GenerateModel.py

import tensorflow as tf
from tensorflow import keras
import numpy as np

data = keras.datasets.imdb

# num_words is set to the number of words the neural network will try to learn.
(train_data, train_labels), (test_data, test_labels) = data.load_data(num_words=100000)

#print(train_data[0])

word_index = data.get_word_index()
word_index = {k:(v+3) for k, v in word_index.items()}
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2
word_index["<UNUSED>"] = 3

reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])

# maxlen sets the maximum length read for training.
train_data = keras.preprocessing.sequence.pad_sequences(train_data, value=word_index["<PAD>"], padding="post", maxlen=250)
test_data = keras.preprocessing.sequence.pad_sequences(test_data, value=word_index["<PAD>"], padding="post", maxlen=250)

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

model = keras.Sequential()
model.add(keras.layers.Embedding(100000, 16))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(16, activation="relu"))
model.add(keras.layers.Dense(1, activation="sigmoid"))

model.summary()

model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])

x_val = train_data[:10000] 
x_train = train_data[10000:]

y_val = train_labels[:10000] 
y_train = train_labels[10000:]

fitModel = model.fit(x_train, y_train, epochs=40, batch_size=512, validation_data=(x_val, y_val), verbose=1)

results = model.evaluate(test_data, test_labels)

print(results)

test_review = test_data[5]
predict = model.predict([test_review])
print("Review: ")
print(decode_review(test_review))
print("Prediction: " + str(predict[5]))
print("Actual: " + str(test_labels[5]))
print(results)

model.save("model.h5")