imdb.py

from __future__ import absolute_import, division, print_function

import tensorflow as tf
from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt
print(tf.__version__)
imdb = keras.datasets.imdb

np_load_old = np.load
np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
np.load = np_load_old

# A dictionary mapping words to an integer index
word_index = imdb.get_word_index()
# The first indices are reserved
word_index = {k:(v+3) for k,v in word_index.items()} 
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2  # unknown
word_index["<UNUSED>"] = 3
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])

def decode_review(text):
    return ' '.join([reverse_word_index.get(i, '?') for i in text])

#print(decode_review(train_data[0]))

train_data = keras.preprocessing.sequence.pad_sequences(train_data,
                                                        value=word_index["<PAD>"],
                                                        padding='post',
                                                        maxlen=256)
test_data = keras.preprocessing.sequence.pad_sequences(test_data,
                                                       value=word_index["<PAD>"],
                                                       padding='post',
                                                       maxlen=256)

vocab_size = 10000

model = keras.Sequential()
model.add(keras.layers.Embedding(vocab_size, 16))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(16, activation=tf.nn.relu))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))

model.summary()


model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])
x_val = train_data[:10000]
partial_x_train = train_data[10000:]

y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]
'''
model.fit(partial_x_train,
                    partial_y_train,
                    epochs=40,
                    batch_size=512,
                    validation_data=(x_val, y_val),
                    verbose=1)
'''
model.fit(train_data,train_labels,epochs=40)
result = model.evaluate(test_data,test_labels)
print('results are ',result)
'''
predictions = model.predict(test_data)
print(test_labels[0])
for i in range(len(predictions)):
    print(100*predictions[i])
'''