RNN.py

import pandas as pd
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D, Bidirectional, SimpleRNN
from sklearn.model_selection import train_test_split
from keras.callbacks import EarlyStopping
from keras.layers import Dropout

df = pd.read_json("data.json")

# The maximum number of words to be used. (most frequent)
MAX_NB_WORDS = 50000
# Max number of words in each text.
MAX_SEQUENCE_LENGTH = 500
# This is fixed.
EMBEDDING_DIM = 100

#use Tokenizer to convert text to numbers
tokenizer = Tokenizer(num_words=MAX_NB_WORDS, filters='!"#$%&()*+,-./:;<=>?@[\]^_`{|}~', lower=True, char_level=False, split=' ')
tokenizer.fit_on_texts(df['text'].values)
word_index = tokenizer.word_index
print('Found %s unique tokens.' % len(word_index))

X = tokenizer.texts_to_sequences(df['text'].values)
X = pad_sequences(X, maxlen=MAX_SEQUENCE_LENGTH)
print('Shape of data tensor:', X.shape)

Y = pd.get_dummies(df['label']).values
print('Shape of label tensor:', Y.shape)

#split data into test and train
X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.10, random_state = 42)
print(X_train.shape,Y_train.shape)
print(X_test.shape,Y_test.shape)

#make model
model = Sequential()
model.add(Embedding(MAX_NB_WORDS, EMBEDDING_DIM, input_length=X.shape[1]))
model.add(SpatialDropout1D(0.2))
model.add(SimpleRNN(50, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adagrad', metrics=['accuracy'])

#hyperparameters
epochs = 3
batch_size = 16

#train model
history = model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size,validation_split=0.1,callbacks=[EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)])

#test model
accr = model.evaluate(X_test,Y_test)
print('Test set\n  Loss: {:0.3f}\n  Accuracy: {:0.3f}'.format(accr[0],accr[1]))