I'm trying to run the followin code on google colab:


```
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from keras import models
from keras import layers
from keras.datasets import boston_housing
from sklearn.model_selection import KFold
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import GridSearchCV
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.metrics import mean_squared_error

sns.set()

(train_data,train_targets),(test_data,test_targets)=boston_housing.load_data()

mean=np.mean(train_data)
std=np.std(train_data)

train_data_norm=(train_data-mean)/std
test_data_norm=(test_data-mean)/std

def build_model():
    model=models.Sequential()
    model.add(layers.Dense(64,activation="relu",
                          input_shape=(train_data_norm.shape[1],)))
    model.add(layers.Dense(64,activation="relu"))
    model.add(layers.Dense(1))
    model.compile(optimizer='rmsprop',loss="mse",metrics=["mae"])
    return model 


model=KerasRegressor(build_fn=build_model,epochs=30,verbose=0)
param_grid = {"epochs":range(1,11)}

kf1=KFold(n_splits=5, random_state=None, shuffle=False) #n_splits = number of folds
kf2=KFold(n_splits=5, random_state=1, shuffle=True)
ss = ShuffleSplit(n_splits=5,test_size=0.20,random_state=1)

grid_model_KFFalse=GridSearchCV(model,param_grid,cv=kf1,n_jobs=-1,scoring='neg_mean_squared_error')
grid_model_KFTrue=GridSearchCV(model,param_grid,cv=kf2,n_jobs=-1,scoring='neg_mean_squared_error')
grid_model_SS=GridSearchCV(model,param_grid,cv=ss,n_jobs=-1,scoring='neg_mean_squared_error')

listKFFalse=[]
listKFTrue=[]
listSS=[]

for i in range(1,21):
  grid_model_KFFalse.fit(train_data, train_targets)
  grid_model_KFTrue.fit(train_data, train_targets)
  grid_model_SS.fit(train_data, train_targets)
  mseKFFalse=mean_squared_error(grid_model_KFFalse.predict(test_data),test_targets)
  mseKFTrue=mean_squared_error(grid_model_KFTrue.predict(test_data),test_targets)
  mseSS=mean_squared_error(grid_model_SS.predict(test_data),test_targets)
  listKFFalse=np.append(listKFFalse,[mseKFFalse])
  listKFTrue=np.append(listKFTrue,[mseKFTrue])
  listSS=np.append(listSS,[mseSS])
```
You can check it [here](https://colab.research.google.com/drive/1P-HNBtMNDqUBsoUaBL9BlPgrQ5al3QrO?usp=sharing).


However, it's taking a very long time... I wonder why it is. Isn't google colab supposed to use GPU(or TPU) when learning neural networks with keras?
I've already tried changing the runtime type, but it's still very slow.