np.arange(1, 31), logs[2], color='g')
plt.plot(np.arange(1, 31), logs[3], color='orange')
plt.xlabel('Epocas')
plt.ylabel('Perdida')
plt.title('Perdida')
red_patch = mpatches.Patch(color='g', label='Perdida de entrenamiento')
orange_patch = mpatches.Patch(color='orange', label='Perdida de validacion')
plt.legend(handles=[red_patch, orange_patch])
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18207024029574861, 'fraction_numerical': 0.022181146025878003, 'mean_word_length': 7.804878048780488, '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': '258421', 'license': 'MIT', 'n_tokens_mistral': 455, 'n_tokens_neox': 412, 'n_words': 69, 'path': 'Notebooks/Make and Model Recognition - Showing graphs.ipynb', 'repo_name': 'f-maldo/title-project', 'size': '1082'}