<jupyter_start><jupyter_text>Imports necessary files from other .py notebooks<jupyter_code>from py_files import preprocessing as pp
from py_files import Augmentation as ag
from py_files import pic_class as pc
from py_files import TrainTestSplit as ts
import tensorflow as tf
from tensorflow import keras
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib agg
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator

import os<jupyter_output>Using TensorFlow backend.<jupyter_text>Runs a preprocessing step in order to graph, then save the files as images.<jupyter_code>pp.preprocessing('Raman_Data')<jupyter_output>fluorescent
nylon
polyamide
polyethylene
unknown<jupyter_text>Augments the preprocessed images so the tensorflow program is able to work off of more datapoints.<jupyter_code>ag.full_augmentation('Pre_Augmented_Raman_Pictures')
source = os.getcwd()
source1 = source + '/Post_Augmented_Raman_Pictures'
ts.train_test_split(source1, 0.8)
source2 = source + '/validationdata_Pictures'
pc.picture_classification(source2)
source3 = source + '/trainingdata_Pictures'
pc.picture_classification(source3)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.06102212051868802, 'fraction_numerical': 0.007627765064836003, 'mean_word_length': 7.805369127516778, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 11, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '100292', 'license': 'MIT', 'n_tokens_mistral': 426, 'n_tokens_neox': 406, 'n_words': 119, 'path': 'model_train_and_validate/Training and Validation Files Generation.ipynb', 'repo_name': 'nsuzuki97/Spicy-Raman', 'size': '1311'}