ppd = PrepareData()
data = ppd.get_data()
lstm = LSTMClassifier()
X = lstm.get_matrix(data)
Y = pd.get_dummies(data['label']).values
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
test_size=0.33,
random_state=42)
print(X_train.shape, Y_train.shape)
print(X_test.shape, Y_test.shape)
model = lstm.get_model(X.shape[1])
history = lstm.fit_model(model, X_train, Y_train)
validation_size = 1500
X_validate = X_test[-validation_size:]
Y_validate = Y_test[-validation_size:]
X_test = X_test[:-validation_size]
Y_test = Y_test[:-validation_size]
score, acc = model.evaluate(X_test, Y_test, verbose=2, batch_size=batch_size)
print("score: %.2f" % (score))
print("acc: %.2f" % (acc))
pos_cnt, neg_cnt, pos_correct, neg_correct = 0, 0, 0, 0
for x in range(len(X_validate)):
result = model.predict(X_validate[x].reshape(1, X_test.shape[1]),
batch_size=1,
