import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from scipy.spatial.distance import cdist
# from tf.keras.models import Sequential # This does not work!
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, GRU, Embedding
from tensorflow.python.keras.optimizers import Adam
from tensorflow.python.keras.preprocessing.text import Tokenizer
from tensorflow.python.keras.preprocessing.sequence import pad_sequences
import imdb
imdb.maybe_download_and_extract()
input_text_train, target_train = imdb.load_data(train=True)
input_text_test, target_test = imdb.load_data(train=False)
print("Size of the trainig set: ", len(input_text_train))
print("Size of the testing set: ", len(input_text_test))
text_data = input_text_train + input_text_test
print('Sample example from the training set...')
print(input_text_train[1])
print('Sample example actual sentiment...')
print(target_train[1])
#Include only the popular ones
from keras.layers import Dense, GRU, Embedding
from keras.optimizers import Adam
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
# In[3]:
# Some of the code and explaination here is taken from https://github.com/Hvass-Labs/ :)
# In[4]:
import imdb # this is helper package to download and load the imdb dataset by https://github.com/Hvass-Labs/
# In[5]:
imdb.maybe_download_and_extract() #Downloading and Extracting the dataset
# In[6]:
x_train_text, y_train = imdb.load_data(train=True) #loading train data
x_test_text, y_test = imdb.load_data(train=False) # loading test data
# In[7]:
print("Train-set size: ", len(x_train_text))
print("Test-set size: ", len(x_test_text))
# In[8]:
data_text = x_train_text + x_test_text