application_vnd.jupyter.stderr_output_1.png
from tensorflow.keras.preprocessing.sequence import pad_sequences

from tensorflow.keras.preprocessing.text import Tokenizer

import numpy as np

import numpy as np # linear algebra

import numpy as np
train_data, test_data = (imdb['train'], imdb['test'])
training_sentences = []
training_labels = []
testing_sentences = []
testing_labels = []
for s, l in train_data:
    training_sentences.append(str(s.numpy()))
    training_labels.append(l.numpy())
for s, l in test_data:
    testing_sentences.append(str(s.numpy()))
    testing_labels.append(l.numpy())
training_labels_final = np.array(training_labels)
testing_labels_final = np.array(testing_labels)

vocab_size = 10000
embedding_dim = 16
max_length = 120
trunc_type = 'post'
oov_tok = '<OOV>'
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_tok)
tokenizer.fit_on_texts(training_sentences)
word_index = tokenizer.word_index
sequences = tokenizer.texts_to_sequences(training_sentences)
padded = pad_sequences(sequences, maxlen=max_length, truncating=trunc_type)
testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
testing_padded = pad_sequences(testing_sequences, maxlen=max_length)

model = tf.keras.Sequential([tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length), tf.keras.layers.Flatten(), tf.keras.layers.Dense(6, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid')])
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()