<jupyter_start><jupyter_text>Dense Sentiment Classifier classifying IMDB reviews by sentiment.  Load dependencies<jupyter_code>import keras
from keras.datasets import imdb
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, Flatten, Dropout
from keras.layers import Embedding
from keras.callbacks import ModelCheckpoint
import os
from sklearn.metrics import roc_auc_score
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline<jupyter_output><empty_output><jupyter_text>Set Hyperparameter<jupyter_code>output_dir = './model_output/dense'

epochs = 4
batch_size = 128

n_dim = 64
n_unique_words = 5000
n_words_to_skip = 50
max_review_length = 100
pad_type = trunc_type = 'pre'

n_dense = 64
dropout = 0.5<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.06097560975609756, 'fraction_numerical': 0.023170731707317073, 'mean_word_length': 6.970873786407767, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '540668', 'license': 'Apache-2.0', 'n_tokens_mistral': 297, 'n_tokens_neox': 274, 'n_words': 74, 'path': '.ipynb_checkpoints/dense_sentiment_classifier-checkpoint.ipynb', 'repo_name': 'siddhx/Natural-Language-Processing', 'size': '820'}