<jupyter_start><jupyter_text>Import statements<jupyter_code>import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import sklearn
import random
import os,sys

# import keras
import keras
from keras import backend as K
backend_keras = keras.backend.backend()
print("keras is using", backend_keras, "as the backend")
from keras.layers import Input, Flatten, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model, load_model, Sequential

# opt
from keras.optimizers import SGD, Nadam, Adamax, Adam, Adadelta, Adagrad, RMSprop<jupyter_output>keras is using tensorflow as the backend<jupyter_text>Load esc frac data and labelsThese are the ones which have been created with the keras notebook<jupyter_code>f = os.listdir()
f.sort()
for i in f: print(i)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07221542227662178, 'fraction_numerical': 0.0036719706242350062, 'mean_word_length': 6.644859813084112, 'pattern_counts': {'":': 0, '<': 8, '<?xml version=': 0, '>': 8, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '267604', 'license': 'MIT', 'n_tokens_mistral': 276, 'n_tokens_neox': 273, 'n_words': 93, 'path': 'CNN/All/.ipynb_checkpoints/CNN5_classify_esc_frac_with_autoencoder-checkpoint.ipynb', 'repo_name': 'dcxSt/MAIS202---21cm-ionization-maps-of-the-universe', 'size': '817'}