Python – How do I get an index of prediction error data in Keras?

I’m doing sentiment analysis and using keras to predict positive/negative movie reviews.
What I want to know is the raw data, which my model incorrectly predicted.
I can only get accuracy and loss from my model, but I want to get a subset of text that my model predicts incorrectly. How?

import pandas as pd
from keras.preprocessing.text import Tokenizer
from keras.layers import Dense
import keras
import numpy as np
import  gc
from sklearn.model_selection import train_test_split
dataset=pd.read_csv('balanced_dataset.csv')
tk=Tokenizer(num_words=2000)
tk.fit_on_texts(dataset.review)
x=tk.texts_to_matrix(dataset.review)
y=dataset.label

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=40)

model=keras.models.Sequential()
model.add(Dense(8,input_dim=2000))
model.add(Dense(1,activation='sigmoid'))

model.compile(loss='binary_crossentropy',optimizer='rmsprop',metrics=['acc'])

del tk
gc.collect()

result=model.fit(x_train,y_train,batch_size=128,epochs=20,validation_split=0.1)

Simply put, use: model.predict().

pred = model.predict(x_test)
indices = [i for i,v in enumerate(pred) if pred[i]!=y_test[i]]
subset_of_wrongly_predicted = [x_test[i] for i in indices ]