model.py

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats

from keras.datasets import imdb
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers.embeddings import Embedding
from keras.layers import SimpleRNN, Dense, Activation

rnn = Sequential()
rnn.add(Embedding(num_words, 32, input_length = len(X_train[0])))
rnn.add(SimpleRNN(16, input_shape = (num_words,maxlen), return_sequences= False, activation= "relu"))
rnn.add(Dense(1))
rnn.add(Activation("sigmoid"))

print(rnn.summary())
rnn.compile(loss = "binary_crossentropy", optimizer="rmsprop",metrics= ["acc"])

history = rnn.fit(X_train, Y_train, validation_data= (X_test, Y_test), epochs=5, batch_size= 128, verbose=1)
rnn.save('model.h5')
score = rnn.evaluate(X_test, Y_test)
print("Accuracy: %",score[1]*100)

plt.figure()
plt.plot(history.history["acc"], label = "Train")
plt.plot(history.history["val_acc"], label = "Test")
plt.title("Acc")
plt.ylabel("Acc")
plt.xlabel("Epochs")
plt.legend()
plt.show()

plt.figure()
plt.plot(history.history["loss"], label = "Train")
plt.plot(history.history["val_loss"], label = "Test")
plt.title("Acc")
plt.ylabel("Acc")
plt.xlabel("Epochs")
plt.legend()
plt.show()