Index of predicted wrong data in Keras how to find it

Question

I am doing sentimental analysis and using keras to predict positive/negative of movie reviews. What I want to know is the raw data, those are wrongly predicted by my model. I only can get the accuracy, loss from my model but I want to get the subset of texts in which my model predicted wrong. How to do it?

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)

Answer 1

Simply, 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 ]

Comments

Don't put the solution if you don't understand the question.

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Can you please tell me what's wrong with the solution?